CN112927098A - Power grid economic dispatching comprehensive evaluation method considering source load uncertainty - Google Patents

Power grid economic dispatching comprehensive evaluation method considering source load uncertainty Download PDF

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CN112927098A
CN112927098A CN202110130454.0A CN202110130454A CN112927098A CN 112927098 A CN112927098 A CN 112927098A CN 202110130454 A CN202110130454 A CN 202110130454A CN 112927098 A CN112927098 A CN 112927098A
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张文军
胡浩
易善军
韩玉辉
宋昆
尹洪全
朱宏涛
周攀
李海琛
明德才
张晓志
陈双
刘蒙聪
刘绎高
刘文胜
尚国政
戴幸涛
张钧贺
王祎晨
史航
周宁
顾晓川
赵嵩
赵鹏宇
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Shenyang University of Technology
East Inner Mongolia Electric Power Co Ltd
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East Inner Mongolia Electric Power Co Ltd
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Abstract

The invention relates to the technical field of power system scheduling, and provides a comprehensive evaluation method for power grid economic scheduling considering source load uncertainty, which comprises the following steps: step 1: establishing a source side uncertainty calculation model: calculating the actual output of the fan considering the uncertainty of the fan and the actual photovoltaic output considering the uncertainty of the photovoltaic; step 2: establishing a load side uncertainty calculation model: respectively calculating an electric load actual value, an air load actual value and a heat load actual value which take the electric load uncertainty, the air load uncertainty and the heat load uncertainty into consideration; and step 3: calculating the comprehensive index of the economic dispatching of the power grid: calculating a power grid economic dispatching reliability index (power grid average power supply unavailability), an economic index and an schedulability index (regulation accuracy rate) considering source load uncertainty, and calculating a power grid economic dispatching comprehensive index considering source load uncertainty; and 4, step 4: and carrying out comprehensive evaluation on economic dispatching of the power grid. The method and the device can improve the accuracy of economic dispatching evaluation of the power grid.

Description

Power grid economic dispatching comprehensive evaluation method considering source load uncertainty
Technical Field
The invention relates to the technical field of power system scheduling, in particular to a comprehensive evaluation method for power grid economic scheduling considering source load uncertainty.
Background
With the increasing of the penetration ratio of renewable energy sources, the intermittency and the volatility of the source side and the prediction difficulty of the load side are increased. When the source load prediction error is too large, the power grid dispatching is greatly influenced, in order to improve the system operation reliability and improve the operation economy, the relation between the prediction error caused by the source load uncertainty and the power grid economic dispatching needs to be fully considered, and a reasonable basis is provided for improving the economic operation.
Firstly, in the calculation method for the economic dispatching of the power grid, all factors related to the economy in the dispatching of the power grid are partially considered for analysis, secondly, the investment fund in the system and the consumption cost in the running process of the system are calculated in a related mode, and finally, the economic benefit generated in the dispatching process is analyzed. The economic dispatching calculation of the power grid aims at the economic optimization, and the single economic calculation cannot guarantee the requirements on the reliability and the schedulability of the power grid in the dispatching process.
In the calculation method for carrying out the economic dispatching of the power grid by considering the prediction error, the uncertainty is partially expressed according to a fuzzy theory, but the selection of the ambiguity is limited by artificial subjectivity, so that inevitable deviation can be caused, the subjective deviation can cause inaccurate calculation of a power supply end of the power grid, and certain deviation can be caused after the economic dispatching of the power grid is combined.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides the comprehensive evaluation method for the economic dispatching of the power grid, which considers the uncertainty of the source load, can improve the accuracy of the economic dispatching evaluation of the power grid, and is comprehensive and objective.
The technical scheme of the invention is as follows:
a comprehensive assessment method for power grid economic dispatching considering source load uncertainty is characterized by comprising the following steps: the method comprises the following steps:
step 1: establishing a source side uncertainty calculation model
Step 1.1: output calculation taking into account fan uncertainty
The actual output of the fan considering the uncertainty of the fan at the moment t is calculated as
Figure BDA0002924966220000011
In the formula (1), the reaction mixture is,
Figure BDA0002924966220000012
the predicted value of the fan output at the moment t,
Figure BDA0002924966220000013
predicting an error value for the output of the fan at the time t;
Figure BDA0002924966220000014
Figure BDA0002924966220000021
in the formula (2), λcIs the electromechanical conversion coefficient of the fan, CpIs the wind energy utilization coefficient of the fan, rho is the air density, and the unit of rho is kg/m3R is the radius of a wind wheel of the fan, v is the wind speed, and the unit of v is m/s;
in the formula (3), the reaction mixture is,
Figure BDA0002924966220000022
respectively as the predicted maximum value and the predicted minimum value of the fan output at the time t,
Figure BDA0002924966220000023
influence factors of the output prediction error of the fan at the moment t;
Figure BDA0002924966220000024
in the formula (4), the reaction mixture is,
Figure BDA0002924966220000025
the predicted average value of the output of the fan for a plurality of times at the time t is obtained;
step 1.2: output calculation taking photovoltaic uncertainty into account
The actual photovoltaic output considering the photovoltaic uncertainty at the moment t is calculated as
Figure BDA0002924966220000026
In the formula (5), the reaction mixture is,
Figure BDA0002924966220000027
for the predicted value of the photovoltaic output at the moment t,
Figure BDA0002924966220000028
predicting an error value for the photovoltaic output at the time t;
Figure BDA0002924966220000029
Figure BDA00029249662200000210
in the formula (6), PPV，STCRated capacity of photovoltaic under STC condition, GCThe intensity of illumination in the area of the photovoltaic cell, GCHas a unit of kW/m2，GSTCIs the intensity of solar radiation, T, under STC conditionsCFor the operating temperature, T, of the panel of the photovoltaic in the process of converting electric energyC，STCThe temperature of the photovoltaic cell panel under the STC condition;
in the formula (7), the reaction mixture is,
Figure BDA00029249662200000211
respectively as the predicted maximum value and the predicted minimum value of the photovoltaic output at the time t,
Figure BDA00029249662200000212
influence factors of photovoltaic output prediction errors are obtained;
Figure BDA00029249662200000213
in the formula (8), the reaction mixture is,
Figure BDA00029249662200000214
the average value of multiple photovoltaic output predictions at the time t;
step 2: establishing a load side uncertainty calculation model
Step 2.1: electrical load calculation taking into account electrical load uncertainty
Calculating the actual value of the electrical load considering the uncertainty of the electrical load at the moment t as
Figure BDA00029249662200000215
In the formula (9), the reaction mixture is,
Figure BDA0002924966220000031
for pre-charging the electrical load at time tThe value of the measured value is measured,
Figure BDA0002924966220000032
predicting an error value for the electrical load at time t;
Figure BDA0002924966220000033
in the formula (10), the compound represented by the formula (10),
Figure BDA0002924966220000034
respectively as the predicted maximum value and minimum value of the electric load at the time t,
Figure BDA0002924966220000035
the impact factor of the prediction error for the electrical load at time t,
Figure BDA0002924966220000036
in the formula (11), the reaction mixture is,
Figure BDA0002924966220000037
the average value of multiple times of electric load prediction at the time t;
step 2.2: gas load calculation taking into account gas load uncertainty
Calculating the actual value of the air load considering the uncertainty of the air load at the moment t as
Figure BDA0002924966220000038
In the formula (12), the reaction mixture is,
Figure BDA0002924966220000039
is a predicted value of the air load at the moment t,
Figure BDA00029249662200000310
predicting an error value for the gas load at time t;
Figure BDA00029249662200000311
in the formula (13), the reaction mixture is,
Figure BDA00029249662200000312
respectively as the maximum value and the minimum value of the air load prediction at the time t,
Figure BDA00029249662200000313
for the influencing factor of the air load prediction error at time t,
Figure BDA00029249662200000314
in the formula (14), the compound represented by the formula (I),
Figure BDA00029249662200000315
is the average of multiple air load predictions at time t, LgInstalled capacity for air load;
step 2.3: thermal load calculation taking into account thermal load uncertainty
Calculating the actual value of the heat load considering the uncertainty of the heat load at the moment t as
Figure BDA00029249662200000316
In the formula (15), the reaction mixture is,
Figure BDA00029249662200000317
for the predicted value of the thermal load at time t,
Figure BDA00029249662200000318
predicting an error value for the thermal load at time t;
Figure BDA00029249662200000319
in the formula (16), the compound represented by the formula,
Figure BDA00029249662200000320
respectively as the maximum value and the minimum value of the thermal load prediction at the time t,
Figure BDA00029249662200000321
the impact factor of the thermal load prediction error for time t,
Figure BDA0002924966220000041
in the formula (17), the compound represented by the formula (I),
Figure BDA0002924966220000042
is the average of multiple thermal load predictions at time t, LhInstalled capacity as heat load;
and step 3: comprehensive index calculation for economic dispatching of power grid
Step 3.1: power grid economic dispatching reliability index calculation
Calculating the average power supply unavailability rate of the power grid considering the uncertainty of the source load
Figure BDA0002924966220000043
In the formula (18), T is the number of electricity-requiring hours in a predetermined time, TsyThe off-line time of the power grid within the specified time is defined;
taking the average power supply unavailability rate of the power grid as an economic dispatching reliability index of the power grid;
step 3.2: power grid economic dispatching economy index calculation
The economic dispatching economic index of the power grid considering the uncertainty of the source load is calculated as
Figure BDA0002924966220000044
In the formula (19), cw、cpvRespectively wind power grid-connected electricity price and photovoltaic grid-connected electricity price;
step 3.3: calculation of non-schedulability index of economic dispatching of power grid
The regulation and control accuracy rate considering the uncertainty of the source load is calculated as
Figure BDA0002924966220000045
In the formula (20), NcThe number of scheduling time; l ismIs the installed capacity of the fan, LPVInstalled capacity for photovoltaics;
taking the regulation and control accuracy as an index of the economical dispatching non-schedulability of the power grid;
step 3.4: power grid economic dispatching comprehensive index calculation considering source load uncertainty
The comprehensive index of the economic dispatching of the power grid considering the uncertainty of the source load is calculated as
Figure BDA0002924966220000046
And 4, step 4: comprehensive evaluation of economic dispatching of the power grid:
if A belongs to [ a, 1], under the condition of considering source load uncertainty, the power grid operation reliability is high, the scheduling cost is low, and the non-schedulability is low in the power grid economic scheduling process;
if A belongs to [0, a), under the condition of considering source load uncertainty, the operation reliability of the power grid is low, the dispatching cost is too high, and the non-dispatchability is higher in the economic dispatching process of the power grid.
The invention has the beneficial effects that:
(1) according to the method, when the prediction error is calculated, source load uncertainty is considered, the uncertainty is represented in a random planning mode, and the function and data are combined and analyzed to obtain the optimization function, so that the technical problem that the calculation of the energy supply end is inaccurate due to artificial subjective errors when the fuzzy theory is adopted in the conventional power grid economic dispatching calculation method considering the prediction error is solved.
(2) The method not only considers the factors related to the economical efficiency of the power grid economic dispatching, but also considers the related factors of the dispatching reliability and the dispatching adjustability of the system, more comprehensively, objectively and in line with the practical calculation of the comprehensive evaluation index of the power grid economic dispatching, and solves the technical problem that the existing single economic calculation method cannot guarantee the requirements on the reliability and the dispatching adjustability of the power grid in the dispatching process.
(3) According to the method, on the basis of considering source load uncertainty, the prediction error is calculated by adopting random planning, and the comprehensive evaluation index is calculated by using three types of indexes, so that the accuracy of the economic dispatching evaluation of the power grid is greatly improved, and the reliability, the schedulability and the economy of the economic dispatching of the power grid can be simultaneously ensured.
Drawings
Fig. 1 is a flowchart of a comprehensive evaluation method for economic dispatch of a power grid considering source load uncertainty according to the present invention.
FIG. 2 is a graph comparing fan output prediction errors of different models in an embodiment.
Fig. 3 is a comparison graph of photovoltaic output prediction errors of different models in a specific embodiment.
Fig. 4 is a comparison diagram of the number of start-stop times of the unit in each time period in different scheduling manners in the specific embodiment.
Fig. 5 is a comparison diagram of unbalanced power of each time interval under different scheduling modes in an embodiment.
Detailed Description
The invention will be further described with reference to the accompanying drawings and specific embodiments.
In this embodiment, the economic dispatch of a certain power grid at a certain time t is comprehensively evaluated.
As shown in fig. 1, the method for comprehensively evaluating economic dispatch of a power grid considering uncertainty of source load of the invention comprises the following steps:
step 1: establishing a source side uncertainty calculation model
Step 1.1: output calculation taking into account fan uncertainty
The wind power generation has strong uncertainty, is influenced by wake effect, terrain, installed capacity and weather, accurate description of prediction errors in power grid economic dispatching considering source load uncertainty is the premise of safe economic dispatching in an energy system, and the uncertainty of the wind power generation is represented by the sum of a determined prediction value and the uncertain prediction errors in combination with the characteristics of a wind generating set. The wind power generation is combined with the predicted maximum and minimum fluctuation values, and the actual output of the fan considering the uncertainty of the fan at the moment t is calculated as
Figure BDA0002924966220000051
In the formula (1), the reaction mixture is,
Figure BDA0002924966220000061
the predicted value of the fan output at the moment t,
Figure BDA0002924966220000062
and predicting an error value for the output of the fan at the moment t.
Figure BDA0002924966220000063
Figure BDA0002924966220000064
In the formula (2), λcIs the electromechanical conversion coefficient of the fan, CpIs the wind energy utilization coefficient of the fan, rho is the air density, and the unit of rho is kg/m3R is the radius of a wind wheel of the fan, v is the wind speed, and the unit of v is m/s;
in the formula (3), the reaction mixture is,
Figure BDA0002924966220000065
respectively as the predicted maximum value and the predicted minimum value of the fan output at the time t,
Figure BDA0002924966220000066
and the influence factor of the output prediction error of the fan at the moment t.
Figure BDA0002924966220000067
And calculating by adopting a stochastic programming method, wherein the stochastic programming method is in accordance with probability density distribution.
Figure BDA0002924966220000068
In the formula (4), the reaction mixture is,
Figure BDA0002924966220000069
and the predicted average value of the output of the fan for a plurality of times at the time t is obtained.
In this embodiment, the electromechanical conversion coefficient λ of the fanc90%, wind energy utilization coefficient C of the fanp0.5, and an air density ρ of 1.2kg/m at time t3The radius R of the rotor of the fan is 13m, and the wind speed v at the moment t is 5 m/s. The predicted values of the output of the tertiary fan are respectively as follows: 5893.9MW, 6453.8MW, 6555.0MW, hence the maximum predicted fan output
Figure BDA00029249662200000610
Minimum value
Figure BDA00029249662200000611
Average value of multiple fan output predictions at time t
Figure BDA00029249662200000612
The last predicted value is used as the predicted value of the output of the fan at the time t, so that
Figure BDA00029249662200000613
Calculated according to the formula (4),
Figure BDA00029249662200000614
according to the formulas (1) to (3), the calculation is carried out to obtain
Figure BDA00029249662200000615
Step 1.2: output calculation taking photovoltaic uncertainty into account
Aiming at the photovoltaic output uncertainty, the illumination intensity, the solar radiation intensity under the standard test condition and various coefficients of the photovoltaic equipment are considered, the photovoltaic power generation characteristic is combined, and the photovoltaic actual output considering the photovoltaic uncertainty at the moment t is calculated as
Figure BDA00029249662200000616
In the formula (5), the reaction mixture is,
Figure BDA00029249662200000617
for the predicted value of the photovoltaic output at the moment t,
Figure BDA00029249662200000618
predicting an error value for the photovoltaic output at the time t;
Figure BDA00029249662200000619
Figure BDA00029249662200000620
in the formula (6), PPV，STCRated capacity of photovoltaic under STC condition, GCThe intensity of illumination in the area of the photovoltaic cell, GCHas a unit of kW/m2，GSTCIs the intensity of solar radiation, T, under STC conditionsCFor the operating temperature, T, of the panel of the photovoltaic in the process of converting electric energyC，STCThe temperature of the photovoltaic cell panel under the STC condition;
in the formula (7), the reaction mixture is,
Figure BDA0002924966220000071
respectively as the predicted maximum value and the predicted minimum value of the photovoltaic output at the time t,
Figure BDA0002924966220000072
influence factors of photovoltaic output prediction errors are obtained;
Figure BDA0002924966220000073
in the formula (8), the reaction mixture is,
Figure BDA0002924966220000074
and (4) the average value of multiple photovoltaic output predictions at the time t.
In this example, the photovoltaic rated capacity P under STC conditionPV，STC5kW, the illumination intensity G in the photovoltaic region at time tC＝0.6kW/m2Intensity of solar radiation G under STC conditionsSTC＝1kW/m2The working temperature T of the panel of the photovoltaic in the process of converting the electric energyCTemperature T of the solar panel in photovoltaic under STC condition of 26 DEG CC，STCAt 25 ℃. The predicted values of the three photovoltaic output are respectively as follows: 4133MW, 5528MW, 4948MW, maximum predicted photovoltaic output
Figure BDA0002924966220000075
Minimum value
Figure BDA0002924966220000076
Average value of multiple photovoltaic output predictions at time t
Figure BDA0002924966220000077
The last predicted value is used as the predicted value of the photovoltaic output at the moment t, so that
Figure BDA0002924966220000078
Calculated according to the formula (8),
Figure BDA0002924966220000079
according to the formulas (5) to (7), the calculation results
Figure BDA00029249662200000710
Step 2: establishing a load side uncertainty calculation model
For the uncertainty of the load, converting uncertain variables contained in the load into uncertainty distribution representation for the load prediction error, so that the actual load value is the sum of the load prediction numerical value and the uncertainty error, which is specifically as follows:
step 2.1: electrical load calculation taking into account electrical load uncertainty
Calculating the actual value of the electrical load considering the uncertainty of the electrical load at the moment t as
Figure BDA00029249662200000711
In the formula (9), the reaction mixture is,
Figure BDA00029249662200000712
for the predicted value of the electrical load at time t,
Figure BDA00029249662200000713
an error value is predicted for the electrical load at time t.
Aiming at the uncertain calculation process of the electric load, the load curve has certain repeatability, so that the correlation between the electric load prediction error and the time scale is relatively small, the prediction error is relatively large at the peak value and is small at the valley value at three special points of the peak, the flat and the valley in the electric load prediction process, and the electric load prediction error value at the time t is further calculated
Figure BDA00029249662200000714
Is composed of
Figure BDA00029249662200000715
In the formula (10), the compound represented by the formula (10),
Figure BDA00029249662200000716
respectively as the predicted maximum value and minimum value of the electric load at the time t,
Figure BDA00029249662200000717
the impact factor of the prediction error for the electrical load at time t,
Figure BDA0002924966220000081
in the formula (11), the reaction mixture is,
Figure BDA0002924966220000082
is the average of multiple electrical load predictions at time t.
In this embodiment, the three predicted values of the electrical load are: 4465MW, 4285MW, 4331MW, the maximum predicted electrical load
Figure BDA0002924966220000083
Minimum value
Figure BDA0002924966220000084
Average of multiple electrical load predictions at time t
Figure BDA0002924966220000085
The last predicted value is used as the predicted value of the electric load at the time t, so that
Figure BDA0002924966220000086
Calculated according to the formula (11),
Figure BDA0002924966220000087
according to the formulas (9) to (10), the calculation results
Figure BDA0002924966220000088
Step 2.2: gas load calculation taking into account gas load uncertainty
Aiming at the principle that the prediction error of the gas load and the electric load are similar to the normal distribution of the load, the actual value of the gas load has a crucial influence on the economic dispatching of the system, the economical efficiency of the system operation can be improved under the condition of considering the uncertainty of the actual value, and the actual value of the gas load considering the uncertainty of the gas load at the moment t is calculated as
Figure BDA0002924966220000089
In the formula (12), the reaction mixture is,
Figure BDA00029249662200000810
is a predicted value of the air load at the moment t,
Figure BDA00029249662200000811
predicting an error value for the gas load at time t;
Figure BDA00029249662200000812
in the formula (13), the reaction mixture is,
Figure BDA00029249662200000813
respectively as the maximum value and the minimum value of the air load prediction at the time t,
Figure BDA00029249662200000814
for the influencing factor of the air load prediction error at time t,
Figure BDA00029249662200000815
in the formula (14), the compound represented by the formula (I),
Figure BDA00029249662200000816
is the average of multiple air load predictions at time t, LgIs the installed capacity of the air load.
In this embodiment, the predicted values of the tertiary air load are respectively: 3345MW, 3567MW, 3472MW, maximum of gas load prediction
Figure BDA00029249662200000817
Minimum value
Figure BDA00029249662200000818
Average of multiple air load predictions at time t
Figure BDA00029249662200000819
The last predicted value is taken as the predicted value of the air load at the time t, so that
Figure BDA00029249662200000820
Installed capacity L of air loadg＝3600MW。
Calculated according to the formula (14),
Figure BDA00029249662200000821
according to the formulas (12) to (13), the calculation results
Figure BDA00029249662200000822
Step 2.3: thermal load calculation taking into account thermal load uncertainty
Aiming at the principle that the thermal load prediction error and the electric load are similar to the load normal distribution, the actual value of the thermal load has a crucial influence on the economic dispatching of the system, the economical efficiency of the system operation can be improved under the condition of considering the uncertainty of the actual value, and the actual value of the thermal load considering the uncertainty of the thermal load at the moment t is calculated as
Figure BDA0002924966220000091
In the formula (15), the reaction mixture is,
Figure BDA0002924966220000092
for the predicted value of the thermal load at time t,
Figure BDA0002924966220000093
predicting an error value for the thermal load at time t;
Figure BDA0002924966220000094
in the formula (16), the compound represented by the formula,
Figure BDA0002924966220000095
respectively as the maximum value and the minimum value of the thermal load prediction at the time t,
Figure BDA0002924966220000096
the impact factor of the thermal load prediction error for time t,
Figure BDA0002924966220000097
in the formula (17), the compound represented by the formula (I),
Figure BDA0002924966220000098
is the average of multiple thermal load predictions at time t, LhThe installed capacity of the thermal load.
In this embodiment, the three predicted values of the thermal load are: 3678MW, 3578MW, 3675MW, maximum of thermal load prediction
Figure BDA0002924966220000099
Minimum value
Figure BDA00029249662200000910
Average of multiple thermal load predictions at time t
Figure BDA00029249662200000911
The last predicted value is used as the predicted value of the thermal load at the time t, so that
Figure BDA00029249662200000912
Installed capacity L of thermal loadh＝3800MW。
Calculated according to the formula (17),
Figure BDA00029249662200000913
according to the formulas (15) to (16), the calculation results
Figure BDA00029249662200000914
And step 3: comprehensive index calculation for economic dispatching of power grid
The prediction error can cause errors between the actual fan power generation, photovoltaic power generation and power grid dispatching capacity and the corresponding strategy, and the errors are gradually enlarged according to the uncertainty of electricity, heat and gas on the load side. The power grid economic dispatching calculation method considering the source load uncertainty performs analysis and calculation by considering three types of indexes, namely a reliability index, an economic index and a schedulability index, and power grid dispatching is performed by considering the three types of indexes under the condition that the source load uncertainty is considered, so that the reliability, the economic efficiency and the rationality of the power grid economic dispatching calculation method are guaranteed.
Step 3.1: power grid economic dispatching reliability index calculation
Considering the condition that the source load is uncertain, whether the source side can ensure stable and reliable external output power or not and calculating the average power supply unavailability of the power grid
Figure BDA00029249662200000915
In the formula (18), T is the number of electricity-requiring hours in a predetermined time, TsyThe off-line time of the power grid within the specified time is defined;
and taking the average power supply unavailability rate of the power grid as an economic dispatching reliability index of the power grid.
In the present embodiment, the first and second electrodes are,
Figure BDA0002924966220000101
the electricity demand hours T in the specified time is 24h, and the outage time T of the power grid in the specified timesy2 h. A is obtained by calculation according to the formula (18)asai＝0.56。
Step 3.2: power grid economic dispatching economy index calculation
The economic dispatching economic index of the power grid considering the uncertainty of the source load is calculated as
Figure BDA0002924966220000102
In the formula (19)，cw、cpvThe electricity price of wind power on-line and the electricity price of photovoltaic on-line are respectively.
In this embodiment, the wind power grid electricity price cw0.58 yuan, photovoltaic on-line electricity price cpvThe compound has the advantages of 0.45-element,
Figure BDA0002924966220000103
Figure BDA0002924966220000104
according to the formula (19), A is obtained by calculationw，pv＝0.63。
Step 3.3: calculation of non-schedulability index of economic dispatching of power grid
The regulation and control accuracy rate considering the uncertainty of the source load is calculated as
Figure BDA0002924966220000105
In the formula (20), NcThe number of scheduling time; l ismIs the installed capacity of the fan, LPVInstalled capacity for photovoltaics;
and taking the regulation and control accuracy as the power grid economic dispatching non-schedulability index.
In this embodiment, the number of scheduling times Nc＝24，
Figure BDA0002924966220000106
Figure BDA0002924966220000107
Installed capacity L of fanm7000MW, installed photovoltaic capacity LPV5500 MW. According to the formula (20), A is obtained by calculationA＝0.42。
Step 3.4: power grid economic dispatching comprehensive index calculation considering source load uncertainty
The comprehensive index of the economic dispatching of the power grid considering the uncertainty of the source load is calculated as
Figure BDA0002924966220000108
In this example, Aasai＝0.56，Aw，pv＝0.6，AA0.42. And (3) calculating according to a formula (21) to obtain a comprehensive index A of the economic dispatching of the power grid, which is 0.5.
And 4, step 4: comprehensive evaluation of economic dispatching of the power grid:
if A belongs to [ a, 1], under the condition of considering source load uncertainty, the power grid operation reliability is high, the scheduling cost is low, and the non-schedulability is low in the power grid economic scheduling process. At the moment, the economic dispatching condition of the power grid is good, the output of the unit, the start and stop of the unit and the like do not need to be adjusted to an excessive degree, and the power grid runs reliably and has good benefits.
If A belongs to [0, a), under the condition of considering source load uncertainty, the operation reliability of the power grid is low, the dispatching cost is too high, and the non-dispatchability is higher in the economic dispatching process of the power grid. At this time, it is necessary to improve the problem of stable operation inside the system to prevent unnecessary economic loss during the scheduling process.
In this example, a is 0.4. Therefore, A is 0.5 epsilon [ a, 1], so that the economic dispatching condition of the power grid at the moment t is good, the system is relatively stable in operation, the schedulability is relatively good, but dispatching optimization is required, and the condition that various numerical values in the system exceed the standard or are lower than the minimum value in the dispatching process is prevented.
In the embodiment, a random planning mode for calculating the output prediction error of the fan and the photovoltaic is compared with a traditional fuzzy model, the scheduling effect of an economic scheduling mode and a single economic scheduling mode is integrated, and the scheduling effect of a power grid economic scheduling mode with uncertain source load and a traditional economic scheduling mode is considered.
FIG. 2 is a graph comparing the wind turbine output prediction error considering the wind turbine uncertainty between the stochastic programming model and the conventional fuzzy model. FIG. 3 is a graph illustrating a comparison of the wind turbine output prediction error of the stochastic programming model and the conventional fuzzy model considering the uncertainty of the wind turbine. As can be seen from the figures 2 and 3, the stochastic programming model adopted by the method is higher in calculation accuracy than the traditional fuzzy model, and the calculation accuracy of the energy supply end can be improved.
The economic dispatching calculation of the power grid is carried out by considering the uncertainty of the source load, and the prediction error is reduced, and the reliability, the schedulability and the economic index of the power grid are called for carrying out comprehensive calculation, so that the reserved rotary reserve of the unit in the system is reduced to some extent under the condition, the running cost of the system and the start-stop loss of the unit are reduced, the system is safer and more reliable to run, and the economic efficiency is improved.
The comparison graph of the unbalanced power generated by the system in the power grid economic dispatching process considering source load uncertainty and the power grid economic dispatching process in the traditional economic dispatching mode is shown in fig. 5. As can be seen from fig. 5, under the power grid economic dispatch considering the comprehensive factors, the distribution of unbalanced power in the system is reduced, the reliability of the system in the dispatching process is ensured, and the economic dispatch is more real and effective.
It is to be understood that the above-described embodiments are only a few embodiments of the present invention, and not all embodiments. The above examples are only for explaining the present invention and do not constitute a limitation to the scope of protection of the present invention. All other embodiments, which can be derived by those skilled in the art from the above-described embodiments without any creative effort, namely all modifications, equivalents, improvements and the like made within the spirit and principle of the present application, fall within the protection scope of the present invention claimed.

Claims (1)

1. A comprehensive assessment method for power grid economic dispatching considering source load uncertainty is characterized by comprising the following steps: the method comprises the following steps:
step 1: establishing a source side uncertainty calculation model
Step 1.1: output calculation taking into account fan uncertainty
The actual output of the fan considering the uncertainty of the fan at the moment t is calculated as
Figure FDA0002924966210000011
In the formula (1), the reaction mixture is,
Figure FDA0002924966210000012
the predicted value of the fan output at the moment t,
Figure FDA0002924966210000013
predicting an error value for the output of the fan at the time t;
Figure FDA0002924966210000014
Figure FDA0002924966210000015
in the formula (2), λcIs the electromechanical conversion coefficient of the fan, CpIs the wind energy utilization coefficient of the fan, rho is the air density, and the unit of rho is kg/m3R is the radius of a wind wheel of the fan, v is the wind speed, and the unit of v is m/s;
in the formula (3), the reaction mixture is,
Figure FDA0002924966210000016
respectively as the predicted maximum value and the predicted minimum value of the fan output at the time t,
Figure FDA0002924966210000017
influence factors of the output prediction error of the fan at the moment t;
Figure FDA0002924966210000018
in the formula (4), the reaction mixture is,
Figure FDA0002924966210000019
the predicted average value of the output of the fan for a plurality of times at the time t is obtained;
step 1.2: output calculation taking photovoltaic uncertainty into account
The actual photovoltaic output considering the photovoltaic uncertainty at the moment t is calculated as
Figure FDA00029249662100000110
In the formula (5), the reaction mixture is,
Figure FDA00029249662100000111
for the predicted value of the photovoltaic output at the moment t,
Figure FDA00029249662100000112
predicting an error value for the photovoltaic output at the time t;
Figure FDA00029249662100000113
Figure FDA00029249662100000114
in the formula (6), PPV，STCRated capacity of photovoltaic under STC condition, GCThe intensity of illumination in the area of the photovoltaic cell, GCHas a unit of kW/m2，GSTCIs the intensity of solar radiation, T, under STC conditionsCFor the operating temperature, T, of the panel of the photovoltaic in the process of converting electric energyC，STCThe temperature of the photovoltaic cell panel under the STC condition;
in the formula (7), the reaction mixture is,
Figure FDA00029249662100000115
respectively as the predicted maximum value and the predicted minimum value of the photovoltaic output at the time t,
Figure FDA00029249662100000116
influence factors of photovoltaic output prediction errors are obtained;
Figure FDA0002924966210000021
in the formula (8), the reaction mixture is,
Figure FDA0002924966210000022
the average value of multiple photovoltaic output predictions at the time t;
step 2: establishing a load side uncertainty calculation model
Step 2.1: electrical load calculation taking into account electrical load uncertainty
Calculating the actual value of the electrical load considering the uncertainty of the electrical load at the moment t as
Figure FDA0002924966210000023
In the formula (9), the reaction mixture is,
Figure FDA0002924966210000024
for the predicted value of the electrical load at time t,
Figure FDA0002924966210000025
predicting an error value for the electrical load at time t;
Figure FDA0002924966210000026
in the formula (10), the compound represented by the formula (10),
Figure FDA0002924966210000027
respectively as the predicted maximum value and minimum value of the electric load at the time t,
Figure FDA0002924966210000028
the impact factor of the prediction error for the electrical load at time t,
Figure FDA0002924966210000029
in the formula (11), the reaction mixture is,
Figure FDA00029249662100000210
the average value of multiple times of electric load prediction at the time t;
step 2.2: gas load calculation taking into account gas load uncertainty
Calculating the actual value of the air load considering the uncertainty of the air load at the moment t as
Figure FDA00029249662100000211
In the formula (12), the reaction mixture is,
Figure FDA00029249662100000212
is a predicted value of the air load at the moment t,
Figure FDA00029249662100000213
predicting an error value for the gas load at time t;
Figure FDA00029249662100000214
in the formula (13), the reaction mixture is,
Figure FDA00029249662100000215
respectively as the maximum value and the minimum value of the air load prediction at the time t,
Figure FDA00029249662100000216
for the influencing factor of the air load prediction error at time t,
Figure FDA00029249662100000217
in the formula (14), the compound represented by the formula (I),
Figure FDA00029249662100000218
is the average of multiple air load predictions at time t, LgInstalled capacity for air load;
step 2.3: thermal load calculation taking into account thermal load uncertainty
Calculating the actual value of the heat load considering the uncertainty of the heat load at the moment t as
Figure FDA0002924966210000031
In the formula (15), the reaction mixture is,
Figure FDA0002924966210000032
for the predicted value of the thermal load at time t,
Figure FDA0002924966210000033
predicting an error value for the thermal load at time t;
Figure FDA0002924966210000034
in the formula (16), the compound represented by the formula,
Figure FDA0002924966210000035
respectively as the maximum value and the minimum value of the thermal load prediction at the time t,
Figure FDA0002924966210000036
the impact factor of the thermal load prediction error for time t,
Figure FDA0002924966210000037
in the formula (17), the compound represented by the formula (I),
Figure FDA0002924966210000038
is the average of multiple thermal load predictions at time t, LhInstalled capacity as heat load;
and step 3: comprehensive index calculation for economic dispatching of power grid
Step 3.1: power grid economic dispatching reliability index calculation
Calculating the average power supply unavailability rate of the power grid considering the uncertainty of the source load
Figure FDA0002924966210000039
In the formula (18), T is the number of electricity-requiring hours in a predetermined time, TsyThe off-line time of the power grid within the specified time is defined;
taking the average power supply unavailability rate of the power grid as an economic dispatching reliability index of the power grid;
step 3.2: power grid economic dispatching economy index calculation
The economic dispatching economic index of the power grid considering the uncertainty of the source load is calculated as
Figure FDA00029249662100000310
In the formula (19), cw、cpvRespectively wind power grid-connected electricity price and photovoltaic grid-connected electricity price;
step 3.3: calculation of non-schedulability index of economic dispatching of power grid
The regulation and control accuracy rate considering the uncertainty of the source load is calculated as
Figure FDA00029249662100000311
In the formula (20), NcThe number of scheduling time; l ismIs the installed capacity of the fan, LPVInstalled capacity for photovoltaics;
taking the regulation and control accuracy as an index of the economical dispatching non-schedulability of the power grid;
step 3.4: power grid economic dispatching comprehensive index calculation considering source load uncertainty
The comprehensive index of the economic dispatching of the power grid considering the uncertainty of the source load is calculated as
Figure FDA0002924966210000041
And 4, step 4: comprehensive evaluation of economic dispatching of the power grid:
if A belongs to [ a, 1], under the condition of considering source load uncertainty, the power grid operation reliability is high, the scheduling cost is low, and the non-schedulability is low in the power grid economic scheduling process;
if A belongs to [0, a), under the condition of considering source load uncertainty, the operation reliability of the power grid is low, the dispatching cost is too high, and the non-dispatchability is higher in the economic dispatching process of the power grid.
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