CN110766259A - Evaluation method and device for improving renewable energy consumption capacity of wind power heating - Google Patents
Evaluation method and device for improving renewable energy consumption capacity of wind power heating Download PDFInfo
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Abstract
The application relates to an evaluation method for improving the renewable energy consumption capacity of wind power heating, which comprises the following steps: establishing a system wind curtailment evaluation model by using a random production simulation method; acquiring operation parameters of a system, inputting the operation parameters into a system wind curtailment evaluation model, and outputting an initial wind curtailment index; correcting and considering system operation parameters after wind power heating, inputting the corrected operation parameters into a system wind curtailment evaluation model, and outputting corrected wind curtailment indexes; and obtaining a promotion consumption index according to the initial wind abandoning index and the corrected wind abandoning index. The invention also relates to a device for improving the renewable energy consumption capacity by wind power heating. The method and the device can be suitable for calculating the wind power curtailment quantity and evaluating the wind power heating effect in a medium-long term, and provide a basis for planning and implementing a wind power heating project.
Description
Technical Field
The invention relates to the field of calculation methods of power systems, in particular to an evaluation method and device for improving the consumption capacity of renewable energy sources through wind power heating.
Background
In recent years, the situation of renewable energy consumption in China is still severe, and the problem of wind and energy abandonment cannot be completely solved by the renewable energy enrichment province represented by the 'three north' region in China. In order to improve the digestion capacity and promote effective utilization of surplus clean energy in the load valley period, wind power heating pilot project is carried out in China since 2010, however, the implementation effect is not optimistic as expected, and a set of wind power heating effect evaluation method suitable for the early-stage planning and market mechanism design stage is urgently needed.
At present, most of research on wind power heating in the industry stays at policy and qualitative analysis level, and partial wind abandon effect or energy saving and emission reduction effect of wind power heating is evaluated, but most of the research is based on time sequence data of typical scenes, a method of optimizing and solving by a unit combination model or extrapolating according to a static capacity method is adopted, detailed time sequence data must be obtained during implementation, the requirement on the integrity of the data is extremely high, the time sequence solving method is mainly based on an optimization algorithm and is low in solving speed, and the obtained result can only be used as a reference basis of certain special scenes, so that the method is difficult to effectively adapt to medium-long term planning scenes.
Disclosure of Invention
Therefore, in order to solve the above problems, it is necessary to provide an evaluation method and an evaluation device that can be applied to effectively evaluate wind power heating and renewable energy consumption improvement capability in a medium-long time scale.
An evaluation method for improving the consumption capability of renewable energy sources through wind power heating comprises the following steps:
establishing a system wind curtailment evaluation model by using a random production simulation method;
acquiring operation parameters of a system, inputting the operation parameters into the system wind curtailment evaluation model, and outputting an initial wind curtailment index;
correcting and considering the operating parameters of the system after wind power heating, inputting the corrected operating parameters into the system wind curtailment evaluation model, and outputting corrected wind curtailment indexes;
and obtaining a promotion consumption index according to the initial wind curtailment index and the corrected wind curtailment index.
In one embodiment, the establishing a system wind curtailment assessment model by using a stochastic production simulation method includes:
acquiring a power load cumulative probability function and a thermal load cumulative probability function according to historical operating data, and generating an equivalent continuous load curve and a thermal load continuous curve;
utilizing the equivalent continuous load curve and the thermal load continuous curve to constrain the power of a conventional thermal power generating unit, a cogeneration unit and wind power which successively participate in production, and obtaining a wind curtailment power function; wherein the curtailed wind power function is composed of the equivalent continuous load curve and a wind power curve;
and calculating a wind abandoning index according to the wind abandoning electric quantity function.
In one embodiment, the constraining the powers of the conventional thermal power generating unit, the cogeneration unit and the wind power which successively participate in production by using the equivalent continuous load curve and the thermal load continuous curve includes:
and sequentially filling the lowest power of the non-heat-supply thermal power generating unit, the lowest power of the heat-supply thermal power generating unit, the adjustable power of the cogeneration unit and the wind power below the equivalent continuous load curve.
In one embodiment, the sequentially filling the adjustable power and the wind power of the cogeneration unit below the equivalent continuous load curve includes:
mapping a thermal load continuous curve to an equivalent continuous load curve through an electric heating power curve so as to enable the adjustable power of the cogeneration unit to be filled below the equivalent continuous load curve;
and filling the curve of the wind power between the total electric power curve and the equivalent continuous load curve of the thermal power generating unit and the cogeneration unit.
In one embodiment, the operation parameters comprise a power load cumulative probability function, a thermal load cumulative probability function, a conventional thermal power unit technical parameter and a wind power probability density function;
the acquiring of the operating parameters of the system comprises:
acquiring power supply power and heat supply power of a cogeneration unit, and generating an electric load cumulative probability function and a thermal load cumulative probability function;
acquiring installed capacity, outage rate and coal consumption as technical parameters of the conventional thermal power generating unit;
and synthesizing the wind power probability density function according to the wind resource probability distribution and the characteristics of the fan.
In one embodiment, the modifying takes into account an operating parameter of the system after wind power heating, inputs the modified operating parameter into the system wind curtailment evaluation model, and outputs a modified wind curtailment index, including:
acquiring the operating power and the operating time of an electric boiler for wind power heating;
correcting the equivalent continuous load curve and the thermal load continuous curve by using the operating power and the operating time to obtain a corrected power load cumulative probability function and a corrected thermal load cumulative probability function;
and inputting the corrected power load cumulative probability function, the corrected thermal load cumulative probability function, the technical parameters of the conventional thermal power generating unit and the wind power probability density function into the system wind curtailment evaluation model, and outputting a corrected wind curtailment index.
In one embodiment, when the electric boiler for wind power heating is in normal operation, the correcting the equivalent continuous load curve and the thermal load continuous curve to obtain a corrected power load cumulative probability function and a corrected thermal load cumulative probability function includes:
determining the operating power of an electric boiler for wind power heating;
and generating an electric load cumulative probability function and a thermal load cumulative probability function during the wind power heating operation and outage period in the heating period.
In one embodiment, when the electric boiler for wind power heating is shut down, the correcting the equivalent continuous load curve and the thermal load continuous curve to obtain a corrected power load cumulative probability function and a corrected thermal load cumulative probability function includes:
and according to the unavailability of the electric boiler, performing convolution and correction on the equivalent continuous load curve and the thermal load curve to obtain a corrected power load cumulative probability function and a corrected thermal load cumulative probability function.
An apparatus for wind power heating to improve renewable energy consumption, wherein the apparatus comprises:
the model establishing module is used for establishing a system wind curtailment evaluation model by using a random production simulation method;
the index operation module is used for acquiring operation parameters of a system, inputting the operation parameters into the system wind curtailment evaluation model and outputting an initial wind curtailment index;
the index operation module is also used for correcting and considering the operation parameters of the system after wind power heating, inputting the corrected operation parameters into the system wind curtailment evaluation model and outputting corrected curtailment indexes;
and the consumption evaluation module is used for obtaining a wind power heating promotion consumption index according to the initial wind abandoning index and the corrected wind abandoning index.
A computer device comprising a memory and a processor, the memory having stored thereon a computer program operable on the processor, the processor when executing the computer program implementing the steps of:
establishing a system wind curtailment evaluation model by using a random production simulation method;
acquiring operation parameters of a system, inputting the operation parameters into the system wind curtailment evaluation model, and outputting an initial wind curtailment index;
correcting and considering the operating parameters of the system after wind power heating, inputting the corrected operating parameters into the system wind curtailment evaluation model, and outputting corrected wind curtailment indexes;
and obtaining a promotion consumption index according to the initial wind curtailment index and the corrected wind curtailment index.
A computer-readable storage medium, on which a computer program is stored which, when executed by a processor, carries out the steps of:
establishing a system wind curtailment evaluation model by using a random production simulation method;
acquiring operation parameters of a system, inputting the operation parameters into the system wind curtailment evaluation model, and outputting an initial wind curtailment index;
correcting and considering the operating parameters of the system after wind power heating, inputting the corrected operating parameters into the system wind curtailment evaluation model, and outputting corrected wind curtailment indexes;
and obtaining a promotion consumption index according to the initial wind curtailment index and the corrected wind curtailment index.
According to the method, the device, the computer equipment and the computer readable storage medium for evaluating the renewable energy consumption improving capability of wind power heating, a system wind abandon evaluation model is established on the basis of a random production simulation method, the influence of the wind power heating on the operation parameters of the system and the probability distribution correction effect are considered, a set of evaluation method suitable for planning of wind power heating and wind abandon power consumption promoting effect is established, wind abandon index calculation and wind power heating effect evaluation of medium and long time scales can be carried out, and effective theoretical support is provided for planning and implementation of a wind power heating project. Meanwhile, the method solves the wind abandoning index of wind power heating by utilizing the system load probability characteristic and the wind power probability characteristic, and has the advantages of simpler data requirement and more convenient calculation process.
Drawings
Fig. 1 is a flowchart of a method for evaluating wind power heating increase digestion capability according to an embodiment of the present invention;
fig. 2 is a flowchart of establishing a system wind curtailment evaluation model by using a random production simulation method according to an embodiment of the present invention;
fig. 3 is a flowchart of an evaluation model for wind curtailment of an operating system after wind power heating is taken into consideration according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a device for improving the absorption capacity of wind power heating according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of a computer device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
As shown in fig. 1, the present embodiment provides an evaluation method for improving the renewable energy consumption capability in wind power heating, including the following steps:
and step S10, establishing a system wind curtailment evaluation model by using a random production simulation method.
The random production simulation method is essentially used for performing medium-term and long-term production calculation by utilizing probabilistic description of loads and units, and has principle advantages when renewable energy electric quantity evaluation is performed because rate probability distribution of fluctuating power supplies such as wind power has certain regularity but specific time sequence information cannot be solved under a long-time scale.
The system wind curtailment evaluation model is a set of evaluation method for promoting the consumption capability of the system renewable energy, the input quantity is the operation parameter of the system, and the output quantity is the wind curtailment index.
And step S20, obtaining the operation parameters of the system, inputting the operation parameters into the system wind curtailment evaluation model, and outputting an initial wind curtailment index.
The operation parameters of the system refer to parameters of each unit participating in system operation, and include: the system comprises an electric power load cumulative probability function, a thermal power load cumulative probability function, technical parameters of a conventional thermal power generating unit and a wind power probability density function.
The initial wind curtailment index comprises an initial wind curtailment electric quantity and an initial wind curtailment rate.
And step S30, correcting and considering the operation parameters of the system after wind power heating, inputting the corrected operation parameters into a system wind curtailment evaluation model, and outputting corrected wind curtailment indexes.
In the embodiment of the present invention, in the steps S10 and S20, the operation parameters of each unit of the system are input into the system wind curtailment evaluation model without considering wind power heating, and a wind curtailment index is output.
In step S30, the influence of wind power heating on the system is considered, the operating parameters of the system are corrected, so as to obtain corrected operating parameters of the system, the corrected operating parameters are input into the system wind curtailment evaluation model, and the corrected wind curtailment index is output. And the corrected wind abandoning index comprises the corrected wind abandoning electric quantity and the corrected wind abandoning rate.
And step S40, obtaining a lifting consumption index according to the initial wind abandoning index and the corrected wind abandoning index.
In the embodiment of the invention, the difference value between the initial wind abandoning index and the corrected wind abandoning index is calculated to obtain the index of the increased consumption, and the index is used for evaluating the consumption effect of wind power heating on improving renewable energy. And the lifting consumption index comprises a abandoned wind electric quantity lifting amount and a abandoned wind rate lifting amount. Specifically, the boost consumption index is calculated by the following formula:
the wind curtailment electric quantity lifting amount is equal to the initial wind curtailment electric quantity-the corrected wind curtailment electric quantity
Wind curtailment rate increase is equal to initial wind curtailment rate-corrected wind curtailment rate
According to the evaluation method for improving the renewable energy consumption capability of wind power heating, provided by the embodiment, the system wind curtailment evaluation model is established on the basis of the random production simulation method, the influence of the wind power heating on the operation parameters of the system and the correction effect of probability distribution are considered, a set of evaluation method suitable for planning of the wind power heating and wind curtailment promotion effect is established, and the calculation of the wind curtailment power and the evaluation of the wind power heating effect of medium and long time scales can be performed.
As shown in fig. 2, a schematic flowchart of the refining step of step S10 specifically includes:
and step S101, obtaining a power load cumulative probability function and a thermal load cumulative probability function according to historical operation data, and generating an equivalent continuous load curve and a thermal load continuous curve.
In the embodiment of the invention, according to historical operating data, an Equivalent continuous Load function (ELDC) and a thermal Load continuous function (HLDC) of all power loads in a research period are generated according to a common Load probability model, and an Equivalent continuous Load Curve and a thermal Load continuous Curve are further generated.
The electrical load and the heating load (thermal load) are obtained from the grid data.
The common cumulative probability model is:
1-cumulative probability function of load
Specifically, the equivalent continuous load function of the power load is 1-the cumulative probability function of the power load, and the continuous load function of the thermal load is 1-the cumulative probability function of the thermal load.
And according to the accumulative probability model, calculating to obtain an equivalent continuous load function and a thermodynamic load continuous function of the power load, and then generating an equivalent continuous load curve and a thermodynamic load continuous curve.
Step S102, restraining the power of a conventional thermal power generating unit, a cogeneration unit and wind power which successively participate in production by using an equivalent continuous load curve and the thermal load continuous curve to obtain a wind power abandoning function; wherein the curtailment wind power function is composed of an ELDC curve and a wind power curve.
In the embodiment of the invention, the ELDC curve and the HLDC curve are used for constraining the power of the conventional thermal power generating unit, the cogeneration unit and the wind power which successively participate in production, so that a wind curtailment power function is obtained.
The conventional thermal power generating unit comprises a heat supply thermal power generating unit and a non-heat supply thermal power generating unit.
The abandoned wind power function is a relation function between a wind power probability curve and an ELDC curve, wherein the relation function is obtained after the ELDC curve and the HLDC curve constrain the power of a conventional thermal power generating unit, a cogeneration unit and wind power which successively participate in production.
And step S103, calculating a wind abandoning index according to the wind abandoning electric quantity function.
And the abandoned wind index comprises abandoned wind electric quantity and abandoned wind rate.
The abandoned wind power is calculated according to a abandoned wind power function according to the following formula:
in the formula, EcurTo discard wind power, PmaxMaximum value arranged for wind power, LmaxIs the maximum value of the electrical load in the system, Fw(x) The probability function is a wind power cumulative probability function, f (x) is a system power load cumulative probability function, and Peq is an intersection point of an ELDC curve and the wind power probability curve. Wherein,
Fww(Peq)＝f(Peq)
the air abandonment rate is calculated according to the following formula:
in the formula, kcurTo abandon the wind rate, EcurTo discard wind power, PmaxFor the maximum value after the wind power arrangement,Fw(x) Is the cumulative probability function of the wind power.
In the embodiment of the invention, a system wind curtailment evaluation model is established by using a random production simulation method, the power of each unit is calculated by adopting the cumulative probability, and the wind curtailment electric quantity calculation of medium and long time scales can be carried out on the basis of only the system load probability characteristic and the wind power supply probability characteristic. Compared with the traditional time sequence solving method, the data requirement is simpler, and the calculation process is more convenient.
Optionally, in an embodiment, the constraining the powers of the conventional thermal power generating unit, the cogeneration unit and the wind power that successively participate in production by using the ELDC curve and the HLDC curve in step S102 specifically includes: and sequentially filling the lowest power of the non-heat-supply thermal power generating unit, the lowest power of the heat-supply thermal power generating unit, the adjustable power of the cogeneration unit and the wind power below the ELDC curve.
In the embodiment of the invention, the ELDC curve and the HLDC curve are utilized to restrain the power of a conventional thermal power generating unit, a cogeneration unit and wind power which successively participate in production, and the lowest power of a non-heat-supply thermal power generating unit, the lowest power of a heat-supply thermal power generating unit, the adjustable power of the cogeneration unit and the wind power are sequentially filled below the ELDC curve, and the method comprises the following steps:
firstly, filling the lowest power of the non-heat-supply thermal power generating unit below an ELDC curve.
And calculating the average coal consumption rate in the base load and peak load sections of all the non-heat-supply thermal power generating units, arranging the average coal consumption rates in ascending order to ensure that the coal consumption of the system for generating electricity is minimum, determining the arrangement sequence of the thermal power generating units participating in electricity generation, arranging the base load capacity (lowest power) of the non-heat-supply thermal power generating units to participate in production in sequence, and filling the lowest power of the non-heat-supply thermal power generating units below an ELDC curve.
And secondly, filling the lowest power of the thermal power supply unit below the ELDC curve.
Assuming that N non-heat supply units are already involved in arrangement, for M cogeneration units, the base charge parts (lowest power) of the M cogeneration units are arranged on the ELDC and the HLDC according to the following formula to participate in production, and the lowest power of the heat supply thermal power unit is filled below an ELDC curve.
In the formula, HMINMinimum thermal power H of each unitiminSum and system heat demand minimum
Taking the maximum value between, GL(H) Is a system heat load curve, Ft(x) Is the electric power curve of the thermoelectric power unit, f (x) is the probability distribution function of the system electric load, GM(H) The continuous thermal power curve of M sets is shown.
And step three, filling the adjustable power of the cogeneration unit below an ELDC curve, specifically: mapping an HLDC curve onto an ELDC curve through an electric-to-thermal power curve such that an adjustable power of the cogeneration unit fills below the ELDC curve.
The adjustable capacity (adjustable power) of the cogeneration unit is arranged to participate in production, and the constraint condition is to meet the thermal load of the supply system. The adjustable capacity (adjustable power) is a capacity between the most stable output and the rated power.
For a cogeneration unit, assume that the ith station has a heating power HiAnd generated power PiThere are the following relationships:
in the formula, Prg,prgRespectively an accumulative probability function and a probability density function of the thermal power of the thermoelectric unit, Gi,giRespectively, the function names thereof; prp,prpThe probability function and the probability density function of the electric power of the thermoelectric generating set are respectively.
The electric heating power curve of the cogeneration unit can be simplified into a linear model:
in the formula, ai,biAre respectively linear coefficients of an electrothermal relationship, and biThe minimum stable electric power of the unit is provided for the heating season.
When the cogeneration units in the system are determined on line and the sequence thereof, the overall 'electricity-heat' relationship at the power supply side can be uniquely determined and recorded as
Csys＝Fsys(Hsys)
In the formula, Csys，HsysRespectively the electric power and the thermal power of the system cogeneration unit, and the relation function FsysIs a strictly monotonically increasing function.
For the ith unit, the interval values of the internal heat power and the electric power of the j section are respectively delta H and delta x, and when the adjustable part participates in the production, the following electric heat power relations are provided:
in the formula, Hpos、xposRespectively the thermal and electrical load locations to which the system has been arranged.
And for each cogeneration unit, mapping the probability value of each unit on the HLDC curve to the ELDC curve according to the electric-heat power relation of the cogeneration unit, so that the adjustable power of the cogeneration unit is filled below the ELDC curve.
Fourthly, filling the wind power below an ELDC curve, specifically: and filling the curve of the wind power between the total electric power curve and the ELDC curve of the thermal power generating unit and the cogeneration unit.
Generating a wind power cumulative probability curve according to the following formula, and filling the wind power cumulative probability curve between power curves of a thermal power generating unit and a cogeneration unit and an ELDC curve:
in the formula (f)w(x) Is composed ofWind power probability density.
It should be noted that the above four steps are performed in sequence, and the powers of the conventional thermal power generating unit, the cogeneration unit and the wind power which participate in production in sequence, that is, the lowest power of the non-heat-supply thermal power generating unit, the lowest power of the heat-supply thermal power generating unit, the adjustable power of the cogeneration unit and the wind power, all adopt the cumulative probability distribution to participate in the ELDC curve filling.
Optionally, in an embodiment, the operation parameters include an electric power load cumulative probability function, a thermal power load cumulative probability function, technical parameters of a conventional thermal power generating unit, and a wind power probability density function, and the obtaining of the operation parameters of the system in step S20 specifically includes:
and acquiring power supply power and heat supply power of the cogeneration unit, and generating the power load cumulative probability function and the thermal load cumulative probability function.
In the embodiment of the invention, the power load cumulative probability function and the thermal load cumulative probability function are generated by acquiring the power supply power and the heat supply power of the cogeneration unit.
And acquiring installed capacity, outage rate and coal consumption as technical parameters of the conventional thermal power generating unit.
In the embodiment of the invention, the technical parameters of the conventional thermal power generating unit comprise installed capacity, outage rate and coal consumption.
And synthesizing the wind power probability density function according to the wind resource probability distribution and the characteristics of the fan.
In the embodiment of the invention, the wind power probability density function is obtained by synthesizing wind resource probability distribution and fan characteristics. The existing acknowledged wind resource probability distribution is mostly Weibull distribution:
in the formula, swIs a wind speed value with the unit of m/s; k is a shape parameter; and c is a scale parameter which reflects the average wind speed value in the statistical week.
Because the fan power generation has the characteristic of piecewise linearity and has the saturation property, the actually synthesized wind power probability density function is obviously different from the Weibull curve.
In an embodiment, as shown in fig. 3, a schematic flowchart of the refining step of step S30 specifically includes:
and S301, acquiring the operation power and the operation time of the electric boiler for wind power heating.
In the embodiment of the invention, the heating period T is in a certain areahIn the electric boiler, the electric boiler is used for a period of time t every days,te]Internal normal operation ThdHour for wind power P in the periodwwAnd long-term statistics is carried out, and a proper power quantile α is selected, so that the wind power consumption can be guaranteed under a certain probability.
Installation capacity (operation power) C of the electric boiler in the operation periodhgIs determined by the following formula:
Chg＝Pww|p(x>Pww)＝1-α
in the formula, P (P)ww) Representing the statistical probability of the wind power value.
Step S302, the equivalent continuous load curve and the thermal load continuous curve are corrected by utilizing the running power and the running time, and a corrected power load cumulative probability function and a corrected thermal load cumulative probability function are obtained.
In the embodiment of the invention, the ELDC curve and the HLDC curve are corrected by using the determined running power and running time:
in a statistical period ThInternal to section electric load and thermal load respectively
Generating cumulative probability curves corresponding to fL1(x),fL2(x),G1(H),G2(H) Wherein f isL1(x),fL2(x) Electrical loads corresponding respectively to periods of operation and outage of wind power heating, G1(H),G2(H) The system thermal load corresponds to the thermal load of the system during the operation and the shutdown of the wind power heating respectively.
Wherein, the correction of the equivalent continuous load curve and the thermal load continuous curve adopts two different methods under the two conditions of normal operation and shutdown of the electric boiler used for wind power heating, and is specific:
the first method comprises the following steps: when the electric boiler for wind power heating normally operates, determining the operating power of the electric boiler for wind power heating; and generating an electric load cumulative probability function and a thermal load cumulative probability function during the wind power heating operation and outage period in the heating period.
When the electric boiler used for wind power heating normally operates, the total cumulative probability distribution function of the power load and the heat load in the period is counted as the sum of all the sections according to the following formula:
the electric boiler mainly has an influence on the cumulative probability curve over the second section. For the HLDC curve, the modified function is:
in the formula, HmaxShows the original thermal load curve G2(H) Maximum value of medium thermal load, HhgIs the thermal output of an electric boiler, G'2(H) Showing the cumulative probability curve after the electric boiler is added.
Similar to the above process, the original ELDC curve in the second segment is fL2(x) Equivalent to t in constant power mode of electric food warmer boilers,te]Increasing the load value by C at each moment in the time intervalhg。
For an ELDC curve, the modified function is:
and the second method comprises the following steps: when the electric boiler for wind power heating is shut down, the equivalent continuous load curve and the thermal load curve are convolved and corrected according to the unavailability of the electric boiler, and a corrected power load cumulative probability function and a corrected thermal load cumulative probability function are obtained.
If the electric boiler is shut down due to faults, maintenance and the like, the HLDC and ELDC curves of the areas are changed. At this time, the heat load cumulative probability function and the power load cumulative probability function are corrected as follows:
defining the unavailability A of the electric boiler in the whole area:
in the formula, qiT is a statistical period for the forced outage rate of each electric boiler,
and the maintenance outage time of the ith electric heating boiler is calculated.
The shutdown of the electric boiler is equivalent to the increase of the thermal load and the reduction of the electric load of the cogeneration unit. Convolving and correcting the HLDC curve according to a convolution principle, wherein the corrected function is as follows:
when convolution difference correction is performed on the ELDC curve,
and f'L2(x) Has the following relationship:
function after correction
Can be obtained by backward calculation.
And obtaining a corrected power load cumulative probability function and a corrected thermal load cumulative probability function considering wind power heating by adopting the correction processing.
And step S303, inputting the corrected power load cumulative probability function, the corrected thermal load cumulative probability function, the technical parameters of the conventional thermal power generating unit and the wind power probability density function into the system abandoned wind evaluation model, and outputting a corrected abandoned wind index.
In one embodiment, the corrected power load cumulative probability function, the corrected thermal load cumulative probability function, the technical parameters of the conventional thermal power generating unit and the wind power probability density function are input into the system wind curtailment evaluation model established in step S10, and a corrected wind curtailment index is output.
In the embodiment, different correction methods are adopted for normal operation and shutdown of the electric boiler used for wind power heating, on the basis of system load probability characteristics and wind power supply probability characteristics, the influence of the wind power heating on system operation parameters is considered to perform probability method correction, and the corrected power load cumulative probability function and thermal load cumulative probability function enable the system operation parameters to be more fit with an actual scene, so that the method is suitable for calculating the wind abandoning index of a medium-long time scale.
It should be understood that although the various steps in the flow charts of fig. 1-3 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least some of the steps in fig. 1-3 may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performance of the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternating with other steps or at least some of the sub-steps or stages of other steps.
As shown in fig. 4, an embodiment of the present invention further provides an apparatus for improving the renewable energy consumption by wind power heating, where the apparatus includes:
the model establishing module 100 is used for establishing a system wind curtailment evaluation model by using a random production simulation method;
the index operation module 200 is used for acquiring operation parameters of a system, inputting the operation parameters into the system wind curtailment evaluation model and outputting an initial wind curtailment index;
the index operation module 200 is further configured to correct an operation parameter of the system after wind power heating is considered, input the corrected operation parameter into the system wind curtailment evaluation model, and output a corrected wind curtailment index;
and the consumption evaluation module 300 is configured to obtain a wind power heating promotion consumption index according to the initial wind abandoning index and the corrected wind abandoning index.
As an optional implementation manner, the model building module 100 is specifically configured to:
acquiring a power load cumulative probability function and a thermal load cumulative probability function according to historical operating data, and generating an equivalent continuous load curve and a thermal load continuous curve;
utilizing the equivalent continuous load curve and the thermal load continuous curve to constrain the power of a conventional thermal power generating unit, a cogeneration unit and wind power which successively participate in production, and obtaining a wind curtailment power function; wherein the curtailed wind power function is composed of the equivalent continuous load curve and a wind power curve;
and calculating a wind abandoning index according to the wind abandoning electric quantity function.
As an optional implementation, the model building module 100 is further configured to:
and sequentially filling the lowest power of the non-heat-supply thermal power generating unit, the lowest power of the heat-supply thermal power generating unit, the adjustable power of the cogeneration unit and the wind power below the equivalent continuous load curve.
As an optional implementation, the model building module 100 is further configured to:
mapping a thermal load continuous curve to an equivalent continuous load curve through an electric heating power curve so as to enable the adjustable power of the cogeneration unit to be filled below the equivalent continuous load curve;
and filling the curve of the wind power between the total electric power curve and the equivalent continuous load curve of the thermal power generating unit and the cogeneration unit.
As an optional implementation manner, the index operation module 200 is configured to:
acquiring power supply power and heat supply power of a cogeneration unit, and generating an electric load cumulative probability function and a thermal load cumulative probability function;
acquiring installed capacity, outage rate and coal consumption as technical parameters of the conventional thermal power generating unit;
and synthesizing the wind power probability density function according to the wind resource probability distribution and the characteristics of the fan.
As an optional implementation manner, the index operation module 200 is further configured to:
acquiring the operating power and the operating time of an electric boiler for wind power heating;
correcting the equivalent continuous load curve and the thermal load continuous curve by using the operating power and the operating time to obtain a corrected power load cumulative probability function and a corrected thermal load cumulative probability function;
and inputting the corrected power load cumulative probability function, the corrected thermal load cumulative probability function, the technical parameters of the conventional thermal power generating unit and the wind power probability density function into the system wind curtailment evaluation model, and outputting a corrected wind curtailment index.
As an optional implementation manner, the index operation module 200 is further configured to:
determining the operating power of an electric boiler for wind power heating;
and generating an electric load cumulative probability function and a thermal load cumulative probability function during the wind power heating operation and outage period in the heating period.
As an optional implementation manner, the index operation module 200 is further configured to:
and according to the unavailability of the electric boiler, performing convolution and correction on the equivalent continuous load curve and the thermal load curve to obtain a corrected power load cumulative probability function and a corrected thermal load cumulative probability function.
The specific limitation of the device for improving the renewable energy consumption capability by wind power heating can be referred to the above limitation of the evaluation method for improving the renewable energy consumption capability by wind power heating, and is not described herein again. All modules in the device for improving the renewable energy consumption capacity by wind power heating can be completely or partially realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 5. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer equipment is used for storing the operation parameters, the wind curtailment index and the lifting consumption index of the system. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to realize the evaluation method for improving the renewable energy consumption capacity of wind power heating.
Those skilled in the art will appreciate that the architecture shown in fig. 5 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, there is provided a computer device comprising a memory and a processor, the memory having stored thereon a computer program operable on the processor, the processor implementing the following steps when executing the computer program:
establishing a system wind curtailment evaluation model by using a random production simulation method;
acquiring operation parameters of a system, inputting the operation parameters into the system wind curtailment evaluation model, and outputting an initial wind curtailment index;
correcting and considering the operating parameters of the system after wind power heating, inputting the corrected operating parameters into the system wind curtailment evaluation model, and outputting corrected wind curtailment indexes;
and obtaining a promotion consumption index according to the initial wind curtailment index and the corrected wind curtailment index.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
acquiring a power load cumulative probability function and a thermal load cumulative probability function according to historical operating data, and generating an equivalent continuous load curve and a thermal load continuous curve;
utilizing the equivalent continuous load curve and the thermal load continuous curve to constrain the power of a conventional thermal power generating unit, a cogeneration unit and wind power which successively participate in production, and obtaining a wind curtailment power function; wherein the curtailed wind power function is composed of the equivalent continuous load curve and a wind power curve;
and calculating a wind abandoning index according to the wind abandoning electric quantity function.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
and sequentially filling the lowest power of the non-heat-supply thermal power generating unit, the lowest power of the heat-supply thermal power generating unit, the adjustable power of the cogeneration unit and the wind power below the equivalent continuous load curve.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
mapping a thermal load continuous curve to an equivalent continuous load curve through an electric heating power curve so as to enable the adjustable power of the cogeneration unit to be filled below the equivalent continuous load curve;
and filling the curve of the wind power between the total electric power curve and the equivalent continuous load curve of the thermal power generating unit and the cogeneration unit.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
acquiring power supply power and heat supply power of a cogeneration unit, and generating an electric load cumulative probability function and a thermal load cumulative probability function;
acquiring installed capacity, outage rate and coal consumption as technical parameters of the conventional thermal power generating unit;
and synthesizing the wind power probability density function according to the wind resource probability distribution and the characteristics of the fan.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
acquiring the operating power and the operating time of an electric boiler for wind power heating;
correcting the equivalent continuous load curve and the thermal load continuous curve by using the operating power and the operating time to obtain a corrected power load cumulative probability function and a corrected thermal load cumulative probability function;
and inputting the corrected power load cumulative probability function, the corrected thermal load cumulative probability function, the technical parameters of the conventional thermal power generating unit and the wind power probability density function into the system wind curtailment evaluation model, and outputting a corrected wind curtailment index.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
determining the operating power of an electric boiler for wind power heating;
and generating an electric load cumulative probability function and a thermal load cumulative probability function during the wind power heating operation and outage period in the heating period.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
and according to the unavailability of the electric boiler, performing convolution and correction on the equivalent continuous load curve and the thermal load curve to obtain a corrected power load cumulative probability function and a corrected thermal load cumulative probability function.
In one embodiment, a computer-readable storage medium is provided, having stored thereon a computer program which, when executed by a processor, performs the steps of:
establishing a system wind curtailment evaluation model by using a random production simulation method;
acquiring operation parameters of a system, inputting the operation parameters into the system wind curtailment evaluation model, and outputting an initial wind curtailment index;
correcting and considering the operating parameters of the system after wind power heating, inputting the corrected operating parameters into the system wind curtailment evaluation model, and outputting corrected wind curtailment indexes;
and obtaining a promotion consumption index according to the initial wind curtailment index and the corrected wind curtailment index.
In one embodiment, the computer program when executed by the processor further performs the steps of:
acquiring a power load cumulative probability function and a thermal load cumulative probability function according to historical operating data, and generating an equivalent continuous load curve and a thermal load continuous curve;
utilizing the equivalent continuous load curve and the thermal load continuous curve to constrain the power of a conventional thermal power generating unit, a cogeneration unit and wind power which successively participate in production, and obtaining a wind curtailment power function; wherein the curtailed wind power function is composed of the equivalent continuous load curve and a wind power curve;
and calculating a wind abandoning index according to the wind abandoning electric quantity function.
In one embodiment, the computer program when executed by the processor further performs the steps of:
and sequentially filling the lowest power of the non-heat-supply thermal power generating unit, the lowest power of the heat-supply thermal power generating unit, the adjustable power of the cogeneration unit and the wind power below the equivalent continuous load curve.
In one embodiment, the computer program when executed by the processor further performs the steps of:
mapping a thermal load continuous curve to an equivalent continuous load curve through an electric heating power curve so as to enable the adjustable power of the cogeneration unit to be filled below the equivalent continuous load curve;
and filling the curve of the wind power between the total electric power curve and the equivalent continuous load curve of the thermal power generating unit and the cogeneration unit.
In one embodiment, the computer program when executed by the processor further performs the steps of:
acquiring power supply power and heat supply power of a cogeneration unit, and generating an electric load cumulative probability function and a thermal load cumulative probability function;
acquiring installed capacity, outage rate and coal consumption as technical parameters of the conventional thermal power generating unit;
and synthesizing the wind power probability density function according to the wind resource probability distribution and the characteristics of the fan.
In one embodiment, the computer program when executed by the processor further performs the steps of:
acquiring the operating power and the operating time of an electric boiler for wind power heating;
correcting the equivalent continuous load curve and the thermal load continuous curve by using the operating power and the operating time to obtain a corrected power load cumulative probability function and a corrected thermal load cumulative probability function;
and inputting the corrected power load cumulative probability function, the corrected thermal load cumulative probability function, the technical parameters of the conventional thermal power generating unit and the wind power probability density function into the system wind curtailment evaluation model, and outputting a corrected wind curtailment index.
In one embodiment, the computer program when executed by the processor further performs the steps of:
determining the operating power of an electric boiler for wind power heating;
and generating an electric load cumulative probability function and a thermal load cumulative probability function during the wind power heating operation and outage period in the heating period.
In one embodiment, the computer program when executed by the processor further performs the steps of:
and according to the unavailability of the electric boiler, performing convolution and correction on the equivalent continuous load curve and the thermal load curve to obtain a corrected power load cumulative probability function and a corrected thermal load cumulative probability function.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (11)
1. An evaluation method for improving the consumption capability of renewable energy sources through wind power heating is characterized by comprising the following steps:
establishing a system wind curtailment evaluation model by using a random production simulation method;
acquiring operation parameters of a system, inputting the operation parameters into the system wind curtailment evaluation model, and outputting an initial wind curtailment index;
correcting and considering the operating parameters of the system after wind power heating, inputting the corrected operating parameters into the system wind curtailment evaluation model, and outputting corrected wind curtailment indexes;
and obtaining a promotion consumption index according to the initial wind curtailment index and the corrected wind curtailment index.
2. The method of claim 1, wherein the establishing a system wind curtailment assessment model by using a stochastic production simulation method comprises:
acquiring a power load cumulative probability function and a thermal load cumulative probability function according to historical operating data, and generating an equivalent continuous load curve and a thermal load continuous curve;
utilizing the equivalent continuous load curve and the thermal load continuous curve to constrain the power of a conventional thermal power generating unit, a cogeneration unit and wind power which successively participate in production, and obtaining a wind curtailment power function; wherein the curtailed wind power function is composed of the equivalent continuous load curve and a wind power curve;
and calculating a wind abandoning index according to the wind abandoning electric quantity function.
3. The method according to claim 2, wherein the step of constraining the power of a conventional thermal power generating unit, a cogeneration unit and wind power which successively participate in production by using the equivalent continuous load curve and the thermodynamic load continuous curve comprises the following steps:
and sequentially filling the lowest power of the non-heat-supply thermal power generating unit, the lowest power of the heat-supply thermal power generating unit, the adjustable power of the cogeneration unit and the wind power below the equivalent continuous load curve.
4. The method according to claim 3, wherein said sequentially filling the adjustable power and the wind power of the cogeneration unit below the equivalent continuous load curve comprises:
mapping a thermal load continuous curve to an equivalent continuous load curve through an electric heating power curve so as to enable the adjustable power of the cogeneration unit to be filled below the equivalent continuous load curve;
and filling the curve of the wind power between the total electric power curve and the equivalent continuous load curve of the thermal power generating unit and the cogeneration unit.
5. The method of claim 1, wherein the operating parameters include a power load cumulative probability function, a thermal load cumulative probability function, a conventional thermal power unit technology parameter, and a wind power probability density function;
the acquiring of the operating parameters of the system comprises:
acquiring power supply power and heat supply power of a cogeneration unit, and generating an electric load cumulative probability function and a thermal load cumulative probability function;
acquiring installed capacity, outage rate and coal consumption as technical parameters of the conventional thermal power generating unit;
and synthesizing the wind power probability density function according to the wind resource probability distribution and the characteristics of the fan.
6. The method of claim 1, wherein the modifying takes into account operating parameters of the wind powered heated system, inputs the modified operating parameters into the system wind curtailment assessment model, and outputs a modified curtailment indicator, comprising:
acquiring the operating power and the operating time of an electric boiler for wind power heating;
correcting the equivalent continuous load curve and the thermal load continuous curve by using the operating power and the operating time to obtain a corrected power load cumulative probability function and a corrected thermal load cumulative probability function;
and inputting the corrected power load cumulative probability function, the corrected thermal load cumulative probability function, the technical parameters of the conventional thermal power generating unit and the wind power probability density function into the system wind curtailment evaluation model, and outputting a corrected wind curtailment index.
7. The method according to claim 6, wherein when the electric boiler for wind power heating is in normal operation, the modifying the equivalent continuous load curve and the thermal load continuous curve to obtain a modified electric load cumulative probability function and a modified thermal load cumulative probability function comprises:
determining the operating power of an electric boiler for wind power heating;
and generating an electric load cumulative probability function and a thermal load cumulative probability function during the wind power heating operation and outage period in the heating period.
8. The method of claim 6, wherein the modifying the equivalent continuous load curve and the thermal load continuous curve to obtain the modified cumulative probability function of the electrical load and the modified cumulative probability function of the thermal load when the electric boiler for wind power heating is shut down comprises:
and according to the unavailability of the electric boiler, performing convolution and correction on the equivalent continuous load curve and the thermal load curve to obtain a corrected power load cumulative probability function and a corrected thermal load cumulative probability function.
9. An apparatus for improving the renewable energy consumption of wind power heating, the apparatus comprising:
the model establishing module is used for establishing a system wind curtailment evaluation model by using a random production simulation method;
the index operation module is used for acquiring operation parameters of a system, inputting the operation parameters into the system wind curtailment evaluation model and outputting an initial wind curtailment index;
the index operation module is also used for correcting and considering the operation parameters of the system after wind power heating, inputting the corrected operation parameters into the system wind curtailment evaluation model and outputting corrected curtailment indexes;
and the consumption evaluation module is used for obtaining a wind power heating promotion consumption index according to the initial wind abandoning index and the corrected wind abandoning index.
10. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the method of any one of claims 1 to 8 when executing the computer program.
11. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 8.
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