CN106786800B - Power system economic dispatching analysis method based on big data processing and containing photovoltaic power generation - Google Patents

Power system economic dispatching analysis method based on big data processing and containing photovoltaic power generation Download PDF

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CN106786800B
CN106786800B CN201710017269.4A CN201710017269A CN106786800B CN 106786800 B CN106786800 B CN 106786800B CN 201710017269 A CN201710017269 A CN 201710017269A CN 106786800 B CN106786800 B CN 106786800B
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CN106786800A (en
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李德存
于永军
刘大贵
孙谊媊
祁晓笑
张龙
韩华玲
陈宁
张磊
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State Grid Corp of China SGCC
China Electric Power Research Institute Co Ltd CEPRI
Electric Power Research Institute of State Grid Xinjiang Electric Power Co Ltd
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State Grid Corp of China SGCC
China Electric Power Research Institute Co Ltd CEPRI
Electric Power Research Institute of State Grid Xinjiang Electric Power Co Ltd
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/383—
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/50—Photovoltaic [PV] energy
    • Y02E10/56—Power conversion systems, e.g. maximum power point trackers

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Abstract

The invention relates to the technical field of power system economic dispatching, in particular to a photovoltaic power generation-containing power system economic dispatching analysis method based on big data processing, which comprises the steps of firstly, collecting historical data of illumination and load of a target area to obtain a fuzzy variable; secondly, solving respective fuzzy expectation according to respective measured values and fuzzy variables; thirdly, solving a correction coefficient of the fuzzy prediction quantity in the t period; fourthly, establishing a photovoltaic output prediction fuzzy variable at a t-period; fifthly, establishing credibility constraints of power balance and positive and negative rotation reserve capacity; sixthly, obtaining deterministic constraint through clear equivalence class conversion; and seventhly, solving the optimal value of the objective function by adopting an improved simplified particle swarm optimization algorithm. The method and the system can better serve the economic dispatching of the power system under the background of the energy Internet by collecting the historical data of the illumination and the load of the target area, preprocessing the data, establishing a basic fuzzy variable model of the illumination and the load and collecting and analyzing the data.

Description

Power system economic dispatching analysis method based on big data processing and containing photovoltaic power generation
Technical Field
The invention relates to the technical field of power system scheduling analysis, in particular to a photovoltaic power generation-containing power system economic scheduling analysis method based on big data processing.
Background
The new energy is clean, pollution-free and has excellent sustainable utilization performance, China speeds up the pace of developing new energy in recent years, and the development of the photovoltaic industry in China also makes great progress and occupies great proportion in the new energy industry. With the continuous improvement of the photovoltaic power generation permeability in China, the influence of the photovoltaic power generation and the load in the power grid due to uncertainty becomes obvious day by day, and new requirements are put forward for the economic dispatching of the power system. While the current technology deals with uncertainty mainly from the perspective of random variables or fuzzy variables, the uncertainty of photovoltaic output is mostly modeled directly or converted into uncertainty of prediction error. The random sampling method is adopted in the process of processing the random variable, and the generated samples have certain blindness and errors in practice. Although the prior art adopts fuzzy variables to predict the photovoltaic output, the influence of temperature on the output is treated as a constant, and the prediction result and the actual condition still have large errors under the high-temperature environment. In the operation constraint condition of the power system, most of the traditional same constraints do not or only do positive rotation reserve capacity constraint on the rotation reserve capacity, and the negative rotation reserve capacity constraint is not considered under the condition that the photovoltaic output and the load have uncertainty.
Disclosure of Invention
The invention provides a photovoltaic power generation-containing power system economic dispatching analysis method based on big data processing, overcomes the defects of the prior art, and can effectively solve the problem that the dispatching method of the power system is not economical under the background of energy interconnection caused by the fact that the output of a thermal power generating unit cannot be reasonably distributed under the constraint condition of containing photovoltaic power generation of the power system.
The technical scheme of the invention is realized by the following measures: the power system economic dispatching analysis method based on big data processing and containing photovoltaic power generation comprises the following steps:
collecting historical data of illumination and load of a target area, and simulating respective basic fuzzy variables through inter-partition statistics;
secondly, acquiring measured values of illumination, load and temperature in a t-1 time period, and respectively solving respective fuzzy expectation according to fuzzy variables;
thirdly, obtaining a correction coefficient of the fuzzy prediction quantity in the t-1 time period by making a quotient of the measured value in the t-1 time period and the expected value of the corresponding fuzzy variable in the t-1 time period;
fourthly, correcting basic fuzzy variables of the t time period by adopting a correction coefficient of fuzzy prediction quantity of the t time period, combining the temperature measured value acquired in the t-1 time period with the predicted fuzzy variables of illumination of the t time period, and establishing photovoltaic output and photovoltaic negative output of the t time period
A predicted fuzzy variable model of the load; the method for establishing the predicted fuzzy variable corresponding to the corrected t time period comprises the following steps:
①, basic fuzzy variables of illumination and load in a t period are simulated:
Figure GDA0002391485440000011
Figure GDA0002391485440000012
wherein the content of the first and second substances,
Figure GDA0002391485440000013
and
Figure GDA0002391485440000014
respectively illumination and negativeParameters of basic trapezoidal fuzzy variables of load;
②, collecting the illumination and load actual measurement data in the t-1 time period, calculating the correction coefficient by taking the illumination and load actual measurement data in the t-1 time period and the expected value of the basic fuzzy variable in the t-1 time period as a quotient, and finally obtaining the predicted fuzzy variable in the t time period, wherein the formula is as follows:
Figure GDA0002391485440000015
Figure GDA0002391485440000021
Figure GDA0002391485440000022
Figure GDA0002391485440000023
Figure GDA0002391485440000024
Figure GDA0002391485440000025
wherein, K1(t)、K2(t) is the correction coefficient of the fuzzy variable of the illumination and the load in the period t,
Figure GDA0002391485440000026
forecasting fuzzy variables of light and load in a t period respectively;
Figure GDA0002391485440000027
the basic fuzzy variable expectation value of the solar irradiance in the t-1 period;
Figure GDA0002391485440000028
the expected value of a basic fuzzy variable of the load in the t-1 period; srea(t-1) actual illumination for a period of t-1Strength;
Figure GDA0002391485440000029
actual power of the load for a period t-1;
③, solving a prediction fuzzy variable of the photovoltaic output in the t period, wherein the formula is as follows:
Figure GDA00023914854400000210
Tc＝Ta+ζ·Srea(t-1) (10)
wherein
Figure GDA00023914854400000211
The prediction fuzzy variable is a photovoltaic output t time period; srea(t-1) actual illumination intensity in t-1 time period, SrefIs standard illumination intensity, η is photoelectric conversion efficiency of photovoltaic power generation system, k is peak power temperature coefficient value, T iscIs the actual operating temperature, T, of the photovoltaic cellrefIs the standard cell temperature, TaIs ambient temperature; zeta is the illumination temperature coefficient;
fifthly, in an application stage, establishing credibility constraints of power balance and positive and negative rotation reserve capacity based on the photovoltaic output and load prediction fuzzy variable model established in the fourth step; based on a prediction fuzzy model of photovoltaic output and load, constraint conditions are established from the aspects of a power balance equation and positive and negative rotation reserve capacity, and the method specifically comprises the following steps:
① an objective function is established containing the penalty cost of the environment:
Figure GDA00023914854400000212
f (t) is the total output economic cost of the thermal power generating unit at the moment t, ai、bi、ciFor coal consumption washing of thermal power unit i, PG,i(t) is the output of the thermal power generating unit i at the moment t, N is the total number of the thermal power generating units, HEPSystem for punishing economic cost for environmental pollutionNumber αi、βi、χiThe method comprises the following steps of (1) obtaining a unit output waste gas emission coefficient of a thermal power generating unit i;
② establishing credibility constraints under fuzzy variables:
Figure GDA00023914854400000213
Figure GDA00023914854400000214
Figure GDA00023914854400000215
wherein, Cr is the credibility; n is a radical of1And N2The number of the thermal power generating units and the number of the photovoltaic power stations are respectively;
Figure GDA00023914854400000216
predicting a fuzzy variable of output for the photovoltaic power station j in the t period;
Figure GDA00023914854400000217
as defined in formula (8); ploss(t) is the system network loss in the period t; ru(t) and Rd(t) providing positive and negative rotation standby for all thermal power generating units in the period t respectively, α, β1And β2Confidence levels of power balance and positive and negative rotation reserve capacity constraints are respectively set;
sixthly, obtaining certainty constraint by clear equivalence class conversion on the credibility constraint in the fifth step; in the sixth step, the clear equivalence class is used for carrying out deterministic conversion on the credibility constraint, and the formula is as follows:
Figure GDA0002391485440000031
Figure GDA0002391485440000032
Figure GDA0002391485440000033
wherein L is+And W+Respectively positive and standby demand coefficients of the system for load and photovoltaic output; l is-And W-Negative standby demand coefficients of the system for load and photovoltaic output are respectively;
Figure GDA0002391485440000034
respectively referring to the 3 rd prediction and 4 th prediction fuzzy variables of the load in the t period,
Figure GDA0002391485440000035
fuzzy variables of predicted output of the photovoltaic power station j at the 1 st time, the 2 nd time, the 3 rd time and the 4 th time in the t period are respectively referred to;
and seventhly, solving the optimal value of the objective function containing the penalty cost of environmental pollution by adopting an improved simplified particle swarm optimization algorithm.
The following is further optimization or/and improvement of the technical scheme of the invention:
in the first step, the basic fuzzy variables of each part are simulated through inter-partition statistics, and the method comprises the following steps:
① collecting the illumination and load data of the target area sampled for many times in the past year, wherein the illumination selects the large-span history data, and the load selects the data of the last 4 years;
② analyzing and preprocessing the data to eliminate bad database.
The method comprises the steps of collecting historical data of illumination and load of a target area, preprocessing the data, and establishing basic fuzzy variables of the illumination and the load; collecting values of illumination, load and temperature in the last time period, and obtaining correction coefficients by utilizing the illumination, the load and respective basic fuzzy variables; obtaining respective corrected fuzzy variables of the time period by means of the correction coefficients; establishing a prediction fuzzy variable model of photovoltaic output by combining temperature data and a prediction fuzzy variable of illumination; the environment penalty cost is included in the objective function, credibility constraint considering the positive and negative rotation reserve capacity is established in the constraint condition, the fuzzy constraint is clarified by adopting a clear equivalence class, and an optimal value is solved by using an improved simplified particle swarm optimization algorithm. The method is used as a processing method for electric energy distribution, better serves the economic dispatching of the electric power system under the background of energy Internet through data collection and analysis, and realizes reasonable distribution of the electric power system to the output of the thermal power unit under the operation constraint condition by carrying out fuzzy modeling aiming at the uncertainty of photovoltaic output and load under different time periods, so that a target model obtains an optimal value, and a method reference is provided for the economic operation of the electric power system under the background of energy interconnection.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
FIG. 2 is a schematic diagram of the system operation cost under different confidence levels in embodiment 2 of the present invention.
FIG. 3 is a schematic diagram of the system's positive rotational reserve capacity at a confidence level of 0.65/0.9 for example 2 of the present invention.
FIG. 4 is a graphical comparison of the system cost curves at a confidence level of 0.95 for example 2 of the present invention.
FIG. 5 is a schematic diagram of the system cost difference of example 2 of the present invention with or without considering temperature at a confidence level of 0.95.
Detailed Description
The present invention is not limited by the following examples, and specific embodiments may be determined according to the technical solutions and practical situations of the present invention.
The invention is further described with reference to the following examples and figures:
example 1: as shown in fig. 1, a method for economic dispatch analysis of a power system including photovoltaic power generation based on big data processing comprises the following steps:
collecting historical data of illumination and load of a target area, and simulating respective basic fuzzy variables through inter-partition statistics;
secondly, acquiring measured values of illumination, load and temperature in a t-1 time period, and respectively solving respective fuzzy expectation according to fuzzy variables;
thirdly, obtaining a correction coefficient of the fuzzy prediction quantity in the t-1 time period by making a quotient of the measured value in the t-1 time period and the expected value of the corresponding fuzzy variable in the t-1 time period;
fourthly, correcting a basic fuzzy variable of the t time period by adopting a correction coefficient of the fuzzy prediction quantity of the t time period, combining the temperature measured value acquired in the t-1 time period with a prediction fuzzy variable of illumination of the t time period, and establishing a prediction fuzzy variable model of photovoltaic output and load of the t time period; the method for establishing the predicted fuzzy variable corresponding to the corrected t time period comprises the following steps:
①, basic fuzzy variables of illumination and load in a t period are simulated:
Figure GDA0002391485440000041
Figure GDA0002391485440000042
wherein the content of the first and second substances,
Figure GDA0002391485440000043
and
Figure GDA0002391485440000044
parameters of basic trapezoidal fuzzy variables of illumination and load respectively;
②, collecting the illumination and load actual measurement data in the t-1 time period, calculating the correction coefficient by taking the illumination and load actual measurement data in the t-1 time period and the expected value of the basic fuzzy variable in the t-1 time period as a quotient, and finally obtaining the predicted fuzzy variable in the t time period, wherein the formula is as follows:
Figure GDA0002391485440000045
Figure GDA0002391485440000046
Figure GDA0002391485440000047
Figure GDA0002391485440000048
Figure GDA0002391485440000049
Figure GDA00023914854400000410
wherein, K1(t)、K2(t) is the correction coefficient of the fuzzy variable of the illumination and the load in the period t,
Figure GDA00023914854400000411
forecasting fuzzy variables of light and load in a t period respectively;
Figure GDA00023914854400000412
the basic fuzzy variable expectation value of the solar irradiance in the t-1 period;
Figure GDA00023914854400000413
the expected value of a basic fuzzy variable of the load in the t-1 period; srea(t-1) actual illumination intensity at the time period of t-1;
Figure GDA00023914854400000414
actual power of the load for a period t-1;
③, solving a prediction fuzzy variable of the photovoltaic output in the t period, wherein the formula is as follows:
Figure GDA00023914854400000415
Tc＝Ta+ζ·Srea(t-1) (10)
wherein
Figure GDA00023914854400000416
The prediction fuzzy variable is a photovoltaic output t time period; srea(t-1) is the t-1 periodIntensity of illumination, SrefIs standard illumination intensity, η is photoelectric conversion efficiency of photovoltaic power generation system, k is peak power temperature coefficient value, T iscIs the actual operating temperature, T, of the photovoltaic cellrefIs the standard cell temperature, TaIs ambient temperature; zeta is the illumination temperature coefficient;
fifthly, in an application stage, establishing credibility constraints of power balance and positive and negative rotation reserve capacity based on the photovoltaic output and load prediction fuzzy variable model established in the fourth step; based on a prediction fuzzy model of photovoltaic output and load, constraint conditions are established from the aspects of a power balance equation and positive and negative rotation reserve capacity, and the method specifically comprises the following steps:
① an objective function is established containing the penalty cost of the environment:
Figure GDA0002391485440000051
f (t) is the total output economic cost of the thermal power generating unit at the moment t, ai、bi、ciFor coal consumption washing of thermal power unit i, PG,i(t) is the output of the thermal power generating unit i at the moment t, N is the total number of the thermal power generating units, HEPPenalizing an economic cost coefficient for environmental pollution, αi、βi、χiThe method comprises the following steps of (1) obtaining a unit output waste gas emission coefficient of a thermal power generating unit i;
② establishing credibility constraints under fuzzy variables:
Figure GDA0002391485440000052
Figure GDA0002391485440000053
Figure GDA0002391485440000054
wherein, Cr is the credibility; n is a radical of1And N2The number of the thermal power generating units and the number of the photovoltaic power stations are respectively;
Figure GDA0002391485440000055
predicting a fuzzy variable of output for the photovoltaic power station j in the t period;
Figure GDA0002391485440000056
as defined in formula (8); ploss(t) is the system network loss in the period t; ru(t) and Rd(t) providing positive and negative rotation standby for all thermal power generating units in the period t respectively, α, β1And β2Confidence levels of power balance and positive and negative rotation reserve capacity constraints are respectively set;
sixthly, obtaining certainty constraint by clear equivalence class conversion on the credibility constraint in the fifth step; in the sixth step, the clear equivalence class is used for carrying out deterministic conversion on the credibility constraint, and the formula is as follows:
Figure GDA0002391485440000057
Figure GDA0002391485440000058
Figure GDA0002391485440000059
wherein L is+And W+Respectively positive and standby demand coefficients of the system for load and photovoltaic output; l is-And W-Negative standby demand coefficients of the system for load and photovoltaic output are respectively;
Figure GDA00023914854400000510
respectively referring to the 3 rd prediction and 4 th prediction fuzzy variables of the load in the t period,
Figure GDA00023914854400000511
fuzzy variables of predicted output of the photovoltaic power station j at the 1 st time, the 2 nd time, the 3 rd time and the 4 th time in the t period are respectively referred to;
and seventhly, solving the optimal value of the objective function containing the penalty cost of environmental pollution by adopting an improved simplified particle swarm optimization algorithm.
The method comprises the steps of collecting historical data of illumination and load of a target area, preprocessing the data, and establishing basic fuzzy variables of the illumination and the load; collecting values of illumination, load and temperature in the last time period, and obtaining correction coefficients by utilizing the illumination, the load and respective basic fuzzy variables; obtaining respective corrected fuzzy variables of the time period by means of the correction coefficients; establishing a prediction fuzzy variable model of photovoltaic output by combining temperature data and a prediction fuzzy variable of illumination; the environment penalty cost is included in the objective function, credibility constraint considering the positive and negative rotation reserve capacity is established in the constraint condition, the fuzzy constraint is clarified by adopting a clear equivalence class, and an optimal value is solved by using an improved simplified particle swarm optimization algorithm. The method is used as a processing method for electric energy distribution, better serves the economic dispatching of the electric power system under the background of energy Internet through data collection and analysis, and realizes reasonable distribution of the electric power system to the output of the thermal power unit under the operation constraint condition by carrying out fuzzy modeling aiming at the uncertainty of photovoltaic output and load under different time periods, so that a target model obtains an optimal value, and a method reference is provided for the economic operation of the electric power system under the background of energy interconnection.
The economic dispatching analysis method of the power system based on big data processing and containing photovoltaic power generation can be further optimized or/and improved according to actual needs:
as shown in fig. 1, in the first step, the step of modeling the basic fuzzy variables by inter-partition statistics includes the following steps:
① collecting the illumination and load data of the target area sampled for many times in the past year, wherein the illumination selects the large-span history data, and the load selects the data of the last 4 years;
② analyzing and preprocessing the data to eliminate bad database.
When the illumination data is selected, large-span historical data is selected for ensuring accurate illumination, and the data of the last 4 years is selected in a load mode; and simulating fuzzy parameters corresponding to each interval through interval statistics, and finally establishing a basic fuzzy variable.
Example 2: as shown in fig. 1,2, 3, 4, and 5 and tables 1,2, 3, and 4, a method for analyzing economic dispatch of a power system including photovoltaic power generation based on big data processing is described by way of example, 1 photovoltaic power station with installed capacity of 100MW, 6 thermal power generating units, and 24-period dispatch cycle, and includes the following steps:
the method comprises the steps of firstly, collecting historical data of illumination and load of a certain area, wherein each data is sampled 1000 times in the same time period; selecting data of nearly 10 years by illumination, selecting data of nearly 3 years by load, and simulating a basic fuzzy variable by regional modeling after removing the data by bad and rejecting; each time interval 1000 groups of illumination and load data are expressed by vectors as follows
Illumination of S ═ S (S)1,s2,s3,...,st-1,st,...,s999,s1000) (18)
Load P ═ P1,p2,p3,...,pt-1,pt,...,p999,p1000) (19)
And secondly, based on the established basic fuzzy variable, utilizing the measured values of the illumination, the load and the temperature collected in the previous time period to obtain a correction coefficient, and correcting the basic fuzzy variable model of the illumination and the load and the prediction fuzzy variable model of the photovoltaic output in the current time period by using the correction coefficient, wherein the correction coefficient specifically comprises the following steps:
① collecting basic fuzzy variables of light and load in t-1 and t periods
Figure GDA0002391485440000061
Figure GDA0002391485440000062
Figure GDA0002391485440000063
Figure GDA0002391485440000064
Wherein the content of the first and second substances,
Figure GDA0002391485440000065
basic trapezoidal fuzzy variable parameters of illumination in t-1 time period;
Figure GDA0002391485440000066
a basic trapezoidal fuzzy variable parameter of illumination in a time period t;
Figure GDA0002391485440000067
basic trapezoidal fuzzy variable parameters of load in t-1 time period;
Figure GDA0002391485440000068
basic trapezoidal fuzzy variable parameters of load in t period;
Figure GDA0002391485440000069
basic trapezoidal fuzzy variables of illumination and load in t-1 and t periods;
② calculating basic fuzzy variable expectation of light and load in t-1 period:
Figure GDA00023914854400000610
Figure GDA00023914854400000611
wherein
Figure GDA0002391485440000071
Basic fuzzy expected values of light and load in a t-1 time period respectively;
③ calculating t-period correction coefficient
Figure GDA0002391485440000072
Figure GDA0002391485440000073
Wherein K1(t)、K2(t) the correction coefficient of the fuzzy variables of the illumination and the load in the t time period is obtained, and the prediction fuzzy variables of the illumination and the load in the t time period are obtained;
④ correcting basic fuzzy variables of light and load in t period to obtain respective prediction fuzzy variable models
Figure GDA0002391485440000074
Figure GDA0002391485440000075
Wherein
Figure GDA0002391485440000076
Predicting fuzzy variables of the corrected illumination and load in the t period;
⑤ collecting temperature and illumination measured values in t-1 time period, and calculating photovoltaic output prediction fuzzy variable
Figure GDA0002391485440000077
Tc＝Ta+ζ·Srea(t-1) (31)
Wherein
Figure GDA0002391485440000078
The prediction fuzzy variable is a photovoltaic output t time period; srea(t-1) actual illumination intensity in t-1 time period, SrefIs standard illumination intensity, η is photoelectric conversion efficiency of photovoltaic power generation system, k is peak power temperature coefficient value, T iscIs the actual operating temperature, T, of the photovoltaic cellrefIs the standard cell temperature, TaIs ambient temperature; zeta is illuminationA temperature coefficient;
Figure GDA0002391485440000079
the fuzzy predicted value of the illumination intensity is obtained;
thirdly, establishing a target function and constraint conditions based on the photovoltaic output and load prediction fuzzy variable model established in the second step:
① objective function
Figure GDA00023914854400000710
The target function considers the environmental penalty cost besides the fuel consumption cost of the thermal power generating unit, wherein the value of T is 1,2. CminIs an objective function, i.e. the total minimum output economic cost of the system; f (t) is the total output economic cost of the thermal power generating unit at the moment t; pG,i(t) the output of the thermal power generating unit i at the moment t; a isi、bi、ciThe unit output coal consumption coefficient is the unit output coal consumption coefficient of the thermal power generating unit i; hEPPenalizing economic cost coefficient for environmental pollution αi、βi、γiThe method comprises the following steps of (1) obtaining a unit output waste gas emission coefficient of a thermal power generating unit i;
②, system power balance (formula (33)), thermal power unit output (formula (34)), photovoltaic output (formula (35), thermal power unit climbing (formula (36), (37), (38)), and system positive and negative rotation reserve capacity (formula (39), (40), (41), (42)) constraints are established, and the following formulas are adopted:
Figure GDA00023914854400000711
Figure GDA00023914854400000712
Figure GDA00023914854400000713
ΔPG,i(t)＝ΔT·δG,i(36)
Figure GDA0002391485440000081
Figure GDA0002391485440000082
Figure GDA0002391485440000083
Figure GDA0002391485440000084
Figure GDA0002391485440000085
Figure GDA0002391485440000086
in the above formula PPV,j(t) is the output of the photovoltaic power station j at the moment t; plossα is confidence level of credibility of power balance constraint;
Figure GDA0002391485440000087
and
Figure GDA0002391485440000088
respectively setting the lower limit and the upper limit of the output of the ith thermal power generating unit;
Figure GDA0002391485440000089
the installed capacity of the photovoltaic power station j; deltaG,iThe value is the upper limit value and the lower limit value of the slope climbing rate of the thermal power generating unit i; pG,i(t) the output of the thermal power generating unit i at the moment t; Δ T is the time interval; ru(t) and Rd(t) the total positive and negative rotation of the thermal power generating unit is reserved at a time period t; ru.i(t) and Rd,i(t) the positive and negative rotation reserve capacity of the unit i in the period t; l is+And W+Respectively system to load and photovoltaic outputA positive standby demand coefficient; l is-And W-Respectively negative standby demand coefficient of system to load and photovoltaic output, change α and β1、β2The value of (a) will result in the power system environmental economic dispatch cost under different constraint levels;
③, performing deterministic transformation on the credibility constraint containing the fuzzy variables by using a clear equivalence class method:
Figure GDA00023914854400000810
Figure GDA00023914854400000811
Figure GDA00023914854400000812
the optimal problem of fuzzy credibility constraint is solved, and the key point is to process credibility constraint. One method is fuzzy simulation, which utilizes computer random sampling inspection to obtain credibility of credibility constraint according to a law of large numbers and finally judges the quality of a decision variable value, but a simulation result is only statistical estimation, and is time-consuming and inaccurate. The invention converts credibility constraints into clear equivalence classes and then solves the model. The credibility constraint with the fuzzy variable can be converted into a general inequality to be calculated by adopting a clear equivalence method, so that the fuzzy variable has calculability.
④ solving an optimal solution of the optimization problem by using a simplified particle swarm optimization algorithm, which is represented by the following equations (33) to (36) and (39) to (40), (43) to (45):
Figure GDA00023914854400000813
wherein d is the dimension of the particle vector;
Figure GDA00023914854400000814
and
Figure GDA00023914854400000815
the positions of d-dimensional particles i in the time periods t and t +1, respectively; x is the number ofi,dThe position of the ith particle in the d dimension; omega is a power constant; p is a radical ofi,dThe historical optimal point of the ith particle is taken; p is a radical ofg,dThe current optimal position of the particle swarm is taken as the current optimal position; c. C1、c2Is an acceleration factor; r is1，r2Is [0,1 ]]Random numbers are uniformly distributed in the inner part; x is the number ofi,d∈[-xmax,xmax]. Each particle according to its own optimal solution pi,dAnd a global optimal solution pg,dAnd obtaining the speed and the optimal position of the t +1 th iteration. As shown in tables 1,2, 3, and 4, the calculation results are considered from the system operation costs at different constraint levels, the forward rotation reserve capacity of the system at different constraint levels, the comparison of two cost curves of the system at a certain constraint level, and the system cost difference whether the temperature is considered at a certain constraint level, and the particle swarm optimization algorithm has the basic idea of finding the optimal solution through cooperation and information sharing among individuals in a group. The particle swarm algorithm has the advantages of simplicity, easiness in implementation, no adjustment of many parameters, high searching speed and high efficiency, and is suitable for real-valued processing.
The technical characteristics form an embodiment of the invention, which has strong adaptability and implementation effect, and unnecessary technical characteristics can be increased or decreased according to actual needs to meet the requirements of different situations.
TABLE 1 System operating costs at different confidence levels
Figure GDA0002391485440000101
TABLE 20.65/0.9 confidence level System Forward rotation reserve Capacity
Figure GDA0002391485440000102
TABLE 30.95 confidence level comparison of System costs
Figure GDA0002391485440000103
TABLE 40.95 confidence level whether the system accounts for temperature factor cost differential
Time period 1～6 7 8 9 10 11 12 13
Cost difference/yuan 0 16 3 30 118 497 482 670
Time period 14 15 16 17 18 19 20 21～24
Cost difference/yuan 362 200 80 39 -7 0 0 0

Claims (2)

1. A photovoltaic power generation-containing power system economic dispatching analysis method based on big data processing is characterized by comprising the following steps:
collecting historical data of illumination and load of a target area, and simulating respective basic fuzzy variables through inter-partition statistics;
secondly, acquiring measured values of illumination, load and temperature in a t-1 time period, and respectively solving respective fuzzy expectation according to fuzzy variables;
thirdly, obtaining a correction coefficient of the fuzzy prediction quantity in the t-1 time period by making a quotient of the measured value in the t-1 time period and the expected value of the corresponding fuzzy variable in the t-1 time period;
fourthly, correcting a basic fuzzy variable of the t time period by adopting a correction coefficient of the fuzzy prediction quantity of the t time period, combining the temperature measured value acquired in the t-1 time period with a prediction fuzzy variable of illumination of the t time period, and establishing a prediction fuzzy variable model of photovoltaic output and load of the t time period; the method for establishing the predicted fuzzy variable corresponding to the corrected t time period comprises the following steps:
①, basic fuzzy variables of illumination and load in a t period are simulated:
Figure FDA0002391485430000011
Figure FDA0002391485430000012
wherein the content of the first and second substances,
Figure FDA0002391485430000013
and
Figure FDA0002391485430000014
parameters of basic trapezoidal fuzzy variables of illumination and load respectively;
②, collecting the illumination and load actual measurement data in the t-1 time period, calculating the correction coefficient by taking the illumination and load actual measurement data in the t-1 time period and the expected value of the basic fuzzy variable in the t-1 time period as a quotient, and finally obtaining the predicted fuzzy variable in the t time period, wherein the formula is as follows:
Figure FDA0002391485430000015
Figure FDA0002391485430000016
Figure FDA0002391485430000017
Figure FDA0002391485430000018
Figure FDA0002391485430000019
Figure FDA00023914854300000110
wherein, K1(t)、K2(t) is the correction coefficient of the fuzzy variable of the illumination and the load in the period t,
Figure FDA00023914854300000111
forecasting fuzzy variables of light and load in a t period respectively;
Figure FDA00023914854300000112
the basic fuzzy variable expectation value of the solar irradiance in the t-1 period;
Figure FDA00023914854300000113
the expected value of a basic fuzzy variable of the load in the t-1 period; srea(t-1) actual illumination intensity at the time period of t-1;
Figure FDA00023914854300000114
actual power of the load for a period t-1;
③, solving a prediction fuzzy variable of the photovoltaic output in the t period, wherein the formula is as follows:
Figure FDA00023914854300000115
Tc＝Ta+ζ·Srea(t-1) (10)
wherein
Figure FDA00023914854300000116
The prediction fuzzy variable is a photovoltaic output t time period; srea(t-1) actual illumination intensity in t-1 time period, SrefIs standard illumination intensity, η is photoelectric conversion efficiency of photovoltaic power generation system, k is peak power temperature coefficient value, T iscIs the actual operating temperature, T, of the photovoltaic cellrefIs the standard cell temperature, TaIs ambient temperature; zeta is the illumination temperature coefficient;
fifthly, in an application stage, establishing credibility constraints of power balance and positive and negative rotation reserve capacity based on the photovoltaic output and load prediction fuzzy variable model established in the fourth step; based on a prediction fuzzy model of photovoltaic output and load, constraint conditions are established from the aspects of a power balance equation and positive and negative rotation reserve capacity, and the method specifically comprises the following steps:
① an objective function is established containing the penalty cost of the environment:
Figure FDA0002391485430000021
f (t) is the total output economic cost of the thermal power generating unit at the moment t, ai、bi、ciFor coal consumption washing of thermal power unit i, PG,i(t) is the output of the thermal power generating unit i at the moment t, N is the total number of the thermal power generating units, HEPPenalizing an economic cost coefficient for environmental pollution, αi、βi、χiThe method comprises the following steps of (1) obtaining a unit output waste gas emission coefficient of a thermal power generating unit i;
② establishing credibility constraints under fuzzy variables:
Figure FDA0002391485430000022
Figure FDA0002391485430000023
Figure FDA0002391485430000024
wherein, Cr is the credibility; n is a radical of1And N2The number of the thermal power generating units and the number of the photovoltaic power stations are respectively;
Figure FDA0002391485430000025
predicting a fuzzy variable of output for the photovoltaic power station j in the t period;
Figure FDA0002391485430000026
as defined in formula (8); ploss(t) is the system network loss in the period t; ru(t) and Rd(t) providing positive and negative rotation standby for all thermal power generating units in the period t respectively, α, β1And β2Confidence levels of power balance and positive and negative rotation reserve capacity constraints are respectively set;
sixthly, obtaining certainty constraint by clear equivalence class conversion on the credibility constraint in the fifth step; in the sixth step, the clear equivalence class is used for carrying out deterministic conversion on the credibility constraint, and the formula is as follows:
Figure FDA0002391485430000027
Figure FDA0002391485430000028
Figure FDA0002391485430000029
wherein L is+And W+Respectively positive and standby demand coefficients of the system for load and photovoltaic output; l is-And W-Negative standby demand coefficients of the system for load and photovoltaic output are respectively;
Figure FDA00023914854300000210
respectively referring to the 3 rd prediction and 4 th prediction fuzzy variables of the load in the t period,
Figure FDA00023914854300000211
fuzzy variables of predicted output of the photovoltaic power station j at the 1 st time, the 2 nd time, the 3 rd time and the 4 th time in the t period are respectively referred to;
and seventhly, solving the optimal value of the objective function containing the penalty cost of environmental pollution by adopting an improved simplified particle swarm optimization algorithm.
2. The economic dispatch analysis method for a power system including photovoltaic power generation based on big data processing as claimed in claim 1, characterized in that in the first step, the respective basic fuzzy variables are simulated by inter-partition statistics, comprising the steps of:
① collecting the year-round illumination and load data of the target area sampled many times, the illumination selecting large-span history data, the load selecting the latest 4 years data;
② analyzing and preprocessing the data to eliminate bad database.
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