CN110766259B - Evaluation method and device for improving renewable energy source digestion capability of wind power heating - Google Patents
Evaluation method and device for improving renewable energy source digestion capability of wind power heating Download PDFInfo
- Publication number
- CN110766259B CN110766259B CN201910335035.3A CN201910335035A CN110766259B CN 110766259 B CN110766259 B CN 110766259B CN 201910335035 A CN201910335035 A CN 201910335035A CN 110766259 B CN110766259 B CN 110766259B
- Authority
- CN
- China
- Prior art keywords
- wind
- power
- load
- curve
- wind power
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000010438 heat treatment Methods 0.000 title claims abstract description 100
- 230000029087 digestion Effects 0.000 title claims abstract description 26
- 238000011156 evaluation Methods 0.000 title claims abstract description 25
- 239000002699 waste material Substances 0.000 claims abstract description 101
- 238000013210 evaluation model Methods 0.000 claims abstract description 47
- 238000000034 method Methods 0.000 claims abstract description 47
- 238000004519 manufacturing process Methods 0.000 claims abstract description 37
- 238000004088 simulation Methods 0.000 claims abstract description 19
- 230000006870 function Effects 0.000 claims description 179
- 230000001186 cumulative effect Effects 0.000 claims description 119
- 238000005485 electric heating Methods 0.000 claims description 39
- 238000004590 computer program Methods 0.000 claims description 27
- 238000009826 distribution Methods 0.000 claims description 16
- 239000003245 coal Substances 0.000 claims description 10
- 238000003860 storage Methods 0.000 claims description 9
- 238000010521 absorption reaction Methods 0.000 claims description 8
- 230000002194 synthesizing effect Effects 0.000 claims description 7
- 230000006872 improvement Effects 0.000 claims description 6
- 238000013507 mapping Methods 0.000 claims description 6
- 238000010248 power generation Methods 0.000 claims description 5
- 238000004364 calculation method Methods 0.000 abstract description 10
- 230000000694 effects Effects 0.000 abstract description 10
- 238000012937 correction Methods 0.000 description 6
- 238000005265 energy consumption Methods 0.000 description 5
- 238000011160 research Methods 0.000 description 5
- 238000010586 diagram Methods 0.000 description 3
- 238000009825 accumulation Methods 0.000 description 2
- 238000005315 distribution function Methods 0.000 description 2
- 239000004973 liquid crystal related substance Substances 0.000 description 2
- 230000007774 longterm Effects 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 230000001737 promoting effect Effects 0.000 description 2
- 230000000452 restraining effect Effects 0.000 description 2
- 230000003068 static effect Effects 0.000 description 2
- 230000001360 synchronised effect Effects 0.000 description 2
- 230000001174 ascending effect Effects 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000009434 installation Methods 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000004451 qualitative analysis Methods 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 238000012163 sequencing technique Methods 0.000 description 1
- 238000003786 synthesis reaction Methods 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Physics & Mathematics (AREA)
- Human Resources & Organizations (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Data Mining & Analysis (AREA)
- Marketing (AREA)
- Computational Mathematics (AREA)
- Operations Research (AREA)
- Entrepreneurship & Innovation (AREA)
- Pure & Applied Mathematics (AREA)
- Educational Administration (AREA)
- Mathematical Physics (AREA)
- Development Economics (AREA)
- Mathematical Optimization (AREA)
- General Business, Economics & Management (AREA)
- Mathematical Analysis (AREA)
- Tourism & Hospitality (AREA)
- Health & Medical Sciences (AREA)
- Software Systems (AREA)
- Life Sciences & Earth Sciences (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Bioinformatics & Computational Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Primary Health Care (AREA)
- Public Health (AREA)
- Evolutionary Biology (AREA)
- General Engineering & Computer Science (AREA)
- Databases & Information Systems (AREA)
- Algebra (AREA)
- Probability & Statistics with Applications (AREA)
- Game Theory and Decision Science (AREA)
- Quality & Reliability (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The application relates to an evaluation method for improving renewable energy source digestion capability of wind power heating, which comprises the following steps: establishing a system abandoned wind evaluation model by using a random production simulation method; acquiring operation parameters of the system, inputting the operation parameters into a system waste wind evaluation model, and outputting initial waste wind indexes; correcting and considering the system operation parameters after wind power heating, inputting the corrected operation parameters into a system waste wind evaluation model, and outputting corrected waste wind indexes; and obtaining the enhanced digestion quantity index according to the initial waste wind index and the corrected waste wind index. The invention also relates to a device for improving the renewable energy source digestion capability of wind power heating. The method and the device can be suitable for medium-and-long-term wind power rejection calculation and wind power heating effect evaluation, and provide basis for planning and implementation of wind power heating engineering.
Description
Technical Field
The invention relates to the field of power system calculation methods, in particular to an evaluation method and device for improving renewable energy source digestion capability of wind power heating.
Background
In recent years, the situation of renewable energy consumption in China is still severe, and renewable energy enrichment provinces represented by the three north areas of China cannot completely solve the wind and energy abandoning problem. In order to improve the digestion capability and promote the effective utilization of surplus clean energy in the load off-peak period, wind power heating test point engineering is pushed from 2010 in China, however, the implementation effect is not optimistic as expected, and a wind power heating effect evaluation method suitable for the earlier planning of the system and the design stage of a market mechanism is urgently needed.
At present, most of research on wind power heating in the industry stays in the policy and qualitative analysis level, and part of the research evaluates the wind discarding effect or the energy saving and emission reduction effect of wind power heating, but most of the research is based on time sequence data of a typical scene, and the research is performed by adopting a method of optimizing and solving a set combination model or extrapolating according to a static capacity method, so that detailed time sequence data is required to be obtained in implementation, the integrity requirement on the data is extremely high, the time sequence solving method is usually mainly based on an optimization algorithm, the solving speed is low, the obtained result can only be used as a reference basis of certain special scenes, and the method is difficult to be effectively applied to medium-long-term planning scenes.
Disclosure of Invention
In view of the above, it is necessary to provide an evaluation method and apparatus that can be applied to the long-term and medium-time scale effective evaluation of the capability of wind power heating to promote renewable energy consumption.
An evaluation method for improving renewable energy source digestion capability of wind power heating, wherein the method comprises the following steps:
establishing a system abandoned wind evaluation model by using a random production simulation method;
acquiring operation parameters of a system, inputting the operation parameters into a system waste wind evaluation model, and outputting initial waste wind indexes;
correcting and considering the operation parameters of the wind power heating system, inputting the corrected operation parameters into the system waste wind evaluation model, and outputting corrected waste wind indexes;
and obtaining an enhanced digestion amount index according to the initial waste wind index and the corrected waste wind index.
In one embodiment, the establishing a system wind curtailment evaluation model by using a random production simulation method includes:
obtaining an electric load cumulative probability function and a thermodynamic load cumulative probability function according to historical operation data, and generating an equivalent continuous load curve and a thermodynamic load continuous curve;
constraining the power of a conventional thermal power generating unit, a cogeneration unit and wind power which participate in production in sequence by using the equivalent continuous load curve and the thermodynamic load continuous curve to obtain a waste wind power function; the wind discarding capacity function is composed of the equivalent continuous load curve and a wind power curve;
And calculating the wind abandoning index according to the wind abandoning power function.
In one embodiment, the constraining the power of the conventional thermal power generating unit, the cogeneration unit and the wind power which participate in production sequentially by using the equivalent continuous load curve and the thermodynamic 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 and the wind power of the cogeneration unit below the equivalent continuous load curve.
In one embodiment, the filling the adjustable power and the wind power of the cogeneration unit under the equivalent continuous load curve sequentially includes:
mapping a thermodynamic load continuous curve onto an equivalent continuous load curve through an electrothermal power curve so that adjustable power of the cogeneration unit is 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 unit and the cogeneration unit.
In one embodiment, the operating parameters include an electrical load cumulative probability function, a thermal load cumulative probability function, a conventional thermal power plant technical parameter, and an electrical power probability density function;
The acquiring the operation parameters of the system comprises:
acquiring power supply power and heat supply power of a cogeneration unit, and generating the electric load cumulative probability function and the thermodynamic load cumulative probability function;
acquiring the installed capacity, the outage rate and the coal consumption as the technical parameters of the conventional thermal power generating unit;
and synthesizing the wind power probability density function through wind resource probability distribution and fan characteristics.
In one embodiment, the correcting considers the operation parameters of the wind power heating system, inputs the corrected operation parameters into the system waste wind evaluation model, outputs corrected waste wind indexes, and includes:
acquiring the running power and the running time of an electric heating boiler for wind power heating;
correcting the equivalent continuous load curve and the thermal load continuous curve by utilizing the operating power and the operating time to obtain a corrected electric load cumulative probability function and a corrected thermal load cumulative probability function;
and inputting the corrected electric 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 waste wind evaluation model, and outputting corrected waste wind indexes.
In one embodiment, when the electric boiler for wind power heating is operating normally, the correcting the equivalent continuous load curve and the thermal load continuous curve to obtain a corrected electric load cumulative probability function and a corrected thermal load cumulative probability function includes:
determining the running power of an electric heating boiler for wind power heating;
and generating an electric load cumulative probability function and a thermodynamic load cumulative probability function of the wind power heating operation and the off period in the heating period.
In one embodiment, when the electric heating boiler for wind power heating is out of operation, the correcting the equivalent continuous load curve and the thermal load continuous curve to obtain a corrected electric load cumulative probability function and a corrected thermal load cumulative probability function includes:
and convolving and correcting the equivalent continuous load curve and the thermodynamic load curve according to the unavailability rate of the electric heating boiler to obtain a corrected electric load cumulative probability function and a corrected thermodynamic load cumulative probability function.
A device for improving renewable energy source digestion capability of wind power heating, wherein the device comprises:
the model building module is used for building a system abandoned wind evaluation model by utilizing a random production simulation method;
The index operation module is used for acquiring operation parameters of the system, inputting the operation parameters into the system waste wind evaluation model and outputting initial waste wind indexes;
the index operation module is also used for correcting and considering the operation parameters of the wind power heating system, inputting the corrected operation parameters into the system waste wind evaluation model and outputting corrected waste wind indexes;
and the absorption evaluation module is used for obtaining the wind power heating improvement absorption amount 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 executable on the processor, the processor when executing the computer program performing the steps of:
establishing a system abandoned wind evaluation model by using a random production simulation method;
acquiring operation parameters of a system, inputting the operation parameters into a system waste wind evaluation model, and outputting initial waste wind indexes;
correcting and considering the operation parameters of the wind power heating system, inputting the corrected operation parameters into the system waste wind evaluation model, and outputting corrected waste wind indexes;
and obtaining an enhanced digestion amount index according to the initial waste wind index and the corrected waste wind index.
A computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of:
establishing a system abandoned wind evaluation model by using a random production simulation method;
acquiring operation parameters of a system, inputting the operation parameters into a system waste wind evaluation model, and outputting initial waste wind indexes;
correcting and considering the operation parameters of the wind power heating system, inputting the corrected operation parameters into the system waste wind evaluation model, and outputting corrected waste wind indexes;
and obtaining an enhanced digestion amount index according to the initial waste wind index and the corrected waste wind index.
According to the evaluation method, the device, the computer equipment and the computer readable storage medium for improving the renewable energy consumption capability of wind power heating, the system wind abandon evaluation model is established based on the random production simulation method, the influence of wind power heating on the operation parameters of the system and the probability distribution correction function are considered, a set of evaluation method suitable for planning wind power heating for promoting the wind abandon energy consumption effect is established, wind abandon index calculation and wind power heating effect evaluation on a medium-long time scale can be performed, and effective theoretical support is provided for planning and implementation of wind power heating engineering. Meanwhile, the method solves the wind abandon index of wind power heating by utilizing the system load probability characteristic and the wind-solar power supply probability characteristic, and has the advantages of simpler data requirements and more convenient calculation process.
Drawings
FIG. 1 is a flow chart of an evaluation method for improving the capacity of wind power heating provided by an embodiment of the invention;
FIG. 2 is a flowchart of a method for 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 of a wind curtailment evaluation system of a running system after wind power heating considered according to an embodiment of the invention;
FIG. 4 is a schematic diagram of a device for improving the capacity of wind power heating provided by an embodiment of the 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 will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
As shown in fig. 1, the embodiment provides an evaluation method for improving the renewable energy source digestion capability of wind power heating, which includes the following steps:
and S10, establishing a system abandoned wind evaluation model by using a random production simulation method.
The random production simulation method is essentially characterized in that the medium-and-long-term production calculation is carried out by using the probabilistic description of the load and the set, and because the rate probability distribution of the fluctuation power supply such as wind power has certain regularity but cannot solve specific time sequence information under a long time scale, the random production simulation method has principle advantages when carrying out renewable energy power evaluation.
The system abandoned wind evaluation model is a set of evaluation method for the capability of promoting the digestion of renewable energy sources of the system, the input quantity is an operation parameter of the system, and the output quantity is an abandoned wind index.
And S20, acquiring the operation parameters of the system, inputting the operation parameters into a system waste wind evaluation model, and outputting initial waste wind indexes.
The operation parameters of the system refer to parameters of each unit participating in the operation of the system, and the parameters comprise: an electric load cumulative probability function, a thermodynamic load cumulative probability function, a conventional thermal power unit technical parameter and a wind power probability density function.
The initial waste wind index comprises initial waste wind power and initial waste wind rate.
And step S30, correcting and considering the operation parameters of the wind power heating system, inputting the corrected operation parameters into a system waste wind evaluation model, and outputting corrected waste wind indexes.
In the embodiment of the present invention, the step S10 and the step S20 are to input the operation parameters of each unit of the system into the system wind-discarding evaluation model and output the wind-discarding index under the condition of not considering wind power heating.
In step S30, the operation parameters of the system are corrected in consideration of the influence of wind power heating on the system, so as to obtain corrected operation parameters of the system, the corrected operation parameters are input into the system waste wind evaluation model, and the corrected waste wind indexes are output. The corrected waste wind index comprises corrected waste wind power and corrected waste wind rate.
And S40, obtaining the improved digestion index according to the initial waste wind index and the corrected waste wind index.
In the embodiment of the invention, the difference value between the initial waste wind index and the corrected waste wind index is calculated to obtain the enhanced digestion index, and the enhanced digestion index is used for evaluating the digestion effect of wind power heating enhanced renewable energy sources. The lifting and absorbing amount index comprises a waste wind amount lifting amount and a waste wind rate lifting amount. Specifically, the improvement amount of consumption index is calculated by the following formula:
wind curtailment amount increase = initial wind curtailment amount-corrected wind curtailment amount
Curtailment rate rise = initial curtailment rate-corrected curtailment rate
According to the evaluation method for improving the renewable energy consumption capability of wind power heating, a system waste wind evaluation model is established based on a random production simulation method, the influence of wind power heating on the operation parameters of the system and the probability distribution correction function are considered, a set of evaluation method suitable for planning of the wind power heating effect of improving waste wind power consumption is established, and the calculation of waste wind power consumption and the evaluation of wind power heating effect on the medium-long time scale can be carried out.
As shown in fig. 2, a flow chart of the refinement step of step S10 specifically includes:
Step S101, an electric load cumulative probability function and a thermodynamic load cumulative probability function are obtained according to historical operation data, and an equivalent continuous load curve and a thermodynamic load continuous curve are generated.
In an embodiment of the invention, an equivalent continuous load function (Equivalent Load Duration Curve, ELDC) and a thermodynamic load continuous function (Heat Load Duration Curve, HLDC) of all the electric loads in the research period are generated according to the historical operation data and according to a common load probability model, and an equivalent continuous load curve and a thermodynamic load continuous curve are further generated.
The electrical load and the heating load (thermal load) are obtained from the grid data.
Among the commonly used cumulative probability models are:
continuous function of load = 1-cumulative probability function of load
Specifically, the equivalent continuous load function of the electrical load = 1-the cumulative probability function of the electrical load, and the continuous load function of the thermal load = 1-the cumulative probability function of the thermal load.
And calculating to obtain an equivalent continuous load function and a thermodynamic load continuous function of the electric load according to the cumulative probability model, and 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 participate in production in sequence by utilizing an equivalent continuous load curve and the thermodynamic load continuous curve to obtain a waste wind power function; the wind curtailment power function is composed of an ELDC curve and a wind power curve.
In the embodiment of the invention, the power of a conventional thermal power generating unit, a cogeneration unit and wind power which are sequentially involved in production is constrained by using an ELDC curve and an HLDC curve, so that a waste wind 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 wind power rejection function is a relation function between an obtained wind power probability curve and an ELDC curve after the ELDC curve and the HLDC curve restrict the power of a conventional thermal power generating unit, a conventional cogeneration unit and the wind power which are sequentially involved in production.
And step S103, calculating an abandoned wind index according to the abandoned wind power function.
The wind discarding indexes comprise wind discarding electric quantity and wind discarding rate.
The wind discarding power is calculated according to the wind discarding power function according to the following formula:
wherein E is cur To discard wind power, P max For maximum value after wind power arrangement, L max F is the maximum value of the power load in the system w (x) And f (x) is a system power load cumulative probability function, and Peq is an intersection point of the ELDC curve and the wind power probability curve. Wherein, the liquid crystal display device comprises a liquid crystal display device,
F ww (Peq)＝f(Peq)
the wind rejection rate is calculated according to the following formula:
wherein k is cur To reject wind rate E cur To discard wind power, P max For maximum value after wind power arrangement, F w (x) Is a cumulative probability function of wind power.
In the embodiment of the invention, a system waste wind evaluation model is established by utilizing a random production simulation method, and the power of each unit is calculated by adopting accumulated probability, so that the waste wind electric quantity calculation of a medium-long time scale can be performed on the basis of only the probability characteristic of the system load and the probability characteristic of wind-solar power supply. Compared with the traditional time sequence solving method, the method has the advantages that the data requirements are simpler, and the calculation process is more convenient.
Optionally, in an embodiment, the restraining the power of the conventional thermal power generating unit, the cogeneration unit and the wind power sequentially participating in production in the step S102 by using the ELDC curve and the HLDC curve 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 and the wind power of the cogeneration unit below the ELDC curve.
In the embodiment of the invention, the power of a conventional thermal power generating unit, a cogeneration unit and wind power which participate in production in sequence is constrained by using an ELDC curve and an HLDC curve, and the lowest power of a non-heating thermal power generating unit, the lowest power of a heating 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:
In the first step, the lowest power of the non-heating thermal power generating unit is filled below the ELDC curve.
And calculating average coal consumption rates in base load and peak load sections of all non-heat supply units, arranging the base load and peak load sections according to ascending order, ensuring the minimum power generation coal consumption of the system, determining the arrangement order of the thermal power units participating in power generation, arranging the base load capacity (lowest power) of the non-heat supply thermal power units according to the arrangement order to participate in production, and filling the lowest power of the non-heat supply thermal power units under an ELDC curve.
And secondly, filling the lowest power of the thermal power generating unit for supplying heat under the ELDC curve.
Assuming that N non-heating units are already involved in the arrangement, for M cogeneration units, the base load parts (the lowest power) of the M cogeneration units are arranged on ELDC and HLDC to participate in the production according to the following scheme, and the lowest power of the heating thermal power unit is filled below the ELDC curve.
Wherein H is MIN Minimum thermal power H at each unit imin Sum and system thermal demand minimum
Take the maximum value between G L (H) For the system thermal load curve, F t (x) For thermoelectric units, f (x) is the probability distribution function of system electric load, G M (H) Is a continuous thermal power curve of M machine sets.
Thirdly, filling the adjustable power of the cogeneration unit under an ELDC curve, and particularly: the HLDC curve is mapped onto the ELDC curve by means of the electric heating power curve such that the adjustable power of the cogeneration unit is filled under the ELDC curve.
And arranging the adjustable capacity (adjustable power) of the cogeneration unit to participate in production, wherein the constraint condition is that the thermodynamic load of the supply system is satisfied. Where adjustable capacity (adjustable power) refers to the capacity between the most stable output to rated power.
For a cogeneration unit, assume that the ith station has heating power H i And power generation P i The following relationship exists:
wherein P is rg ,p rg G is the cumulative probability function and probability density function of the thermal power of the thermoelectric unit respectively i ,g i Respectively the function names thereof; p (P) rp ,p rp A cumulative probability function and a probability density function of the electric power of the thermoelectric unit respectively.
The electric heating power curve of the cogeneration unit can be simplified into a linear model:
wherein a is i ,b i Linear coefficients of electrothermal relationship respectively, and b i The minimum stable electric power of the heating season unit is obtained.
When the on-line and sequencing of the cogeneration units in the system are determined, the whole electric-thermal relation of the power supply side can be uniquely determined and is recorded as
C sys ＝F sys (H sys )
Wherein C is sys ，H sys The electric power and the thermal power of the system cogeneration unit are respectively the relation function F sys Is a strictly monotonically increasing function.
For the ith machine set, the values of internal thermal power and electric power interval in section j are respectively delta H and delta x, and when the adjustable part participates in production, the following electric heating power relation exists:
Wherein H is pos 、x pos The thermal and electrical load locations to which the system has been arranged, respectively.
And mapping probability values of each unit on the HLDC curve to the ELDC curve according to the electric heating power relation of the cogeneration units for each cogeneration unit, so that the adjustable power of the cogeneration units is filled below the ELDC curve.
Fourth, filling wind power under the 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 accumulation probability curve according to the following steps, and filling the wind power accumulation probability curve between the power curves and the ELDC curves of the thermal power generating unit and the cogeneration unit:
wherein f w (x) Is wind power probability density.
The four steps are sequentially executed, and the powers of the conventional thermal power generating unit, the heat and power cogeneration unit and the wind power which are sequentially involved in production, namely the lowest power of the non-heating thermal power generating unit, the lowest power of the heating thermal power generating unit, the adjustable power of the heat and power cogeneration unit and the wind power are all integrated probability distribution to participate in ELDC curve filling.
Optionally, in an embodiment, the operation parameters include an electric load cumulative probability function, a thermal load cumulative probability function, a conventional thermal power generating unit technical parameter, and an electric power probability density function, and the operation parameters of the acquiring system in the step S20 specifically include:
And obtaining the power supply and heat supply power of the cogeneration unit, and generating the electric load cumulative probability function and the thermodynamic load cumulative probability function.
In the embodiment of the invention, the electric load cumulative probability function and the thermal load cumulative probability function are generated by acquiring the power supply and the heat supply of the cogeneration unit.
And acquiring the installed capacity, the outage rate and the coal consumption as the 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 through wind resource probability distribution and fan characteristics.
In the embodiment of the invention, the wind power probability density function is obtained through synthesis of wind resource probability distribution and fan characteristics. The probability distribution of wind resources is mostly Weibull distribution:
wherein s is w The wind speed value is given in m/s; k is a shape parameter; c is a scale parameter, and reflects the average wind speed value in the statistics week.
Because the fan power generation has piecewise linear characteristics and has saturation property, the actual synthesized wind power probability density function has obvious difference with the Weibull curve.
In one embodiment, as shown in fig. 3, a flowchart of the refinement step of step S30 specifically includes:
step S301, obtaining the running power and the running time of an electric heating boiler for wind power heating.
In the embodiment of the invention, in a certain area heating period T h In the electric heating boiler, in the daily period [ t ] s ,t e ]Internal normal operation T hd For a period of hours, wind powerP ww And carrying out long-term statistics, selecting a proper power dividing point alpha, and ensuring the wind power consumption under a certain probability.
Installation capacity (operating power) C of electric heating boiler in the operating period hg Determined by the following formula:
C hg ＝P ww |p(x>P ww )＝1-α
wherein P (P) ww ) Representing the statistical probability of the wind power value.
And step S302, correcting the equivalent continuous load curve and the thermal load continuous curve by utilizing the running power and the running time to obtain a corrected electric load cumulative probability function and a corrected thermal load cumulative probability function.
In an embodiment of the invention, the ELDC curve and HLDC curve are modified using the determined operating power and operating time:
in the statistical period T h Respectively to the segmented electric load and the thermodynamic load
Generating cumulative probability curves corresponding to f respectively L1 (x),f L2 (x),G 1 (H),G 2 (H) Wherein f L1 (x),f L2 (x) Electric loads corresponding to wind power heating operation and shutdown periods respectively, G 1 (H),G 2 (H) Respectively corresponding to the system thermodynamic load during the wind power heating operation and the shutdown.
The correction of the equivalent continuous load curve and the thermodynamic load continuous curve adopts two different methods under the two conditions of normal operation and shutdown of an electric heating boiler for wind power heating, and is specifically as follows:
first kind: when the electric heating boiler for wind power heating normally operates, determining the operation power of the electric heating boiler for wind power heating; and generating an electric load cumulative probability function and a thermodynamic load cumulative probability function of the wind power heating operation and the off period in the heating period.
When the electric heating boiler for wind power heating normally operates, the total cumulative probability distribution function of the electric load and the heat load in the following statistical period is used as the sum of all the sections:
the electric heating boiler mainly has an influence on the cumulative probability curve over the second section. For HLDC curves, the modified function is:
wherein H is max Representing the original thermal load curve G 2 (H) Maximum value of thermal load, H hg Is the heat output of the electric boiler, G' 2 (H) A cumulative probability curve after the electric heating boiler is added.
Similar to the above procedure, the original ELDC curve in the second segment is f L2 (x) In the constant power mode of the electric pan boiler, the electric pan boiler is equivalent to [ t ] s ,t e ]Load value increase C at each moment in time period hg 。
For the ELDC curve, the modified function is:
second kind: when the electric heating boiler for wind power heating is stopped, the equivalent continuous load curve and the thermodynamic load curve are convolved and corrected according to the unavailability of the electric heating boiler, and a corrected electric load cumulative probability function and a corrected thermodynamic load cumulative probability function are obtained.
If the electric heating boiler is shut down for reasons of failure, maintenance, etc., the HLDC and ELDC curves of the areas will change. At this time, the thermal load cumulative probability function and the electric load cumulative probability function are corrected as follows:
defining the unavailability rate A of the electric heating boiler in the whole area:
wherein q is i For the forced outage rate of each electric heating boiler, T is the statistical period,
the overhauling shutdown time of the ith electric heating boiler.
The shutdown of the electric boiler is equivalent to the increase of the thermodynamic load and the decrease of the electric load of the cogeneration unit. The HLDC curve is convolved and corrected according to the convolution principle, and the corrected function is as follows:
when the ELDC curve is subjected to convolution difference correction,
and f' L2 (x) Has the following relationship:
modified function
Can be obtained by back-pushing calculation.
And obtaining a corrected electric load cumulative probability function and a corrected thermal load cumulative probability function which consider wind power heating by adopting the correction processing.
Step S303, inputting the corrected electric 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 waste wind evaluation model, and outputting corrected waste wind indexes.
In one embodiment, the corrected electric 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 waste wind assessment model established in the step S10, and the corrected waste wind index is output.
In the embodiment, different correction methods are adopted for normal operation and shutdown of the electric heating boiler for wind power heating, on the basis of the probability characteristics of the system load and the probability characteristics of wind-solar power sources, the influence of wind power heating on the operation parameters of the system is considered to correct the probability method, and the corrected electric load cumulative probability function and thermodynamic load cumulative probability function enable the operation parameters of the system to be more fit with an actual scene and are suitable for calculation of wind abandon indexes in medium-long time scales.
It should be understood that, although the steps in the flowcharts of fig. 1-3 are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in fig. 1-3 may include multiple sub-steps or phases that are not necessarily performed at the same time, but may be performed at different times, nor does the order in which the sub-steps or phases are performed necessarily occur sequentially, but may be performed alternately or alternately with at least a portion of the sub-steps or phases of other steps or other steps.
As shown in fig. 4, the embodiment of the invention further provides a device for improving the renewable energy source digestion capability of wind power heating, which comprises:
the model building module 100 is used for building a system waste wind assessment model by utilizing a random production simulation method;
the index operation module 200 is used for acquiring operation parameters of the system, inputting the operation parameters into the system waste wind evaluation model, and outputting initial waste wind indexes;
The index operation module 200 is further configured to modify an operation parameter of the system after wind power heating, input the modified operation parameter into the system waste wind evaluation model, and output a modified waste wind index;
and the absorption evaluation module 300 is configured to obtain a wind power heating enhancement absorption amount index according to the initial wind curtailment index and the corrected wind curtailment index.
As an alternative embodiment, the model building module 100 is specifically configured to:
obtaining an electric load cumulative probability function and a thermodynamic load cumulative probability function according to historical operation data, and generating an equivalent continuous load curve and a thermodynamic load continuous curve;
constraining the power of a conventional thermal power generating unit, a cogeneration unit and wind power which participate in production in sequence by using the equivalent continuous load curve and the thermodynamic load continuous curve to obtain a waste wind power function; the wind discarding capacity function is composed of the equivalent continuous load curve and a wind power curve;
and calculating the wind abandoning index according to the wind abandoning power function.
As an alternative embodiment, 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 and the wind power of the cogeneration unit below the equivalent continuous load curve.
As an alternative embodiment, the model building module 100 is further configured to:
mapping a thermodynamic load continuous curve onto an equivalent continuous load curve through an electrothermal power curve so that adjustable power of the cogeneration unit is 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 unit and the cogeneration unit.
As an alternative embodiment, the index running module 200 is configured to:
acquiring power supply power and heat supply power of a cogeneration unit, and generating the electric load cumulative probability function and the thermodynamic load cumulative probability function;
acquiring the installed capacity, the outage rate and the coal consumption as the technical parameters of the conventional thermal power generating unit;
and synthesizing the wind power probability density function through wind resource probability distribution and fan characteristics.
As an alternative embodiment, the index running module 200 is further configured to:
acquiring the running power and the running time of an electric heating boiler for wind power heating;
correcting the equivalent continuous load curve and the thermal load continuous curve by utilizing the operating power and the operating time to obtain a corrected electric load cumulative probability function and a corrected thermal load cumulative probability function;
And inputting the corrected electric 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 waste wind evaluation model, and outputting corrected waste wind indexes.
As an alternative embodiment, the index running module 200 is further configured to:
determining the running power of an electric heating boiler for wind power heating;
and generating an electric load cumulative probability function and a thermodynamic load cumulative probability function of the wind power heating operation and the off period in the heating period.
As an alternative embodiment, the index running module 200 is further configured to:
and convolving and correcting the equivalent continuous load curve and the thermodynamic load curve according to the unavailability rate of the electric heating boiler to obtain a corrected electric load cumulative probability function and a corrected thermodynamic load cumulative probability function.
For specific limitation of the device for improving the renewable energy source capacity of wind power heating, reference may be made to the limitation of the method for evaluating the renewable energy source capacity of wind power heating, which is not described herein. All or part of each module in the device for improving the renewable energy source absorption capacity of wind power heating can be realized by software, hardware and a combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above 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 includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer equipment is used for storing the operation parameters, the waste wind index and the improvement 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 the processor to realize an evaluation method for improving the renewable energy source digestion capability of wind power heating.
It will be appreciated by those skilled in the art that the structure shown in fig. 5 is merely a block diagram of some of the structures associated with the present application and is not limiting of the computer device to which the present application may be applied, and that a particular computer device may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided, comprising a memory and a processor, the memory having stored thereon a computer program executable on the processor, when executing the computer program, performing the steps of:
establishing a system abandoned wind evaluation model by using a random production simulation method;
acquiring operation parameters of a system, inputting the operation parameters into a system waste wind evaluation model, and outputting initial waste wind indexes;
correcting and considering the operation parameters of the wind power heating system, inputting the corrected operation parameters into the system waste wind evaluation model, and outputting corrected waste wind indexes;
and obtaining an enhanced digestion amount index according to the initial waste wind index and the corrected waste wind index.
In one embodiment, the processor when executing the computer program further performs the steps of:
obtaining an electric load cumulative probability function and a thermodynamic load cumulative probability function according to historical operation data, and generating an equivalent continuous load curve and a thermodynamic load continuous curve;
constraining the power of a conventional thermal power generating unit, a cogeneration unit and wind power which participate in production in sequence by using the equivalent continuous load curve and the thermodynamic load continuous curve to obtain a waste wind power function; the wind discarding capacity function is composed of the equivalent continuous load curve and a wind power curve;
And calculating the wind abandoning index according to the wind abandoning power 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 and the wind power of the cogeneration unit below the equivalent continuous load curve.
In one embodiment, the processor when executing the computer program further performs the steps of:
mapping a thermodynamic load continuous curve onto an equivalent continuous load curve through an electrothermal power curve so that adjustable power of the cogeneration unit is 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 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 the electric load cumulative probability function and the thermodynamic load cumulative probability function;
acquiring the installed capacity, the outage rate and the coal consumption as the technical parameters of the conventional thermal power generating unit;
And synthesizing the wind power probability density function through wind resource probability distribution and fan characteristics.
In one embodiment, the processor when executing the computer program further performs the steps of:
acquiring the running power and the running time of an electric heating boiler for wind power heating;
correcting the equivalent continuous load curve and the thermal load continuous curve by utilizing the operating power and the operating time to obtain a corrected electric load cumulative probability function and a corrected thermal load cumulative probability function;
and inputting the corrected electric 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 waste wind evaluation model, and outputting corrected waste wind indexes.
In one embodiment, the processor when executing the computer program further performs the steps of:
determining the running power of an electric heating boiler for wind power heating;
and generating an electric load cumulative probability function and a thermodynamic load cumulative probability function of the wind power heating operation and the off period in the heating period.
In one embodiment, the processor when executing the computer program further performs the steps of:
And convolving and correcting the equivalent continuous load curve and the thermodynamic load curve according to the unavailability rate of the electric heating boiler to obtain a corrected electric load cumulative probability function and a corrected thermodynamic 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 abandoned wind evaluation model by using a random production simulation method;
acquiring operation parameters of a system, inputting the operation parameters into a system waste wind evaluation model, and outputting initial waste wind indexes;
correcting and considering the operation parameters of the wind power heating system, inputting the corrected operation parameters into the system waste wind evaluation model, and outputting corrected waste wind indexes;
and obtaining an enhanced digestion amount index according to the initial waste wind index and the corrected waste wind index.
In one embodiment, the computer program when executed by the processor further performs the steps of:
obtaining an electric load cumulative probability function and a thermodynamic load cumulative probability function according to historical operation data, and generating an equivalent continuous load curve and a thermodynamic load continuous curve;
Constraining the power of a conventional thermal power generating unit, a cogeneration unit and wind power which participate in production in sequence by using the equivalent continuous load curve and the thermodynamic load continuous curve to obtain a waste wind power function; the wind discarding capacity function is composed of the equivalent continuous load curve and a wind power curve;
and calculating the wind abandoning index according to the wind abandoning power 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 and the wind power of the cogeneration unit below the equivalent continuous load curve.
In one embodiment, the computer program when executed by the processor further performs the steps of:
mapping a thermodynamic load continuous curve onto an equivalent continuous load curve through an electrothermal power curve so that adjustable power of the cogeneration unit is 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 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 the electric load cumulative probability function and the thermodynamic load cumulative probability function;
acquiring the installed capacity, the outage rate and the coal consumption as the technical parameters of the conventional thermal power generating unit;
and synthesizing the wind power probability density function through wind resource probability distribution and fan characteristics.
In one embodiment, the computer program when executed by the processor further performs the steps of:
acquiring the running power and the running time of an electric heating boiler for wind power heating;
correcting the equivalent continuous load curve and the thermal load continuous curve by utilizing the operating power and the operating time to obtain a corrected electric load cumulative probability function and a corrected thermal load cumulative probability function;
and inputting the corrected electric 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 waste wind evaluation model, and outputting corrected waste wind indexes.
In one embodiment, the computer program when executed by the processor further performs the steps of:
determining the running power of an electric heating boiler for wind power heating;
And generating an electric load cumulative probability function and a thermodynamic load cumulative probability function of the wind power heating operation and the off period in the heating period.
In one embodiment, the computer program when executed by the processor further performs the steps of:
and convolving and correcting the equivalent continuous load curve and the thermodynamic load curve according to the unavailability rate of the electric heating boiler to obtain a corrected electric load cumulative probability function and a corrected thermodynamic load cumulative probability function.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the various embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile 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), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples merely represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the invention. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application is to be determined by the claims appended hereto.
Claims (10)
1. An evaluation method for improving renewable energy source digestion capability of wind power heating is characterized by comprising the following steps:
establishing a system abandoned wind evaluation model by using a random production simulation method;
acquiring power supply power and heat supply power of a cogeneration unit, and generating an electric load cumulative probability function and a thermodynamic load cumulative probability function;
acquiring the installed capacity, the outage rate and the coal consumption as technical parameters of a conventional thermal power unit;
Based on Weibull Weibull distribution, synthesizing a wind power probability density function through wind resource probability distribution and fan characteristics, inputting operation parameters into the system abandoned wind evaluation model, and outputting initial abandoned wind indexes; the operation parameters comprise the electric load cumulative probability function, the thermodynamic load cumulative probability function, the conventional thermal power generating unit technical parameters and the wind power probability density function;
acquiring the running power and the running time of an electric heating boiler for wind power heating;
correcting an equivalent continuous load curve and a thermodynamic load continuous curve by utilizing the running power and the running time to obtain a corrected electric load cumulative probability function and a corrected thermodynamic load cumulative probability function;
inputting the corrected electric 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 waste wind evaluation model, and outputting corrected waste wind indexes;
and obtaining an improved digestion quantity index according to the initial waste wind index and the corrected waste wind index, wherein the improved digestion quantity index comprises waste wind quantity improvement quantity and waste wind rate improvement quantity.
2. The method of claim 1, wherein the creating a system wind curtailment assessment model using a random production simulation method comprises:
obtaining an electric load cumulative probability function and a thermodynamic load cumulative probability function according to historical operation data, and generating an equivalent continuous load curve and a thermodynamic load continuous curve;
constraining the power of a conventional thermal power generating unit, a cogeneration unit and wind power which participate in production in sequence by using the equivalent continuous load curve and the thermodynamic load continuous curve to obtain a waste wind power function; the wind discarding capacity function is composed of the equivalent continuous load curve and a wind power curve;
according to the wind discarding capacity function and the wind discarding rate function, calculating a wind discarding index; the waste wind indexes comprise waste wind power and waste wind rate.
3. The method of claim 2, wherein the waste-wind power is calculated based on a pre-set waste-wind power function, the waste-wind power function being:
wherein E is cur To discard wind power, P max For maximum value after wind power arrangement, L max F is the maximum value of the power load in the system ww (x) And f (x) is a system power load cumulative probability function, and Peq is an intersection point of the equivalent continuous load function curve and the wind power probability curve.
4. The method according to claim 2, wherein the constraining the power of the conventional thermal power generation unit, the cogeneration unit, and the wind power sequentially participating in the production using the equivalent continuous load curve and the thermal load continuous curve comprises:
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 and the wind power of the cogeneration unit below the equivalent continuous load curve.
5. The method of claim 4, wherein sequentially filling the adjustable power and the wind power of the cogeneration unit below the equivalent continuous load curve comprises:
mapping a thermodynamic load continuous curve onto an equivalent continuous load curve through an electrothermal power curve so that adjustable power of the cogeneration unit is 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 unit and the cogeneration unit.
6. The method of claim 1, wherein when the electric heating boiler for wind power heating is operating normally, the correcting the equivalent continuous load curve and the thermodynamic load continuous curve to obtain the corrected electric load cumulative probability function and the corrected thermodynamic load cumulative probability function includes:
Determining the running power of an electric heating boiler for wind power heating;
and generating an electric load cumulative probability function and a thermodynamic load cumulative probability function of the wind power heating operation and the off period in the heating period.
7. The method of claim 1, wherein when the electric heating boiler for wind power heating is shut down, the correcting the equivalent continuous load curve and the thermodynamic load continuous curve to obtain the corrected electric load cumulative probability function and the corrected thermodynamic load cumulative probability function comprises:
and convolving and correcting the equivalent continuous load curve and the thermodynamic load curve according to the unavailability rate of the electric heating boiler to obtain a corrected electric load cumulative probability function and a corrected thermodynamic load cumulative probability function.
8. A device for improving renewable energy source digestion capability of wind power heating, characterized in that the device comprises:
the model building module is used for building a system abandoned wind evaluation model by utilizing a random production simulation method;
the index operation module is used for obtaining the power supply and the heat supply power of the cogeneration unit and generating an electric load cumulative probability function and a thermodynamic load cumulative probability function; acquiring the installed capacity, the outage rate and the coal consumption as technical parameters of a conventional thermal power unit; based on Weibull Weibull distribution, synthesizing a wind power probability density function through wind resource probability distribution and fan characteristics, inputting operation parameters into the system abandoned wind evaluation model, and outputting initial abandoned wind indexes; the operation parameters comprise the electric load cumulative probability function, the thermodynamic load cumulative probability function, the conventional thermal power generating unit technical parameters and the wind power probability density function;
The index operation module is also used for acquiring the operation power and the operation time of an electric heating boiler used for wind power heating; correcting an equivalent continuous load curve and a thermodynamic load continuous curve by utilizing the running power and the running time to obtain a corrected electric load cumulative probability function and a corrected thermodynamic load cumulative probability function; inputting the corrected electric 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 waste wind evaluation model, and outputting corrected waste wind indexes;
the absorption evaluation module is used for obtaining an index of improving the absorption amount of wind power heating according to the initial wind abandoning index and the corrected wind abandoning index, and the index of improving the absorption amount comprises a wind abandoning amount improving amount and a wind abandoning rate improving amount.
9. 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 7 when the computer program is executed.
10. A computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the method of any of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910335035.3A CN110766259B (en) | 2019-04-24 | 2019-04-24 | Evaluation method and device for improving renewable energy source digestion capability of wind power heating |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910335035.3A CN110766259B (en) | 2019-04-24 | 2019-04-24 | Evaluation method and device for improving renewable energy source digestion capability of wind power heating |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110766259A CN110766259A (en) | 2020-02-07 |
| CN110766259B true CN110766259B (en) | 2023-06-23 |
Family
ID=69328686
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910335035.3A Active CN110766259B (en) | 2019-04-24 | 2019-04-24 | Evaluation method and device for improving renewable energy source digestion capability of wind power heating |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110766259B (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105139262A (en) * | 2014-05-30 | 2015-12-09 | 清华大学 | Power system annual wind power quantity consumptive ability calculation method |
| CN107122599A (en) * | 2017-04-14 | 2017-09-01 | 南瑞（武汉）电气设备与工程能效测评中心 | A kind of thermal storage electric boiler is dissolved in real time abandons the appraisal procedure that wind abandons optical quantum ability |
| CN107944757A (en) * | 2017-12-14 | 2018-04-20 | 上海理工大学 | Electric power interacted system regenerative resource digestion capability analysis and assessment method |
| CN108258721A (en) * | 2017-12-30 | 2018-07-06 | 国家电网公司华北分部 | Wind electricity digestion capability acquisition methods and system under a kind of unit safety constraint |
| CN109190850A (en) * | 2018-10-24 | 2019-01-11 | 国网辽宁省电力有限公司阜新供电公司 | A kind of area power grid wind electricity digestion capability appraisal procedure containing electrode boiler |
Family Cites Families (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2004309998A (en) * | 2003-02-18 | 2004-11-04 | Nec Corp | Probabilistic distribution estimation apparatus, abnormal behavior detection device, probabilistic distribution estimation method, and abnormal behavior detection method |
| US20110177844A1 (en) * | 2011-01-19 | 2011-07-21 | Lowas Iii Albert Frank | Cellular Mobile Radiotelephone Tower Wind Turbine |
| US8165812B2 (en) * | 2011-07-25 | 2012-04-24 | Clean Power Research, L.L.C. | Computer-implemented system and method for estimating power data for a photovoltaic power generation fleet |
| CN103138256B (en) * | 2011-11-30 | 2015-09-02 | 国网能源研究院 | A kind of new energy electric power reduction panorama analytic system and method |
| US9112805B2 (en) * | 2012-09-28 | 2015-08-18 | Cisco Technology, Inc. | Routing messages in a computer network using deterministic and probabilistic source routes |
| CN104283234A (en) * | 2014-07-23 | 2015-01-14 | 国家电网公司 | Wind electricity receiving ability assessment method based on large-time-scale unit maintenance |
| CN104537204B (en) * | 2014-11-07 | 2017-11-28 | 国家电网公司 | The appraisal procedure of cogeneration power network apoplexy power consumption digestion capability |
| CN106099993B (en) * | 2016-06-28 | 2018-06-08 | 中国电力工程顾问集团西南电力设计院有限公司 | A kind of power source planning method for adapting to new energy and accessing on a large scale |
| CN109586284B (en) * | 2018-11-30 | 2022-05-24 | 国家电网公司西南分部 | Random production simulation method of transmitting-end power system considering energy curtailment and application |
-
2019
- 2019-04-24 CN CN201910335035.3A patent/CN110766259B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105139262A (en) * | 2014-05-30 | 2015-12-09 | 清华大学 | Power system annual wind power quantity consumptive ability calculation method |
| CN107122599A (en) * | 2017-04-14 | 2017-09-01 | 南瑞（武汉）电气设备与工程能效测评中心 | A kind of thermal storage electric boiler is dissolved in real time abandons the appraisal procedure that wind abandons optical quantum ability |
| CN107944757A (en) * | 2017-12-14 | 2018-04-20 | 上海理工大学 | Electric power interacted system regenerative resource digestion capability analysis and assessment method |
| CN108258721A (en) * | 2017-12-30 | 2018-07-06 | 国家电网公司华北分部 | Wind electricity digestion capability acquisition methods and system under a kind of unit safety constraint |
| CN109190850A (en) * | 2018-10-24 | 2019-01-11 | 国网辽宁省电力有限公司阜新供电公司 | A kind of area power grid wind electricity digestion capability appraisal procedure containing electrode boiler |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110766259A (en) | 2020-02-07 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Bruninx et al. | Coupling pumped hydro energy storage with unit commitment | |
| Hua et al. | Representing operational flexibility in generation expansion planning through convex relaxation of unit commitment | |
| CN109472401B (en) | Method, device, equipment and storage medium for determining distributed energy supply operation strategy | |
| Karami et al. | Optimal scheduling of residential energy system including combined heat and power system and storage device | |
| CN116760122B (en) | Virtual power plant resource management and control method and device, computer equipment and storage medium | |
| CN113435659B (en) | Scene analysis-based two-stage optimized operation method and system for comprehensive energy system | |
| CN115954951A (en) | Method and device for calculating reliable output level of new energy | |
| CN110766259B (en) | Evaluation method and device for improving renewable energy source digestion capability of wind power heating | |
| CN110232202B (en) | Power generation right transaction effect evaluation method and device, computer equipment and storage medium | |
| AU2020200488A1 (en) | Operation plan creating apparatus, operation plan creating method, and program | |
| Holttinen et al. | Reduction of CO 2 emissions due to wind energy-methods and issues in estimating operational emission reductions | |
| CN110826863B (en) | Electric quantity distribution method and device, computer equipment and storage medium | |
| AU2020200492A1 (en) | Operation plan creating apparatus, operation plan creating method, and program | |
| JP2005196300A (en) | Discharge amount prediction system of greenhouse gas, program | |
| JP2022010544A (en) | Power-system management system and method | |
| Ávila et al. | Importance of hourly multi-bus unit commitment models in the context of high adoption of variable renewable energies: A chilean example | |
| Agheb et al. | Robust provisioning of demand-side flexibility under electricity price uncertainty | |
| Jaehnert et al. | On the profit variability of power plants in a system with large-scale renewable energy sources | |
| CN113689033B (en) | Method and device for checking and determining capacity generation price, computer equipment and storage medium | |
| CN108921429B (en) | Method for determining electric power of controllable energy source unit in heat/cold power-determination type intelligent micro-grid | |
| CN116523139B (en) | Electric power carbon reduction prediction method, device and storable medium | |
| CN114444915B (en) | New energy consumption cost calculation method and device for electric power system in market environment | |
| CN111156585B (en) | Energy flow control method and device for wind power heating system | |
| CN111291993B (en) | Regional power grid province and province consumption capability boundary assessment method and system | |
| Böcker et al. | Optimal capacity adjustments in electricity market models–an iterative approach based on operational margins and the relevant supply stack |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |