CN112989279A - Scheduling method and device of electric heating combined system containing wind power - Google Patents

Scheduling method and device of electric heating combined system containing wind power Download PDF

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CN112989279A
CN112989279A CN201911294551.2A CN201911294551A CN112989279A CN 112989279 A CN112989279 A CN 112989279A CN 201911294551 A CN201911294551 A CN 201911294551A CN 112989279 A CN112989279 A CN 112989279A
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崔岱
徐飞
姚星辰
程特
陈磊
陈群
闵勇
周云海
郭胜凯
陈晓东
葛维春
苏安龙
高凯
葛延峰
李铁
姜枫
张艳军
王钟辉
姜狄
张凯
佟智波
梁鹏
谷博
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Jinzhou Electric Power Supply Co Of State Grid Liaoning Electric Power Supply Co ltd
Tsinghua University
China Three Gorges University CTGU
State Grid Liaoning Electric Power Co Ltd
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Tsinghua University
China Three Gorges University CTGU
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Abstract

The embodiment of the invention provides a scheduling method and device of an electric heating combined system containing wind power. The method comprises the following steps: acquiring wind power output prediction data of a current time period in the day; acquiring corrected predicted data of the wind power output in the day according to the predicted wind power output data of the current time period in the day and a pre-acquired predicted data set of the wind power output in the period; and obtaining the day-ahead scheduling plan of the current time period according to the corrected and predicted data of the day-ahead wind power output, the parameters of each unit and the heat storage device in the electric-heat combined system, the predicted data of the electric load and the heat load of the electric-heat combined system in the current time period and the day-ahead scheduling plan obtained by correcting and scheduling according to the day-ahead scheduling meter in the previous time period in the day. The scheduling method and device for the wind power-containing electric heating combined system provided by the embodiment of the invention can correct the day-ahead scheduling plan obtained by short-term prediction based on wind power uncertainty based on the ultrashort-term prediction data of wind power, and can reduce resource waste.

Description

Scheduling method and device of electric heating combined system containing wind power
Technical Field
The invention relates to the technical field of energy, in particular to a scheduling method and device of an electric heating combined system containing wind power.
Background
The power system scheduling means that under the condition of meeting various technical and safety constraints, the unit operation mode is reasonably arranged, the load requirement is met, the new energy consumption requirement is considered, and a power generation plan meeting the actual, safe and reliable engineering production is obtained in a reasonable time. The power system dispatching model mainly based on the traditional energy cannot completely meet the grid-connected operation requirements of fluctuating renewable energy sources such as wind power and photovoltaic power generation, the flexibility of the power system cannot be fully exerted, and the new energy consumption problems of water abandonment, wind abandonment and light abandonment exist.
For the electric heating combined system containing wind power, a day-ahead scheduling plan is taken as one of important work of optimizing scheduling, and scientific basis is provided for the output of each unit in the electric heating combined system and the heat storage and release strategy of a heat storage device. The prediction precision of the wind power output at the present stage is not high enough, and the prediction error of the wind power is in positive correlation with the time, so that the difference between a single day-ahead scheduling plan and the day-ahead operation output is large, the method cannot be applied to an electric heating combined system containing large-scale wind power, and the resource waste is large.
Disclosure of Invention
The embodiment of the invention provides a scheduling method and a scheduling device of an electric heating combined system containing wind power, which are used for solving or at least partially solving the defect of great resource waste in the prior art.
In a first aspect, an embodiment of the present invention provides a scheduling method for a wind power-containing electric heating combined system, including:
acquiring wind power output prediction data of a current time period in the day;
acquiring corrected predicted data of the wind power output in the day according to the predicted wind power output data of the current time period in the day and a pre-acquired predicted data set of the wind power output in the period;
and acquiring the day-ahead scheduling plan of the current time period according to the corrected and predicted data of the day-ahead wind power output, the parameters of each unit and the heat storage device in the electric-heat combined system, the predicted data of the electric load and the heat load of the current time period of the electric-heat combined system and the day-ahead scheduling plan acquired by correcting and scheduling according to the day-ahead scheduling meter of the previous time period in the day.
Preferably, the specific step of obtaining the corrected predicted data of the solar wind power output according to the predicted wind power output data of the current time period in the day and the pre-obtained predicted data set of the wind power output of the present period includes:
according to the similarity between the wind power output prediction data of the current time period in the day and the preset wind power output data set of the current period and the wind power output data corresponding to the current time period, screening a plurality of groups of wind power output data from the wind power output prediction data set of the current period;
and replacing the wind power output data corresponding to the current time period in the plurality of groups of wind power output data with the wind power output prediction data of the current time period in the day to obtain corrected prediction data of the wind power output in the day.
Preferably, before obtaining the intra-day scheduling plan of the current time period, the method further includes:
and acquiring the day-ahead scheduling plan according to the predicted data set of the wind power output in the period, the parameters of each unit and the heat storage device in the electric-heating combined system, and the predicted data of the electric load and the heat load of the electric-heating combined system in the period.
Preferably, before obtaining the corrected predicted data of the wind power output in the day according to the predicted wind power output data of the current time period in the day and the pre-obtained predicted data set of the wind power output in the present period, the method further includes:
and acquiring a prediction data set of the wind power output of the period according to the prediction data and the actual data of the historical wind power output and the initial prediction data of the wind power output of the period.
Preferably, the specific step of obtaining the schedule plan in the present period according to the prediction data set of the wind power output in the present period, the parameters of each unit and the heat storage device in the electric-heating combined system, and the prediction data of the electric load and the heat load in the present period of the electric-heating combined system includes:
establishing a day-ahead scheduling model according to the predicted data set of the wind power output in the period, the parameters of each unit and the heat storage device in the electric-heating combined system, and the predicted data of the electric load and the heat load of the electric-heating combined system in the period;
acquiring the day-ahead scheduling plan according to an optimization method and the day-ahead scheduling model;
the day-ahead scheduling model aims at maximizing the total yield of the electric-heat combined system in the current period, and takes output constraints and climbing constraints of each unit, operation constraints of the heat storage device and power supply and heat supply balance constraints as constraint conditions.
Preferably, the specific step of obtaining the intraday scheduling plan of the current time period according to the corrected and predicted data of the intraday wind power output, the parameters of each unit and the heat storage device in the electric heating combined system, the predicted data of the electric load and the heat load of the current time period of the electric heating combined system and the diurnal scheduling plan obtained by performing correction scheduling according to the intraday scheduling meter of the previous time period in the day includes:
establishing an intra-day scheduling model according to the corrected and predicted data of the intra-day wind power output, the parameters of each unit and the heat storage device in the electric heating combined system, the predicted data of the electric load and the heat load of the electric heating combined system in the current time period and the day-ahead scheduling plan obtained by correcting and planning according to the intra-day scheduling meter in the previous time period in the day;
acquiring a day scheduling plan of the current time period according to an optimization method and the day scheduling model;
the day scheduling model aims at maximizing the total yield of the electric heating combined system in the period, and takes output constraint and climbing constraint of each unit, operation constraint of the heat storage device and power supply and heat supply balance constraint as constraint conditions.
Preferably, after the obtaining of the day scheduling plan of the current time period, the method further includes:
and according to the day scheduling plan of the current time period, correcting the day-ahead scheduling plan obtained by correcting the schedule according to the day scheduling plan of the previous time period in the day.
In a second aspect, an embodiment of the present invention provides a scheduling device for an electric heating combined system including wind power, including:
the wind power prediction module is used for acquiring wind power output prediction data of the current time period in the day;
the scene correction module is used for acquiring corrected prediction data of the wind power output in the day according to the wind power output prediction data of the current time period in the day and a prediction data set of the wind power output in the period acquired in advance;
and the scheduling acquisition module is used for acquiring the day-ahead scheduling plan of the current time period according to the corrected and predicted data of the day-ahead wind power output, the parameters of each unit and the heat storage device in the electric heating combined system, the predicted data of the electric load and the heat load of the current time period of the electric heating combined system and the day-ahead scheduling plan acquired by correcting and scheduling according to the day-ahead scheduling meter of the previous time period in the day.
In a third aspect, an embodiment of the present invention provides an electronic device, which includes a memory, a processor, and a computer program that is stored in the memory and is executable on the processor, and when the computer program is executed, the steps of the scheduling method for the electric-heat combined system including wind power provided in any one of the various possible implementation manners of the first aspect are implemented.
In a fourth aspect, an embodiment of the present invention provides a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps of the scheduling method for the combined electric and heat system with wind power as provided in any one of the various possible implementations of the first aspect.
According to the scheduling method and device of the wind power-containing electric heating combined system, the day-ahead scheduling plan obtained by short-term prediction of wind power output based on wind power uncertainty is corrected based on the ultrashort-term prediction data of wind power to obtain the day-in scheduling plan of the current time period, so that the day-in scheduling plan is closer to the running output of the current period, and resource waste can be reduced. And the uncertainty of wind power output can be effectively dealt with, the wind power consumption capability can be improved while the operation economy of the electric heating combination system is ensured, and the method has important practical significance and environmental benefit.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
FIG. 1 is a schematic flow chart of a scheduling method of a wind-power-containing electric-heating combined system according to an embodiment of the invention;
FIG. 2 is a schematic structural diagram of a scheduling device of an electric-heating combined system containing wind power provided in an embodiment of the invention;
fig. 3 is a schematic physical structure diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In order to overcome the problems in the prior art, the embodiment of the invention provides a scheduling method and a scheduling device of an electric heating combined system containing wind power.
Fig. 1 is a schematic flow chart of a scheduling method of a wind power-containing electric-heat combined system according to an embodiment of the present invention. As shown in fig. 1, the method includes: and S101, acquiring wind power output prediction data of the current time period in the day.
It should be noted that the electric-heat combined system containing wind power includes a wind power generation unit, a thermal power generation unit and a cogeneration unit containing a heat storage device.
Each day is divided into a plurality of time periods of equal duration, the duration of the time periods not exceeding 4 hours.
The prediction of the wind power output of 0 to 4 hours is ultra-short-term prediction, so that the wind power output prediction data of the current time period in the day can be obtained according to a common wind power output ultra-short-term prediction method.
The current time period in the day refers to the current time period in the cycle.
The wind power output ultra-short term prediction can be carried out according to the preset time resolution, and the wind power output prediction data of the current time period in the day is expressed as a vector s. The temporal resolution is typically greater than 15 minutes.
For example, if the temporal resolution is 1 hour, s ═ pt+1,pt+2,pt+3,pt+4](ii) a And the s represents wind power output prediction data in the next four hours from t to t + 1.
And S102, acquiring corrected predicted data of the daily wind power output according to the predicted wind power output data of the current time period in the day and a pre-acquired predicted data set of the wind power output in the period.
Specifically, the prediction data set of the wind power output of the period includes a plurality of groups of prediction data of the wind power output of the period and the occurrence probability of each group of the prediction data of the wind power output of the period. And each group of the predicted data of the wind power output of the period is obtained according to the initial predicted data of the wind power output of the period. And each set of the predicted data of the wind power output of the period is used as a wind power output scene, so that the predicted data set of the wind power output of the period is a scene set which is generated based on short-term prediction and contains a large number of wind power output scenes.
The initial prediction data of the wind power output in the period is obtained by performing short-term prediction on the basis of the uncertainty of the wind power. And for each point position on the time resolution, the prediction data on the point position is a wind power output interval. And sampling the wind power output interval corresponding to each point position, so that a plurality of groups of prediction data of the wind power output in the period can be obtained.
The prediction of the wind power output of 0 to 72 hours (namely 0 to 3 days) is short-term prediction, so that initial prediction data of the wind power output of the period and a prediction data set of the wind power output of the period can be obtained according to a common wind power output short-term prediction method.
The prediction precision of the wind power output at the present stage is not high enough, the prediction error of the wind power output is in positive correlation with time, the prediction data set of the wind power output at the present period is screened based on the prediction data of the wind power output at the present time period obtained by ultra-short term prediction, the short-term prediction data matched with the prediction data of the wind power output at the present time period in the day is screened out, the screened prediction data of the wind power output at the present period is corrected according to the prediction data of the wind power output at the present time period in the day, and the corrected prediction data of the wind power output at the present period with higher prediction precision is obtained.
Step S103, obtaining a day-ahead scheduling plan of the current time period according to corrected and predicted data of the wind power output in the day, parameters of each unit and a heat storage device in the electric heating combined system, predicted data of electric loads and heat loads of the electric heating combined system in the current time period and corrected and planned according to a day-ahead scheduling meter of the previous time period in the day.
Specifically, a day-ahead scheduling model with the highest total income of the electric heating combined system as an optimization target is established according to corrected and predicted data of the wind power output in the day, parameters of each unit and a heat storage device in the electric heating combined system, predicted data of the electric load and the heat load of the electric heating combined system in the current time period and a day-ahead scheduling plan obtained by correcting and scheduling according to a day-ahead scheduling meter in the last time period in the day.
The dispatching plan is composed of the power output of each unit (thermal power generating unit and cogeneration unit) in the electric-heat combined system, the heat output of the cogeneration unit and the heat storage and discharge power of the heat storage device.
The power output of each unit (thermal power generating unit and cogeneration unit) in the electric-heat combined system, the heat output of the cogeneration unit and the heat storage and release power of the heat storage device are decision variables of the scheduling model in the day.
The day-ahead scheduling plan is a scheduling plan obtained according to the prediction data before the period; the in-day schedule plan is a schedule plan obtained from the prediction data in the cycle and several hours in the future.
In order to better connect the day scheduling plan and the day-ahead scheduling plan, the difference between the day-ahead scheduling plan and the day-ahead scheduling plan needs to be considered in the day scheduling model.
It can be understood that, due to the time correlation of the wind power output, in the intra-day scheduling model, the influence of the intra-day scheduling plan of the previous time period on the intra-day scheduling plan of the current time period needs to be considered, and the pre-day scheduling plan is obtained by correcting according to the intra-day scheduling plan of the previous time period.
According to the optimization method, the intraday scheduling model is solved, the electric output of each unit (thermal power generating unit and cogeneration unit) in the electric-heat combined system in the current time period, the heat output of the cogeneration unit and the heat storage and discharge power of the heat storage device can be obtained and used as the intraday scheduling plan of the current time period.
The embodiment of the invention corrects the day-ahead scheduling plan obtained by short-term prediction of wind power output based on wind power uncertainty based on the ultrashort-term prediction data of the wind power to obtain the day-in scheduling plan of the current time period, so that the day-in scheduling plan is closer to the running output of the period, and the resource waste can be reduced. And the uncertainty of wind power output can be effectively dealt with, the wind power consumption capability can be improved while the operation economy of the electric heating combination system is ensured, and the method has important practical significance and environmental benefit.
Based on the content of each embodiment, the specific step of obtaining the corrected prediction data of the wind power output in the day according to the wind power output prediction data of the current time period in the day and the prediction data set of the wind power output in the period obtained in advance comprises the following steps: and according to the similarity between the wind power output prediction data of the current time period in the day and the preset wind power output prediction data set of the current time period and the wind power output data corresponding to the current time period, screening a plurality of groups of wind power output data from the wind power output prediction data set of the current time period.
Specifically, in the embodiment of the present invention, short-term prediction with a time scale of 1 day is used, and the embodiment of the present invention is described with the duration of the present period being 1 day, and the duration of the present period actually used does not exceed 72 hours. The prediction data (namely a scene) of a certain group of the wind power output of the period in the prediction data set of the wind power output of the period is
Figure BDA0002320147870000081
Wherein K is a scene set.
It should be noted that, in the following description,
Figure BDA0002320147870000091
the time resolution of (1) is 1 hour, the time resolution of the embodiment of the present invention is described as 1 hour, and the actually used time resolution is more than 15 minutes.
The predicted wind power output data of the current time period in the day is s ═ pt+1,pt+2,pt+3,pt+4]The current time period is t to t +1 hour from
Figure BDA0002320147870000092
Intercepting data of a time period (t to t +1 hour) corresponding to s
Figure BDA0002320147870000093
And calculating the similarity between the s and k' vectors to serve as the wind power output prediction data of the current time period in the day and the similarity between the preset wind power output prediction data set of the current period and the wind power output data corresponding to the current time period.
The similarity between the two vectors s and k 'can be obtained by calculating the distance between the vectors such as the euclidean distance between the two vectors s and k'.
Preferably, since the force value at the previous moment of the wind power has a strong correlation with the magnitude at the previous moment, and the correlation shows a gradually decreasing trend as time goes on, the weighted euclidean distance may be used for calculation when calculating the similarity between s and k'.
The similarity between s and k' is calculated by the formula
Figure BDA0002320147870000094
Wherein, ω isiRepresenting the correlation weight coefficient at each time instant in the current time period.
Comparing the similarity between s and k' with a preset threshold epsilon, if the similarity is less than or equal to the preset threshold epsilon, indicating that the similarity between a scene corresponding to the wind power output prediction data of the current time period in the day and the scene k meets the requirement, and screening the group of prediction data of the wind power output of the current period, and marking the group of prediction data as k "; if the similarity is larger than the preset threshold epsilon, the similarity between the scene corresponding to the wind power output prediction data in the current time period in the day and the scene k is not in accordance with the requirement, and the set of prediction data of the wind power output in the period is discarded. Namely, it is
Figure BDA0002320147870000095
Wherein the preset threshold value epsilon is the maximum value of the distance (such as Euclidean distance or weighted Euclidean distance) between the vectors meeting the condition; rhok”Representing the probability of scene k "occurring.
And replacing the wind power output data corresponding to the current time period in the plurality of groups of wind power output data with the wind power output prediction data of the current time period in the day to obtain corrected prediction data of the wind power output in the day.
Specifically, the data of the current time period (t to t +1 hour) in the scene k ″ is predicted by using the wind power output of the current time period in the day as [ p ]t+1,pt+2,pt+3,pt+4]Instead, note the new scene j ═ pk1,…,pkt,pt+1,…,pt+4,…,pk24]And obtaining the probability rho of the occurrence of the scene jj。
Carrying out scene probability normalization to obtain the probability rho of the scene jj：
Figure BDA0002320147870000101
Wherein N isjThe number of groups of the screened predicted data of the wind power output in the period (namely the number of the scenes k ") is used.
According to the method and the device, based on the similarity between the ultra-short-term prediction data and the short-term prediction data of the wind power output, a scene which is closer to the ultra-short-term prediction data of the wind power output is screened out, so that a day-ahead scheduling plan can be corrected based on a screening result, a day-in scheduling plan of a current time period is obtained, the day-in scheduling plan is closer to the running output of the period, and resource waste can be reduced.
Based on the content of each embodiment, before obtaining the intra-day scheduling plan of the current time period, the method further includes the following steps of, according to the corrected prediction data of the intra-day wind power output, the parameters of each unit and the heat storage device in the electric heating combined system, the prediction data of the electric load and the heat load of the electric heating combined system in the current time period, and the pre-day scheduling plan obtained by performing correction scheduling according to the intra-day scheduling meter of the previous time period in the day: and obtaining a day-ahead scheduling plan according to the predicted data set of the wind power output in the period, the parameters of each unit and the heat storage device in the electric-heating combined system, and the predicted data of the electric load and the heat load of the electric-heating combined system in the period.
Specifically, before step S102, a day-ahead scheduling model with the highest total profit of the electric-heating combined system as an optimization target is established according to the prediction data set of the wind power output in the present period, the parameters of each unit and the heat storage device in the electric-heating combined system, and the prediction data of the electric load and the heat load in the present period of the electric-heating combined system.
The power output of each unit (thermal power generating unit and cogeneration unit) in the electric-heat combined system, the heat output of the cogeneration unit and the heat storage and release power of the heat storage device are decision variables of the day-ahead scheduling model.
According to the optimization method, the day-ahead scheduling model is solved, so that the electric power output of each unit (thermal power generating unit and cogeneration unit) in the electric-heat combined system at each moment in the period, the heat output of the cogeneration unit and the heat storage and release power of the heat storage device can be obtained and used as a day-ahead scheduling plan.
According to the embodiment of the invention, through each scene obtained based on the wind power uncertainty, the day-ahead scheduling plan closer to the running output of the period can be obtained, so that the day-ahead scheduling plan can be corrected based on the day-ahead scheduling plan to obtain the day-in scheduling plan closer to the running output of the period, and the resource waste can be reduced.
Based on the content of each embodiment, before acquiring the corrected predicted data of the wind power output in the day according to the predicted wind power output data of the current time period in the day and the predicted data set of the wind power output in the present period acquired in advance, the method further includes: and acquiring a prediction data set of the wind power output of the period according to the prediction data and the actual data of the historical wind power output and the initial prediction data of the wind power output of the period.
Specifically, an empirical error distribution function of the wind power plant is obtained on the basis of prediction data and actual data of historical wind power output.
In consideration of the uncertainty of wind power, the empirical error distribution function of the electric field can be simulated according to a Monte Carlo (Monte Carlo) method, and a wind power prediction error sequence is obtained.
And combining the obtained wind power prediction error sequence with the initial predicted wind power to obtain the wind power predicted output in each time period under a certain scene, and obtaining the scene (namely the set of predicted data of the wind power output in the period).
By repeating the steps, a prediction data set of the wind power output of the period can be obtained, wherein the prediction data set comprises a plurality of groups of prediction data of the wind power output of the period and the occurrence probability of the prediction data of the wind power output of each group of the period.
According to the embodiment of the invention, the prediction data set of the wind power output in the period is obtained according to the prediction data and the actual data of the historical wind power output and the initial prediction data of the wind power output in the period, the uncertainty of the wind power is considered, and a scene set closer to the actual scene set can be obtained, so that an intra-day scheduling plan closer to the operation output in the period can be obtained based on the scene set, and the resource waste can be reduced.
Based on the content of each embodiment, the specific steps of obtaining the day-ahead scheduling plan according to the prediction data set of the wind power output in the period, the parameters of each unit and the heat storage device in the electric-heating combined system, and the prediction data of the electric load and the heat load in the electric-heating combined system in the period include: and establishing a day-ahead scheduling model according to the predicted data set of the wind power output in the period, the parameters of each unit and the heat storage device in the electric-heating combined system and the predicted data of the electric load and the heat load in the period of the electric-heating combined system. The day-ahead scheduling model takes the total income of the cycle of the maximum power and heat combined system as a target, and takes the output constraint and the climbing constraint of each unit, the operation constraint of the heat storage device and the power supply and heat supply balance constraint as constraint conditions.
Specifically, before the day-ahead scheduling plan is obtained, parameters of each unit and a heat storage device in the electric heating combined system are obtained.
The parameters of each unit and the heat storage device in the electric heating combined system comprise: total number N of thermal power generating unitsFNumber i of thermal power generating units and maximum active power output P of each thermal power generating uniti,maxAnd minimum active power output Pi,minMaximum upward ramp rate of each thermal power generating unit
Figure BDA0002320147870000121
And maximum downward ramp rate ri downCoefficient of consumption constant of thermal power generating unit iciCoefficient of first order term biCoefficient of quadratic term ai(ii) a Total number N of cogeneration units including heat storage devicesRThe number j of the cogeneration units containing the heat storage device and the maximum active power P of each cogeneration unit under the pure condensation working conditionj,maxMinimum active power output Pj,minMaximum heating power Qj,maxAnd minimum heating power Qj,minMaximum upward climbing speed of each cogeneration unit under unit pure condensation working condition
Figure BDA0002320147870000122
And maximum downward ramp rate
Figure BDA0002320147870000123
Consumption constant coefficient c of cogeneration unit jjCoefficient of first order term bjCoefficient of quadratic term ajMaximum rate of heat storage of the heat storage device
Figure BDA0002320147870000124
And maximum heat release rate
Figure BDA0002320147870000125
Maximum heat storage quantity S of heat storage devicej,max(ii) a Obtaining an electrical load demand P for a time period tload,tAnd heat load demand Qload,t。
In the day-ahead scheduling model, the total income of the electric heating combined system in the current period is obtained by subtracting the power generation cost of the units in the electric heating combined system from the power supply and heat supply income of the electric heating combined system.
The objective function of the day-ahead scheduling model is
Figure BDA0002320147870000131
Wherein k represents the kth wind power output scene (abbreviated as "scene"); n is a radical ofkRepresenting the number of wind power scenes; rhokRepresenting the probability of occurrence of the kth wind power scene; t represents a day-ahead scheduling period;
Figure BDA0002320147870000132
representing the sum of the power supply and heat supply benefits of the system in the t period under the k wind power output scene;
Figure BDA0002320147870000133
representing the sum of the power generation cost and the heat supply cost of the system at the t time period in the kth wind power output scene;
Figure BDA0002320147870000134
and (4) representing the penalty cost of the t period in the k wind power output scene.
Figure BDA0002320147870000135
Wherein the content of the first and second substances,
Figure BDA0002320147870000136
representing the power generation power of the thermal power generating unit i at the t time period in the kth wind power output scene;
Figure BDA0002320147870000137
representing the power generation power of the cogeneration unit j at the t time period in the k wind power output scene; pitRepresenting the online electricity price of the combined system in the t period; mu.stThe unit heat supply price of the electric heating combined system in the t period is represented;
Figure BDA0002320147870000138
the total power generation cost of the thermal power generating unit in the t-time period under the k-th wind power output scene is represented;
Figure BDA0002320147870000139
the total power generation cost of the cogeneration unit at the t time period under the k wind power output scene is represented;
Figure BDA00023201478700001310
representing the air abandon quantity of the combined system in the time period t;
Figure BDA00023201478700001311
representing the load shedding amount of the combined system in the t period; gamma represents the punishment cost of unit abandoned wind; beta represents the penalty cost per load shedding.
Figure BDA00023201478700001312
Figure BDA00023201478700001313
Wherein the content of the first and second substances,
Figure BDA0002320147870000141
representing the heat supply power of a cogeneration unit j at a time t under a k wind power output scene;
Figure BDA0002320147870000142
the storage and heat release power (in) of the heat storage device corresponding to the cogeneration unit j in the period t under the k wind power output scene is shown
Figure BDA0002320147870000143
Negative values for exotherm); cv,jRepresenting unit operation parameters of the combined heat and power generation unit j;
Figure BDA0002320147870000144
representing the maximum output of the wind power plant at the t time period under the k wind power output scene;
Figure BDA0002320147870000145
and representing the actual output of the wind power plant at the t time period under the k wind power output scene.
The constraint conditions of the day-ahead scheduling model comprise:
thermal power generating unit output constraint condition
Figure BDA0002320147870000146
Thermal power generating unit climbing constraint condition
Figure BDA0002320147870000147
Wherein, delta t represents the output adjustment time of the thermal power generating unit;
wind power plant output constraint condition
Figure BDA0002320147870000148
Output condition of cogeneration unit
The embodiment of the invention considers the air extraction type cogeneration unit, the cogeneration of the cogeneration unit has coupling constraint, and when the cogeneration is fixed, the electric power of the unit is limited in a specific interval as follows:
Figure BDA0002320147870000149
wherein k is1、k2、k3Respectively representing the operating parameters of the thermoelectric unit; pmRepresenting the minimum generating power of the unit when air extraction exists; qmAnd the heat supply power corresponding to the minimum generating power of the unit when air extraction exists is shown.
Climbing condition of cogeneration unit
The output change of the combined heat and power unit is determined by the air extraction amount, so the climbing speed of the combined heat and power unit can uniformly convert the electric and thermal output into electric power constraint under the unit pure condensation working condition:
Figure BDA0002320147870000151
wherein the content of the first and second substances,
Figure BDA0002320147870000152
and the electric power of the cogeneration unit j under the k wind power output scene is converted into the electric power under the pure condensing working condition in the t time period.
Constraint condition of heat storage and discharge power of heat storage device
Figure BDA0002320147870000153
Capacity constraint condition of heat storage device
Figure BDA0002320147870000154
Wherein the content of the first and second substances,
Figure BDA0002320147870000155
and the heat storage amount of the heat storage device configured by the unit j in the period t under the k wind power output scene is shown.
Heat storage device state constraints
Figure BDA0002320147870000156
Wherein the content of the first and second substances,
Figure BDA0002320147870000157
indicating the rate of heat loss from the heat storage device.
Cycle constraint of heat storage device
Figure BDA0002320147870000158
And obtaining a day-ahead scheduling plan according to the optimization method and the day-ahead scheduling model.
Specifically, according to the optimization method, the day-ahead scheduling model is solved, and the electric power output of each unit (thermal power generating unit and cogeneration unit) in the electric-heat combined system at each moment in the period, the thermal power output of the cogeneration unit and the heat storage and release power of the heat storage device can be obtained and used as a day-ahead scheduling plan.
According to the embodiment of the invention, through each scene obtained based on the wind power uncertainty, the day-ahead scheduling plan closer to the running output of the period can be obtained, so that the day-ahead scheduling plan can be corrected based on the day-ahead scheduling plan to obtain the day-in scheduling plan closer to the running output of the period, and the resource waste can be reduced.
Based on the content of each embodiment, the specific step of obtaining the intra-day scheduling plan of the current time period includes the following steps: and establishing an intra-day scheduling model according to the corrected and predicted data of the wind power output in the day, the parameters of each unit and the heat storage device in the electric heating combined system, the predicted data of the electric load and the heat load of the electric heating combined system in the current time period and the day-ahead scheduling plan obtained by correcting and scheduling according to the intra-day scheduling meter in the previous time period in the day.
The day scheduling model takes the total income of the cycle of the maximum power and heat combined system as a target, and takes the output constraint and the climbing constraint of each unit, the operation constraint of the heat storage device and the power supply and heat supply balance constraint as constraint conditions.
It is understood that step S103 further includes obtaining parameters of each unit and heat storage device in the cogeneration system.
The parameters of each unit and the heat storage device in the electric heating combined system comprise: total number N of thermal power generating unitsFNumber i of thermal power generating units and maximum active power output P of each thermal power generating uniti,maxAnd minimum active power output Pi,minMaximum upward ramp rate r of each thermal power generating uniti upAnd maximum downward ramp rate ri downConstant coefficient of consumption c of thermal power generating unit iiCoefficient of first order term biCoefficient of quadratic term ai(ii) a Total number N of cogeneration units including heat storage devicesRThe number j of the cogeneration units containing the heat storage device and the maximum active power P of each cogeneration unit under the pure condensation working conditionj,maxMinimum active power output Pj,minMaximum heating power Qj,maxAnd minimum heating power Qj,minMaximum upward climbing speed of each cogeneration unit under unit pure condensation working condition
Figure BDA0002320147870000161
And maximum downward ramp rate
Figure BDA0002320147870000162
Consumption constant coefficient c of cogeneration unit jjCoefficient of first order term bjCoefficient of quadratic term ajMaximum rate of heat storage of the heat storage device
Figure BDA0002320147870000163
And maximum heat release rate
Figure BDA0002320147870000164
Maximum heat storage quantity S of heat storage devicej,max(ii) a Obtaining an electrical load demand P for a time period tload,tAnd heat load demand Qload,t。
In the day scheduling model, the total income of the electric heating combined system in the period is obtained by subtracting the power generation cost and the punishment cost of the unit in the electric heating combined system from the power supply and heat supply income of the electric heating combined system.
The objective function of the scheduling model in days is
Figure BDA0002320147870000171
Wherein the content of the first and second substances,
Figure BDA0002320147870000172
and respectively representing the power supply and heat supply income, the power generation and heat supply cost, the wind abandoning and load shedding penalty cost and the deviation penalty with a scheduling plan before the day of the system at the t period under the jth wind power output scene.
Figure BDA0002320147870000173
Wherein the content of the first and second substances,
Figure BDA0002320147870000174
respectively representing the total generated power of a scheduling plan in a period t and a scheduling plan before the day under the jth wind power output scene; ζ represents the unit power deviation penalty cost.
It should be noted that the formulas of the power supply and heat supply benefit, the power generation and heat supply cost, the wind abandoning and load shedding punishment cost and the constraint conditions of the scheduling model in the day are similar to the formulas of the power supply and heat supply benefit, the power generation and heat supply cost, the wind abandoning and load shedding punishment cost constraint conditions of the scheduling model in the day, and the difference is that the scheduling model in the day is based on N before screeningkIndividual scenario, and intra-day scheduling model is based on the screened NjThe scene (2). Therefore, the power supply and heat supply benefit, the power generation and heat supply cost, the wind curtailment and load shedding penalty cost, and the constraint conditions of the in-day scheduling model can be referred to the embodiment of the in-day scheduling model, and are not described herein again.
And acquiring the day scheduling plan of the current time period according to the optimization method and the day scheduling model.
Specifically, according to the optimization method, the intraday scheduling model is solved, and the electric power output of each unit (thermal power generating unit and cogeneration unit) in the electric-heat combined system in the current time period, the thermal power output of the cogeneration unit and the heat storage and release power of the heat storage device can be obtained and used as the intraday scheduling plan of the current time period.
The embodiment of the invention corrects the day-ahead scheduling plan obtained by short-term prediction of wind power output based on wind power uncertainty based on the ultrashort-term prediction data of the wind power to obtain the day-in scheduling plan of the current time period, so that the day-in scheduling plan is closer to the running output of the period, and the resource waste can be reduced.
Based on the content of the foregoing embodiments, after acquiring the intra-day scheduling plan of the current time period, the method further includes: and according to the day scheduling plan of the current time period, correcting the day-ahead scheduling plan obtained by correcting the schedule according to the day scheduling plan of the previous time period in the day.
Specifically, after the intra-day scheduling plan of the current time period is acquired, the pre-day scheduling plan acquired by performing the correction according to the intra-day scheduling meter of the previous time period in the day is corrected, specifically, a part of the current time period in the pre-day scheduling plan acquired by performing the correction according to the intra-day scheduling meter of the previous time period in the day is replaced by the intra-day scheduling plan of the current time period.
By obtaining the day scheduling plan of each time period in the period until the whole day-ahead scheduling period is finished, the day scheduling plan of the electric heating combined system can be obtained.
It can be understood that the value of each decision variable in the last time period in the present cycle is used as the boundary condition for scheduling in the next cycle.
According to the embodiment of the invention, the day-ahead scheduling plan obtained by correcting the day-ahead scheduling plan according to the day-ahead scheduling plan of the current time period and the day-ahead scheduling plan obtained by correcting the day-ahead scheduling plan of the previous time period in the day can be obtained, the day-ahead scheduling plan of the next time period which is closer to the running output of the period can be obtained, and the resource waste can be reduced.
Fig. 2 is a schematic structural diagram of a scheduling device of an electric-heat combined system including wind power provided in an embodiment of the present invention. Based on the content of the foregoing embodiments, as shown in fig. 2, the apparatus includes a wind power prediction module 201, a scene correction module 202, and a scheduling acquisition module 203, where:
the wind power prediction module 201 is used for acquiring wind power output prediction data of the current time period in the day;
the scene correction module 202 is configured to obtain corrected predicted data of the wind power output in the day according to the predicted wind power output data of the current time period in the day and a pre-obtained predicted data set of the wind power output in the present period;
the scheduling obtaining module 203 is configured to obtain the intra-day scheduling plan of the current time period according to the corrected and predicted data of the intra-day wind power output, the parameters of each unit and the heat storage device in the electric-heat combined system, the predicted data of the electric load and the heat load of the electric-heat combined system in the current time period, and the intra-day scheduling plan obtained by performing correction scheduling according to the intra-day scheduling meter in the previous time period in the day.
Specifically, the wind power prediction module 201, the scene correction module 202 and the scheduling acquisition module 203 are electrically connected in sequence.
The wind power prediction module 201 may obtain wind power output prediction data of the current time period in the day according to a common ultra-short wind power output prediction method, or directly obtain wind power output prediction data of the current time period in the day obtained by a wind power output prediction tool.
The scene correction module 202 screens the prediction data set of the wind power output of the current period based on the wind power output prediction data of the current period in the day obtained by the ultra-short term prediction, screens out short-term prediction data matched with the wind power output prediction data of the current period in the day, corrects the screened prediction data of the wind power output of the current period according to the wind power output prediction data of the current period in the day, and obtains corrected prediction data of the wind power output of the day with higher prediction accuracy.
The scheduling acquisition module 203 establishes an in-day scheduling model taking the highest total income of the electric heating combined system as an optimization target according to corrected prediction data of the in-day wind power output, parameters of each unit and a heat storage device in the electric heating combined system, prediction data of electric load and heat load of the electric heating combined system in the current time period and a day-ahead scheduling plan obtained by performing correction scheduling according to an in-day scheduling meter in the previous time period in the day; according to the optimization method, the intraday scheduling model is solved, the electric output of each unit (thermal power generating unit and cogeneration unit) in the electric-heat combined system in the current time period, the heat output of the cogeneration unit and the heat storage and discharge power of the heat storage device can be obtained and used as the intraday scheduling plan of the current time period.
The specific method and flow for realizing the corresponding functions of each module included in the scheduling device of the electric heating combined system containing wind power provided by the embodiments of the present invention are described in detail in the embodiments of the scheduling method of the electric heating combined system containing wind power, and are not described herein again.
The scheduling device of the electric heating combined system containing the wind power is used for the scheduling method of the electric heating combined system containing the wind power in each embodiment. Therefore, the description and definition in the scheduling method of the electric-heating combined system including wind power in the foregoing embodiments can be used for understanding the execution modules in the embodiments of the present invention.
The embodiment of the invention corrects the day-ahead scheduling plan obtained by short-term prediction of wind power output based on wind power uncertainty based on the ultrashort-term prediction data of the wind power to obtain the day-in scheduling plan of the current time period, so that the day-in scheduling plan is closer to the running output of the period, and the resource waste can be reduced.
Fig. 3 is a schematic physical structure diagram of an electronic device according to an embodiment of the present invention. Based on the content of the above embodiment, as shown in fig. 3, the electronic device may include: a processor (processor)301, a memory (memory)302, and a bus 303; wherein, the processor 301 and the memory 302 complete the communication with each other through the bus 303; the processor 301 is configured to invoke computer program instructions stored in the memory 302 and executable on the processor 301 to perform the scheduling method of the combined electric and heat system including wind power provided by the above-mentioned embodiments of the method, including: acquiring wind power output prediction data of a current time period in the day; acquiring corrected predicted data of the wind power output in the day according to the predicted wind power output data of the current time period in the day and a pre-acquired predicted data set of the wind power output in the period; obtaining the day scheduling plan of the current time period according to the corrected and predicted data of the day wind power output, the parameters of each unit and the heat storage device in the electric heating combined system, the predicted data of the electric load and the heat load of the electric heating combined system in the current time period and the day-ahead scheduling plan obtained by correcting and planning according to the day scheduling meter of the previous time period in the day
Another embodiment of the present invention discloses a computer program product, which includes a computer program stored on a non-transitory computer readable storage medium, the computer program includes program instructions, and when the program instructions are executed by a computer, the computer can execute the scheduling method of the combined electric and heat system including wind power provided by the above-mentioned method embodiments, for example, the method includes: acquiring wind power output prediction data of a current time period in the day; acquiring corrected predicted data of the wind power output in the day according to the predicted wind power output data of the current time period in the day and a pre-acquired predicted data set of the wind power output in the period; obtaining the day scheduling plan of the current time period according to the corrected and predicted data of the day wind power output, the parameters of each unit and the heat storage device in the electric heating combined system, the predicted data of the electric load and the heat load of the electric heating combined system in the current time period and the day-ahead scheduling plan obtained by correcting and planning according to the day scheduling meter of the previous time period in the day
Furthermore, the logic instructions in the memory 302 may be implemented in software functional units and stored in a computer readable storage medium when sold or used as a stand-alone product. Based on such understanding, the technical solutions of the embodiments of the present invention may be essentially implemented or make a contribution to the prior art, or may be implemented in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the methods of the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
Another embodiment of the present invention provides a non-transitory computer-readable storage medium, where the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions enable a computer to execute the scheduling method of the electric-heat combined system including wind power provided in the foregoing method embodiments, for example, the method includes: acquiring wind power output prediction data of a current time period in the day; acquiring corrected predicted data of the wind power output in the day according to the predicted wind power output data of the current time period in the day and a pre-acquired predicted data set of the wind power output in the period; obtaining the day scheduling plan of the current time period according to the corrected and predicted data of the day wind power output, the parameters of each unit and the heat storage device in the electric heating combined system, the predicted data of the electric load and the heat load of the electric heating combined system in the current time period and the day-ahead scheduling plan obtained by correcting and planning according to the day scheduling meter of the previous time period in the day
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. It is understood that the above-described technical solutions may be embodied in the form of a software product, which may be stored in a computer-readable storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., and includes several instructions for enabling a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method of the above-described embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims (10)

1. A scheduling method of a wind power-containing electric heating combined system is characterized by comprising the following steps:
acquiring wind power output prediction data of a current time period in the day;
acquiring corrected predicted data of the wind power output in the day according to the predicted wind power output data of the current time period in the day and a pre-acquired predicted data set of the wind power output in the period;
and acquiring the day-ahead scheduling plan of the current time period according to the corrected and predicted data of the day-ahead wind power output, the parameters of each unit and the heat storage device in the electric-heat combined system, the predicted data of the electric load and the heat load of the current time period of the electric-heat combined system and the day-ahead scheduling plan acquired by correcting and scheduling according to the day-ahead scheduling meter of the previous time period in the day.
2. The scheduling method of the wind-power-containing electric-heat combined system according to claim 1, wherein the specific step of obtaining the corrected predicted data of the wind power output in the day according to the predicted wind power output data of the current time period in the day and the pre-obtained predicted data set of the wind power output in the period comprises:
according to the similarity between the wind power output prediction data of the current time period in the day and the preset wind power output data set of the current period and the wind power output data corresponding to the current time period, screening a plurality of groups of wind power output data from the wind power output prediction data set of the current period;
and replacing the wind power output data corresponding to the current time period in the plurality of groups of wind power output data with the wind power output prediction data of the current time period in the day to obtain corrected prediction data of the wind power output in the day.
3. The scheduling method of the electric-heating combined system with wind power of claim 1, wherein before the obtaining of the intra-day scheduling plan of the current time period, the method further comprises the following steps of, according to the corrected predicted data of the intra-day wind power output, the parameters of each unit and the heat storage device in the electric-heating combined system, the predicted data of the electric load and the heat load of the current time period of the electric-heating combined system, and the pre-day scheduling plan obtained by performing the correction scheduling according to the intra-day scheduling of the previous time period in the day:
and acquiring the day-ahead scheduling plan according to the predicted data set of the wind power output in the period, the parameters of each unit and the heat storage device in the electric-heating combined system, and the predicted data of the electric load and the heat load of the electric-heating combined system in the period.
4. The scheduling method of the wind-power-containing electric-heat combined system according to claim 1, wherein before the obtaining of the corrected predicted data of the wind power output in the day according to the predicted wind power output data of the current time period in the day and the pre-obtained predicted data set of the wind power output in the present period, the scheduling method further comprises:
and acquiring a prediction data set of the wind power output of the period according to the prediction data and the actual data of the historical wind power output and the initial prediction data of the wind power output of the period.
5. The scheduling method of the wind-power-containing electric-heat combined system according to claim 3, wherein the specific step of obtaining the day-ahead scheduling plan according to the prediction data set of the wind power output in the present period, the parameters of each unit and the heat storage device in the electric-heat combined system, and the prediction data of the electric load and the heat load in the present period of the electric-heat combined system comprises:
establishing a day-ahead scheduling model according to the predicted data set of the wind power output in the period, the parameters of each unit and the heat storage device in the electric-heating combined system, and the predicted data of the electric load and the heat load of the electric-heating combined system in the period;
acquiring the day-ahead scheduling plan according to an optimization method and the day-ahead scheduling model;
the day-ahead scheduling model aims at maximizing the total yield of the electric-heat combined system in the current period, and takes output constraints and climbing constraints of each unit, operation constraints of the heat storage device and power supply and heat supply balance constraints as constraint conditions.
6. The scheduling method of the electric-heat combined system with wind power of claim 1, wherein the specific step of obtaining the day-ahead scheduling plan of the current time period according to the corrected predicted data of the day-ahead wind power output, the parameters of each unit and the heat storage device in the electric-heat combined system, the predicted data of the electric load and the heat load of the current time period of the electric-heat combined system, and the day-ahead scheduling plan obtained by performing the correction scheduling according to the day-ahead scheduling of the previous time period in the day comprises:
establishing an intra-day scheduling model according to the corrected and predicted data of the intra-day wind power output, the parameters of each unit and the heat storage device in the electric heating combined system, the predicted data of the electric load and the heat load of the electric heating combined system in the current time period and the day-ahead scheduling plan obtained by correcting and planning according to the intra-day scheduling meter in the previous time period in the day;
acquiring a day scheduling plan of the current time period according to an optimization method and the day scheduling model;
the day scheduling model aims at maximizing the total yield of the electric heating combined system in the period, and takes output constraint and climbing constraint of each unit, operation constraint of the heat storage device and power supply and heat supply balance constraint as constraint conditions.
7. The scheduling method of the wind-power-containing electric-heat combined system according to any one of claims 1 to 6, wherein after the obtaining of the day scheduling plan of the current time period, the scheduling method further comprises:
and according to the day scheduling plan of the current time period, correcting the day-ahead scheduling plan obtained by correcting the schedule according to the day scheduling plan of the previous time period in the day.
8. The utility model provides a scheduling device of electric heat combined system who contains wind-powered electricity generation which characterized in that includes:
the wind power prediction module is used for acquiring wind power output prediction data of the current time period in the day;
the scene correction module is used for acquiring corrected prediction data of the wind power output in the day according to the wind power output prediction data of the current time period in the day and a prediction data set of the wind power output in the period acquired in advance;
and the scheduling acquisition module is used for acquiring the day-ahead scheduling plan of the current time period according to the corrected and predicted data of the day-ahead wind power output, the parameters of each unit and the heat storage device in the electric heating combined system, the predicted data of the electric load and the heat load of the current time period of the electric heating combined system and the day-ahead scheduling plan acquired by correcting and scheduling according to the day-ahead scheduling meter of the previous time period in the day.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the scheduling method of the combined electric and heat system including wind power of any of claims 1 to 7 when executing the program.
10. A non-transitory computer readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements the steps of the scheduling method of an electric-heat combined system with wind power according to any one of claims 1 to 7.
CN201911294551.2A 2019-12-16 2019-12-16 Scheduling method and device of electric heating combined system containing wind power Active CN112989279B (en)

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