CN112467730A - Power system optimal scheduling method considering wind-solar output prediction error and demand response flexibility - Google Patents

Power system optimal scheduling method considering wind-solar output prediction error and demand response flexibility Download PDF

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CN112467730A
CN112467730A CN202011307109.1A CN202011307109A CN112467730A CN 112467730 A CN112467730 A CN 112467730A CN 202011307109 A CN202011307109 A CN 202011307109A CN 112467730 A CN112467730 A CN 112467730A
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苟竞
李奥
雷云凯
刘阳
刘嘉蔚
陈玮
张琳
李婷
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Economic and Technological Research Institute of State Grid Sichuan Electric Power Co Ltd
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Abstract

The invention discloses an optimal scheduling method of a power system considering wind-solar output prediction error and demand response flexibility, which is characterized by comprising the following steps of: (1) making an electricity price; (2) establishing a wind-solar output prediction error model, analyzing the sensitivity of load demand response and electricity price, and establishing a price type demand response and conventional unit flexible coordination scheduling model; (3) establishing a target model with the lowest total system operation cost to redistribute the user demand response; (4) analyzing basic constraint conditions of excitation type load demand response, establishing a real-time scheduling model with the minimum unbalanced power of the system as a target and the time scale as a minute level, and finally outputting a real-time scheduling plan according to the real-time scheduling model on the premise of meeting the basic constraint conditions. The method can effectively deal with the prediction errors of wind power and photovoltaic, stabilize the fluctuation of load, wind power and photovoltaic, and realize the high-efficiency absorption of wind power and photovoltaic and the optimal scheduling of a source-load double-end flexible resource system.

Description

Power system optimal scheduling method considering wind-solar output prediction error and demand response flexibility
Technical Field
The invention relates to the technical field of electric power, in particular to an electric power system optimal scheduling method considering wind-solar output prediction errors and demand response flexibility.
Background
With the increasingly serious energy problem and the development of an electric power system, new energy sources such as wind power, photoelectricity and the like are one of important solutions for solving the increasingly shortage problem of energy sources in the future and are greatly supported by various countries, but the access of the wind power and the photovoltaic enables a power grid to generate a large number of uncertain factors, and the phenomena of wind abandoning and light abandoning are increasingly serious along with the increase of the access scale of the wind power and the photovoltaic.
At present, a great deal of research is carried out by a plurality of experts at home and abroad aiming at the problems of uncertain factors and consumption of new energy such as wind power, photovoltaic and the like. A learner establishes a two-stage random planning wind power consumption unit combination model for flexibly configuring day-ahead electricity price type and day-in incentive type demand response resources to participate in power balance based on response complementary relation of two types of demands of incentive and electricity price. The learners further take the wind-solar output uncertainty into consideration, and establish a two-stage optimization model or an adjustable robust optimization model. On the basis of solving the problem of optimal scheduling of different time scales, a scholars provides a source-load interaction two-stage coordination control strategy by analyzing the complementary relation of a conventional power supply, demand response and high energy-carrying load on the regulation of wind power and photovoltaic fluctuation so as to realize the maximum consumption of wind power.
The above research has important significance for solving the uncertainty and the absorption problem of wind power and photovoltaic, but the following problems still exist to be further solved:
1. how to fully excavate the flexible characteristics and economy of resources scheduled on both sides of supply and demand and realize the optimized scheduling of a source-load-double-end flexible resource system;
2. how to combine multi-type demand response with multi-time scale scheduling to reduce wind-light and photovoltaic uncertainty factors and improve the wind-light-electricity consumption.
Disclosure of Invention
The invention aims to provide a power system optimal scheduling method considering wind-solar output prediction errors and demand response flexibility, so that optimal scheduling of a source-to-charge dual-end flexible resource system is realized, and consumption of wind, solar and electricity is improved.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
the optimal scheduling method of the power system considering the wind-solar output prediction error and the demand response flexibility comprises the following steps:
step 1: the power grid company makes a power price;
step 2: establishing a wind-solar output prediction error model, analyzing the sensitivity of load demand response and electricity price, and establishing a price type demand response and conventional unit flexible coordination scheduling model, so as to perform day-ahead scheduling calculation and determine a conventional unit operation plan and a load operation plan; the price type demand response comprises an electricity price type load demand response and an incentive type load demand response, wherein the electricity price type load demand response is used for participating in daily tagging scheduling and intra-day scheduling, and the incentive type load demand response is used for participating in real-time scheduling;
the scheduling model is as follows:
Figure BDA0002788626680000021
in the formula: f. of1Representing morphological similarity and numerical similarityThe size of (d);
Figure BDA0002788626680000022
representing the magnitude of the load demand;
Figure BDA0002788626680000023
the scheduling output of the system under the daily sign prediction is represented, and the scheduling output comprises electricity price type load reduction, wind power output, photovoltaic output and the output of a conventional unit; t belongs to 1: T, wherein T is 24, which means that the scheduling in the day-ahead is carried out by taking 1 hour as a time scale;
and step 3: correcting a daily sign prediction error through wind power and photovoltaic short-term output prediction, analyzing the influence of the wind power photovoltaic output prediction error on real-time electricity price and user load requirements, re-distributing a target model to user requirement response according to the lowest total system operation cost, and re-determining a conventional unit operation plan and a load operation plan; the target model is as follows:
Figure BDA0002788626680000024
in the formula: f. of2The cost of the scheduling for the system is,
Figure BDA0002788626680000025
respectively the scheduling cost of electricity price type load, photovoltaic, wind power and conventional units;
and 4, step 4: analyzing basic constraint conditions of excitation type load demand response, establishing a real-time scheduling model with the minimum unbalanced power of the system as a target and the time scale as a minute level, and finally outputting a real-time scheduling plan according to the real-time scheduling model on the premise of meeting the basic constraint conditions; the basic constraint conditions comprise interruptible load, translatable load and reducible load, and the constraint conditions of the interruptible load, the translatable load and the reducible load are respectively as follows:
interruptible load constraint:
Figure BDA0002788626680000026
in the formula: pi(t)、Pi,ratexi(t) representing the actual power and the rated power of the load respectively; x is the number ofa(t) is a variable 0/1 representing the load operating state;
translatable load restraint:
Figure BDA0002788626680000031
in the formula: x is the number ofi(t) represents the operating state of the load i, indicating that it is in an operating state; y isa(t) indicates the start-stop condition of the load, ya(t) ═ 0 indicates that the load is off; alpha is alphai、βiIs constant and αi<βi；
The load constraint can be reduced:
Figure BDA0002788626680000032
in the formula: x is the number ofi,l(t) 0/1 variables representing operating conditions; n isiRepresenting n successive power modes that can be clipped; pa,i(t) represents an adjustable power level;
the real-time scheduling model is as follows:
Figure BDA0002788626680000033
in the formula:
Figure BDA0002788626680000034
representing the total amount of transferable load, interruptible load and translatable load scheduling;
Figure BDA0002788626680000035
respectively representing the actual output of wind power, photovoltaic and conventional units;
Figure BDA00027886266800000312
load response indicating electricity price at t timeInitial response.
Specifically, in step 2, the actual wind power output model is as follows:
Figure BDA0002788626680000037
the photovoltaic actual output model is as follows:
Figure BDA0002788626680000038
in the formula (I), the compound is shown in the specification,
Figure BDA0002788626680000039
representing wind power and photovoltaic predicted values;
Figure BDA00027886266800000310
and the prediction deviation of wind power and photovoltaic is shown.
Further, a system constraint condition is added in the step 2, which is a system power balance constraint, an upper and lower limit constraint of a conventional unit and a power climbing constraint, respectively, wherein:
the upper and lower limits of the conventional unit are constrained as follows:
Figure BDA0002788626680000036
in the formula:
Figure BDA00027886266800000311
respectively representing the minimum and maximum output, P, of a conventional output unit at time tg,tIndicates the current output of the unit, ug,tIndicating the running state of the unit when ug,tWhen the unit is equal to 1, the unit is in the running state, otherwise, the unit is stopped;
power ramp constraints are as follows:
Figure BDA0002788626680000041
in the formula: pg,tRepresenting the magnitude of the output of the unit at time t, Pg,t-1Representing the output of the unit at time t-1, RuThe climbing limit of the conventional unit is reached.
Specifically, in step 3, the electricity price is corrected based on the intra-day prediction data:
Figure BDA0002788626680000042
in the formula, deltaC is the electricity price variable quantity, and C is the real-time electricity price;
and the relation between the electricity price and the electricity price type load change rate is expressed by adopting price demand elasticity:
Figure BDA0002788626680000044
in the formula, lambda is the influence of the electricity price change of the electricity price type load resource at the t time period on the response rate of the electricity price type load;
Figure BDA0002788626680000047
representing the power price type load response variation in the t period; delta Ct、CtRepresenting the electricity price variation and the initial electricity price in the t period;
Figure BDA0002788626680000048
the power corresponding to a negative value is the daily scheduled reallocation amount.
Preferably, the wind-solar-photovoltaic output prediction is carried out by using the intra-day scheduling and taking 15min as a time scale.
And further, in the step 4, a real-time scheduling model is established by taking the minimum unbalanced power of the system as a target and taking 1min as a time scale.
Furthermore, step 4 adds an upper and lower limit constraint for transferable loads and reduction amounts for reducing loads:
Figure BDA0002788626680000043
in the formula: alpha is alphat、γtAn upper limit and a lower limit of a reduction rate indicating that the excitation type load can be reduced or transferred,
Figure BDA0002788626680000045
representing the initial load amount of the load at time t,
Figure BDA0002788626680000046
represents the amount of reduction of the load at the current time, and η is the upper limit of the rate of change of the total amount of the load.
The main design idea of the invention is as follows:
1. the shape similarity and the numerical similarity of the load curve and the wind-light output are used as targets to establish a target function, the day-ahead scheduling is realized to realize the matching of the wind-light output and the load in a large range,
2. and the part of the electricity load reduced by the influence of the electricity price of the user at a certain time is used as the daily dispatching demand response reallocation amount so as to achieve the aim of lowest total operation cost of the system.
3. The real-time supply and demand matching is achieved as far as possible by means of the load demand and the clean energy output, the scheduling frequency of a conventional unit in real-time operation is reduced, and the demand response scheduling potential is fully excavated.
4. And (3) a scheduling strategy of three stages of day-ahead, day-in and real-time is constructed, so that the influence of load, wind power and photovoltaic prediction errors on the operation of the power grid is gradually reduced.
Therefore, compared with the prior art, the invention has the following beneficial effects:
(1) according to the method, the characteristics of price type demand response and excitation type demand response resources are combined to construct a day-ahead, in-day and real-time three-stage scheduling model, the scheduling mode can effectively deal with prediction errors of wind power and photovoltaic, and the fluctuation of load, wind power and photovoltaic is stabilized, so that high-efficiency absorption of the wind power and photovoltaic is realized.
(2) According to the invention, the intra-day scheduling aims at the lowest operation cost of the power grid, and the intra-day economic scheduling is realized on the basis of matching of wind-solar output and load in a large range, so that the clean energy consumption is promoted and the scheduling operation cost of the power grid is reduced. And in the real-time dispatching stage, the dispatching potential of demand response is fully utilized, the dispatching frequency of the output of the conventional unit is reduced, and a mode that the load is matched with the supply and demand ends of clean energy and the conventional unit is used for bottom-in-pocket regulation is formed. Therefore, the power grid can be in a low-carbon and low-cost running state as far as possible, and optimal scheduling of the source-to-load double-end flexible resource system is further achieved.
Drawings
FIG. 1 is a schematic flow chart of the present invention.
Fig. 2 is a schematic view of the electricity prices determined day before in the embodiment of the present invention.
FIG. 3 is a graph of load and wind-solar output predicted from the day before in an embodiment of the present invention.
Fig. 4 is a diagram illustrating a PDR scheduling result according to an embodiment of the present invention.
FIG. 5 is a graph illustrating load after scheduling in an embodiment of the present invention.
FIG. 6 is a graph of wind-solar predicted contribution in an embodiment of the present invention.
Fig. 7 is a schematic diagram of a scheduling result in a day according to an embodiment of the present invention.
Fig. 8 is a graph of load after real-time scheduling in an embodiment of the present invention.
Fig. 9 is a diagram illustrating an IDR scheduling result according to an embodiment of the present invention.
Detailed Description
The invention provides a wind power-photovoltaic power system operation optimization scheduling strategy considering demand response flexibility, and the scheduling strategy mainly combines characteristics of price type demand response and incentive type demand response resources to construct a day-ahead, day-in-day and real-time three-stage scheduling model. In the dispatching model, the sensibility of the general resident electricity demand response and the electricity price is analyzed in the day-ahead stage, and the day-ahead price demand response and the conventional unit flexible coordination dispatching model is constructed by taking the similarity of day-ahead dispatching and load demand curves and the similarity of numerical values as targets. The day scheduling aims at the lowest system operation cost, and the influence of the day-ahead prediction error on the electricity price is analyzed through short-term prediction of wind power and photovoltaic so as to realize the correction of the day-ahead scheduling. The real-time scheduling model aims at minimizing the unbalanced power and balances uncertainty errors by using various types of excitation type load characteristics so as to improve the consumption of wind power and photovoltaic.
As shown in fig. 1, the implementation process of the present invention is as follows:
step 1, a power grid company makes a power price.
Step 2, establishing a wind-solar output prediction error model, analyzing the sensitivity of load demand response and electricity price, and establishing a price type demand response and conventional unit flexible coordination scheduling model:
2.1, the output scheduling of wind power and photovoltaic is mostly realized by scheduling and predicting in advance and correcting in scheduling to control the output of the wind power and photovoltaic. In order to make the established model conform to the reality, the predicted value obtained by prediction is usually added with a predicted error value to represent the basic output condition of the wind power photovoltaic, and the model is as follows:
actual wind power output model:
Figure BDA0002788626680000061
photovoltaic actual output model:
Figure BDA0002788626680000062
in the formula:
Figure BDA0002788626680000063
respectively represents the actual output of wind power and photovoltaic power,
Figure BDA0002788626680000064
representing wind power and photovoltaic predicted values;
Figure BDA0002788626680000065
and the prediction deviation of wind power and photovoltaic is shown.
2.2, the invention divides the demand response into electricity price type load (PDR): the user is guided to spontaneously adjust the electricity consumption behavior through the time-of-use electricity price, such as the electricity consumption load of residents; exciting load (IDR): by compensating for the incentive to the customer to enter into agreements with the grid, such as transferable loads, interruptible loads, etc., the load can be reduced. The two types of demand response performance and duration performance are also quite different, such as PDR type loads: the response time is long, and the duration is short; IDR type load: short response time and long duration. It is also because of the differences in performance that the participating dispatch plans differ. PDR type load is suitable for participating in day-ahead scheduling and day-within scheduling, and IDR type load is suitable for participating in real-time scheduling.
And 2.3, forecasting the next day wind and light output 24 hours in advance by day-ahead scheduling, scheduling the electric quantity of PDR load response on the basis of considering real-time electricity price, establishing a target function by taking the form similarity and the numerical similarity of a load curve and the wind and light output as targets, realizing day-ahead scheduling, realizing the matching of the wind and light output and the load in a large range, and realizing the day-ahead scheduling reliability.
Figure BDA0002788626680000071
In the formula: f. of1Representing the magnitude of the morphological similarity and the numerical similarity;
Figure BDA0002788626680000074
representing the magnitude of the load demand;
Figure BDA0002788626680000075
the method comprises the steps of (1) representing the dispatching output of a system under the prediction of the day ahead, wherein the dispatching output comprises PDR load reduction, wind power output, photovoltaic output and the output of a conventional unit; t ∈ 1: T, T ═ 24 means that the day-ahead scheduling is scheduled on a 1-hour time scale.
Step 3, correcting a day-ahead prediction error through wind power and photovoltaic short-term output prediction, analyzing the influence of the wind power photovoltaic output prediction error on the real-time electricity price and the user load demand, and re-distributing the user demand response by establishing a model with the lowest total system operation cost as a target:
3.1, due to the relation between the prediction information and the time scale characteristic: the shorter the actual operation output of the prediction time, the higher the precision and the lower the cost. In the invention, the wind-light and photovoltaic output prediction is carried out by using the day scheduling with 15min as a time scale, and the wind-light output under short-term prediction also influences the change of the electricity price, so that the electricity price is corrected on the basis of day prediction data:
Figure BDA0002788626680000072
in the formula, Δ C is the electricity price variation, and C is the real-time electricity price.
The PDR resource influences the electricity consumption behavior of a user mainly through electricity price, so that the load response rate and the electricity price change form an elastic coefficient matrix relation, namely the relation between the electricity price and the PDR change rate is flexibly expressed by price demand:
Figure BDA0002788626680000076
in the formula, λ is the influence of the change of the electrovalence of the PDR resource in the period t on the PDR response rate.
Figure BDA0002788626680000077
Respectively representing the PDR response variation and the initial response in the t period; delta Ct、CtRepresents the electricity price change amount and the initial electricity price in the t period.
Figure BDA0002788626680000078
The power corresponding to a negative value is the daily scheduled reallocation amount.
3.2, on the basis that the day-ahead scheduling wind-solar output is matched with the load in a large range, establishing a model with the lowest total system operation cost as a target after correcting the electricity price in a day to realize the system scheduling economy, wherein the model comprises the following steps:
Figure BDA0002788626680000073
in the formula: f. of2The cost of the scheduling for the system is,
Figure BDA0002788626680000079
the scheduling cost of the PDR type load, the photovoltaic, the wind power and the conventional unit is respectively. The scheduling is always scheduled in the day ahead, and the scheduling is always corrected through short-term prediction in the day, meanwhile, the scheduling of the conventional unit is determined, and the scheduling plan of the conventional unit is not corrected at the real-time stage.
System constraint conditions:
(1) system power balance constraints.
(2) Upper and lower limit constraints of conventional units
Figure BDA0002788626680000081
In the formula:
Figure BDA0002788626680000084
respectively representing the minimum and maximum output, P, of a conventional output unit at time tg,tIndicates the current output of the unit, ug,tIndicating the running state of the unit when ug,tIf the value is 1, the unit is in the running state, otherwise, the unit is stopped.
(3) Power ramp restraint
Figure BDA0002788626680000082
In the formula: pg,tRepresenting the magnitude of the output of the unit at time t, Pg,t-1Representing the output of the unit at time t-1, RuThe climbing limit of the conventional unit is reached.
Step 4, analyzing three basic constraint conditions of excitation type demand response, and establishing a real-time scheduling model taking the minimum unbalanced power of the system as a target and 1min as a time scale:
4.1 interruptible load is mainly load capable of interrupting operation at any time in an operating state, and hardly affects the basic production life of users under the condition of short accumulated interruption operation time, such as electric vehicles and the like.
Assuming that the power of the interruptible load is constant, its basic constraints are as follows:
Figure BDA0002788626680000083
in the formula: pi(t)、Pi,ratexi(t) representing the actual power and the rated power of the load respectively; x is the number ofa(t) is a variable 0/1 representing the load operating state.
The translatable load mainly refers to a load which cannot interrupt the working state of the load in the normal operation process but can lead or delay the working state, such as a washing machine and the like.
Assuming constant power of the transferable load, the basic constraints are as follows:
Figure BDA0002788626680000091
in the formula: x is the number ofi(t) represents the operating state of the load i, indicating that it is in an operating state; y isa(t) indicates the start-stop condition of the load, ya(t) ═ 0 indicates that the load is off; alpha is alphai、βiIs constant and αi<βi。
The load reduction mainly means that the load in the running working state can cut off part of power without influencing the basic production life of a user, such as a working screen with adjustable power brightness and the like.
Assuming constant power at which the load can be shed, the basic constraints are as follows:
Figure BDA0002788626680000092
in the formula: x is the number ofi,l(t) 0/1 variables representing operating conditions; n isiRepresenting reducible niA continuous power mode;Pa,i(t) denotes the adjustable power level.
4.2, the scheduling plan of the conventional unit and the scheduling plan of the PDR type load are determined in daily scheduling, the response performance of the scheduling plan is not suitable for real-time scheduling, but because certain unbalanced power still exists in the real-time scheduling of wind power, photovoltaic output prediction and load prediction errors, the IDR type load with excellent response performance is used as the balanced and unbalanced power of real-time scheduling resources, the minimum unbalanced power is used as the target, the real-time scheduling is carried out by taking 1min as a time scale, and the model is as follows
Figure BDA0002788626680000093
In the formula:
Figure BDA0002788626680000094
representing the total amount of transferable load, interruptible load, translatable load scheduling,
Figure BDA0002788626680000095
and the actual output of the conventional unit is shown.
In addition to considering the relationship between the transfer time, the reduction time, and the user influence, the IDR type load also needs to consider the upper and lower limit constraints of the transferable load and the reduction amount of the reducible load;
Figure BDA0002788626680000101
in the formula: alpha is alphat、γtAn upper limit and a lower limit of a reduction rate indicating that the excitation type load can be reduced or transferred,
Figure BDA0002788626680000102
representing the initial load amount of the load at time t,
Figure BDA0002788626680000103
represents the amount of reduction of the load at the current time, and η is the upper limit of the rate of change of the total amount of the load.
The present invention is further illustrated by the following description and examples, including but not limited to the following examples, taken in conjunction with the accompanying drawings.
Examples
And (3) selecting a power grid of a certain region for example analysis, wherein the daily peak load of the power grid is 4050MW, the photovoltaic access capacity is 500MW, the wind power access capacity is 1000MW, and the electricity price determined in the day ahead is shown in figure 2. The load and wind-solar output curves predicted in the day-ahead are shown in fig. 3.
The first step, carrying out day-ahead scheduling:
the day-ahead scheduling predicts the wind and light output of the next day in advance for 24 hours, schedules the electric quantity of PDR load response on the basis of considering real-time electricity price, establishes a target function by taking the form similarity and the numerical similarity of a load curve and the wind and light output as targets, realizes the day-ahead scheduling, realizes the form similarity and the numerical similarity of a matching curve of the wind and light output and the load in a large range, realizes the day-ahead scheduling, realizes the matching of the output and supply and demand in the large range, and has the PDR scheduling result as shown in figure 4 and the load curve as shown in figure 5 after the scheduling. It can be seen that the load demand curve after scheduling is as close as possible to the predicted output of wind and light, and the net load curve of the system is more smooth, so as to promote the consumption of clean energy.
Secondly, carrying out day scheduling:
the intra-day scheduling predicts wind and light output by taking 15min as a time scale, and the wind and light output under short-term prediction also influences the change of the electricity price, at the moment, the wind and light predicted output is as shown in fig. 6, the electricity price is corrected based on intra-day prediction data, and in the embodiment, theta is-0.8, and lambda is-0.2.
Some users adjust the electricity usage plan as the electricity prices change, and this part load is the demand response redistribution amount. On the basis of meeting load requirements in a large range of scheduling in the day, a model is established for demand response redistribution by taking the lowest total cost of system operation as a target after the electricity price is corrected in the day, wind and light consumption is realized, meanwhile, the economy of system scheduling can be guaranteed, and the scheduling result in the day is shown in fig. 7.
Step three, real-time scheduling is carried out:
because the errors of wind power, photovoltaic output prediction and load prediction still have certain unbalanced power, the power needs to be balanced through real-time scheduling, when the power balance cannot be realized through the real-time scheduling, the power is absorbed through adjusting a conventional unit, and if the constraint is still not met, the wind and the light are abandoned. The real-time scheduled load curve is shown in fig. 8, and the IDR scheduling result is shown in fig. 9. It can be seen that the real-time scheduling reduces the fluctuation caused by load and wind-light output errors, and realizes the consumption of clean energy.
In conclusion, the method can effectively deal with the prediction errors of wind power and photovoltaic, stabilize the fluctuation of load, wind power and photovoltaic, realize high-efficiency consumption of wind power and photovoltaic and optimal scheduling of a source-load double-end flexible resource system, and further provide guarantee for solving the problems of uncertainty and consumption of wind power and photovoltaic. Therefore, the invention has outstanding substantive features and obvious progress.
The above-mentioned embodiment is only one of the preferred embodiments of the present invention, and should not be used to limit the scope of the present invention, but all the insubstantial modifications or changes made within the spirit and scope of the main design of the present invention, which still solve the technical problems consistent with the present invention, should be included in the scope of the present invention.

Claims (7)

1. The optimal scheduling method of the power system considering the wind-solar output prediction error and the demand response flexibility is characterized by comprising the following steps of:
step 1: the power grid company makes a power price;
step 2: establishing a wind-solar output prediction error model, analyzing the sensitivity of load demand response and electricity price, and establishing a price type demand response and conventional unit flexible coordination scheduling model, so as to perform day-ahead scheduling calculation and determine a conventional unit operation plan and a load operation plan; the price type demand response comprises an electricity price type load demand response and an incentive type load demand response, wherein the electricity price type load demand response is used for participating in daily tagging scheduling and intra-day scheduling, and the incentive type load demand response is used for participating in real-time scheduling;
the scheduling model is as follows:
Figure FDA0002788626670000011
in the formula: f. of1Representing the magnitude of the morphological similarity and the numerical similarity;
Figure FDA0002788626670000012
representing the magnitude of the load demand;
Figure FDA0002788626670000013
the scheduling output of the system under the daily sign prediction is represented, and the scheduling output comprises electricity price type load reduction, wind power output, photovoltaic output and the output of a conventional unit; t belongs to 1: T, wherein T is 24, which means that the scheduling in the day-ahead is carried out by taking 1 hour as a time scale;
and step 3: correcting a daily sign prediction error through wind power and photovoltaic short-term output prediction, analyzing the influence of the wind power photovoltaic output prediction error on real-time electricity price and user load requirements, re-distributing a target model to user requirement response according to the lowest total system operation cost, and re-determining a conventional unit operation plan and a load operation plan; the target model is as follows:
Figure FDA0002788626670000014
in the formula: f. of2The cost of the scheduling for the system is,
Figure FDA0002788626670000015
respectively the scheduling cost of electricity price type load, photovoltaic, wind power and conventional units;
and 4, step 4: analyzing basic constraint conditions of excitation type load demand response, establishing a real-time scheduling model with the minimum unbalanced power of the system as a target and the time scale as a minute level, and finally outputting a real-time scheduling plan according to the real-time scheduling model on the premise of meeting the basic constraint conditions; the basic constraint conditions comprise interruptible load, translatable load and reducible load, and the constraint conditions of the interruptible load, the translatable load and the reducible load are respectively as follows:
interruptible load constraint:
Figure FDA0002788626670000016
in the formula: pi(t)、Pi,ratexi(t) representing the actual power and the rated power of the load respectively; x is the number ofa(t) is a variable 0/1 representing the load operating state;
translatable load restraint:
Figure FDA0002788626670000021
in the formula: x is the number ofi(t) represents the operating state of the load i, indicating that it is in an operating state; y isa(t) indicates the start-stop condition of the load, ya(t) ═ 0 indicates that the load is off; alpha is alphai、βiIs constant and αi<βi；
The load constraint can be reduced:
Figure FDA0002788626670000022
in the formula: x is the number ofi,l(t) 0/1 variables representing operating conditions; n isiRepresenting n successive power modes that can be clipped; pa,i(t) represents an adjustable power level;
the real-time scheduling model is as follows:
Figure FDA0002788626670000023
in the formula:
Figure FDA0002788626670000024
representing the total amount of transferable load, interruptible load and translatable load scheduling;
Figure FDA0002788626670000025
respectively representing the actual output of wind power, photovoltaic and conventional units; pt PDRAnd represents the initial response quantity of the electricity price type load response in the period t.
2. The power system optimal scheduling method considering wind-solar power output prediction error and demand response flexibility of claim 1, wherein in the step 2, the wind-power actual output model is as follows:
Figure FDA0002788626670000026
the photovoltaic actual output model is as follows:
Figure FDA0002788626670000027
in the formula (I), the compound is shown in the specification,
Figure FDA0002788626670000028
representing wind power and photovoltaic predicted values;
Figure FDA0002788626670000029
and the prediction deviation of wind power and photovoltaic is shown.
3. The power system optimal scheduling method considering wind-solar power output prediction error and demand response flexibility of claim 2, wherein system constraint conditions are further added in the step 2, and the system constraint conditions are respectively a system power balance constraint, a conventional unit upper and lower limit constraint and a power ramp constraint, wherein:
the upper and lower limits of the conventional unit are constrained as follows:
Figure FDA0002788626670000031
in the formula:
Figure FDA0002788626670000032
respectively representing the minimum and maximum output, P, of a conventional output unit at time tg,tIndicates the current output of the unit, ug,tIndicating the running state of the unit when ug,tWhen the unit is equal to 1, the unit is in the running state, otherwise, the unit is stopped;
power ramp constraints are as follows:
Figure FDA0002788626670000033
in the formula: pg,tRepresenting the magnitude of the output of the unit at time t, Pg,t-1Representing the output of the unit at time t-1, RuThe climbing limit of the conventional unit is reached.
4. The optimal scheduling method for electric power system considering wind-solar power output prediction error and demand response flexibility of claim 3, wherein in the step 3, the electricity price is corrected based on the intra-day prediction data:
Figure FDA0002788626670000034
in the formula, deltaC is the electricity price variable quantity, and C is the real-time electricity price;
and the relation between the electricity price and the electricity price type load change rate is expressed by adopting price demand elasticity:
λ＝(ΔPt PDR/Pt PDR)/(ΔCt/Ct)
wherein λ is the time period t of the electricity price type load resourceThe influence of electricity price change on electricity price type load response rate; delta Pt PDRRepresenting the power price type load response variation in the t period; delta Ct、CtRepresenting the electricity price variation and the initial electricity price in the t period; delta Pt PDRThe power corresponding to a negative value is the daily scheduled reallocation amount.
5. The power system optimal scheduling method considering wind-solar power generation prediction error and demand response flexibility of claim 4, wherein the intra-day scheduling is performed for wind-solar power generation and photovoltaic power generation prediction with 15min as a time scale.
6. The optimal scheduling method for power system considering wind-solar power output prediction error and demand response flexibility according to claim 4 or 5, wherein in the step 4, a real-time scheduling model is established with a system minimum unbalanced power as a target and 1min as a time scale.
7. The power system optimization scheduling method considering wind-solar output prediction error and demand response flexibility according to any one of claims 1 to 6, wherein the step 4 further adds upper and lower limit constraints of transferable loads and reduction amounts of reducible loads:
Figure FDA0002788626670000041
in the formula: alpha is alphat、γtAn upper limit and a lower limit of a reduction rate indicating that the excitation type load can be reduced or transferred,
Figure FDA0002788626670000042
representing the initial load amount of the load at time t,
Figure FDA0002788626670000043
represents the amount of reduction of the load at the current time, and η is the upper limit of the rate of change of the total amount of the load.
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