CN111626470A - Electric heating comprehensive coordination optimization scheduling method and system - Google Patents
Electric heating comprehensive coordination optimization scheduling method and system Download PDFInfo
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Abstract
The invention provides an electric heating comprehensive coordination optimization scheduling method, which comprises the following steps: screening the pre-acquired data of the error probability distribution and calculating scene probability; inputting the screened data into a pre-constructed multi-scene unit combination model for solving based on the scene probability, and determining the on-off condition of the unit; solving a pre-constructed scheduling plan model based on the determined startup and shutdown conditions of the unit and the wind power prediction scene to obtain a scheduling strategy; the multi-scene unit combination model determines the starting and stopping conditions of each unit by taking the minimum total power generation cost of the system as a target under the condition of considering scene types and the occurrence probability of each type of scene; the scheduling plan model is used for predicting the minimum and maximum output of the thermoelectric generator set under each scene based on wind power and calculating the total power generation cost of the system; through the multi-scene unit combination model and the scheduling plan model, the reasonability and the accuracy of the thermoelectric unit limit value setting are improved, and more scientific margin is reserved for further optimization in subsequent scheduling.
Description
Technical Field
The invention belongs to the field of electric power system scheduling plans, and relates to an electric heating comprehensive coordination optimization scheduling method and system.
Background
In some areas, resources such as wind energy, coal and the like are rich, most of thermoelectric units in the power supply are heat supply units, the output adjustment range is obviously reduced in the heat supply period, meanwhile, the heat load demand in the heating period in winter is high, and the thermoelectric units basically do not participate in peak regulation due to 'fixing power by heat', so that the peak regulation capacity of the system is reduced; and the wind energy resource is abundant in winter, the output of the wind energy resource is in a reverse peak regulation characteristic, and under the combined action of the two factors, the wind power is generated greatly in the valley period, the peak regulation capacity of a power grid is insufficient, the grid-connected capacity of the wind power is limited, and the wind abandon is caused. The traditional scheduling operation mode cannot meet the requirements of promoting optimal economic benefits and reducing pollution emission, needs to deeply research the operation characteristics of a cogeneration unit urgently, fully considers scheduling operation under large-scale wind power access, improves wind power consumption capacity and thermoelectric unit energy efficiency, considers heat supply demand and power grid safety, and improves system peak regulation capacity and electricity-heat comprehensive scheduling level.
The method for determining the power by heat as a basic guideline for developing cogeneration requires that in the design of a thermal power plant, the capacities and types of an auxiliary furnace and a steam turbine generator set are selected according to the heating heat load, and the power is taken as a heat supply byproduct while the continuous and stable operation of a heat supply system is met, so that the situation that the power generation amount is excessive in the low-valley period of the power load of a power grid and is insufficient in the peak-peak period of the power load of the power grid is caused to a certain extent, and the problem of the peak-valley difference of the power load of the power grid is not favorably solved. The generated power limit of the combined heat and power transport unit is usually set monthly in a plurality of network provinces currently, but the actual heat and power ratio and the combined heat and power efficiency of the unit cannot meet the national regulation requirements due to the fact that the actual outward heat supply load of each unit is greatly different, the generating space of a large-capacity high-efficiency unit is occupied in the valley period, and the safe operation level and the overall energy-saving and environment-friendly level of a power grid are affected.
Disclosure of Invention
The invention provides an electric heating comprehensive coordination optimization scheduling method, which aims to solve the problems that the peak regulation capacity of a power grid is insufficient due to large wind power generation in the off-peak period and the wind power grid-connected capacity is limited and the wind abandon is caused, and specifically comprises the following steps:
screening the pre-acquired data of the error probability distribution, and calculating scene probability;
inputting the screened data into a pre-constructed multi-scene unit combination model for solving based on the scene probability, and determining the on-off condition of the unit;
solving a pre-constructed scheduling plan model based on the determined startup and shutdown conditions of the unit and the wind power prediction scene to obtain a scheduling strategy;
the multi-scene unit combination model determines the starting and stopping conditions of each unit by taking the minimum total power generation cost of the system as a target under the condition of considering scene types and the occurrence probability of each type of scene;
and the scheduling plan model is used for calculating the total power generation cost of the system by taking the minimum output and the maximum output of the thermoelectric generator set as targets under various scenes based on wind power prediction.
Preferably, the building of the multi-scenario unit combination model includes:
considering scene types and the occurrence probability of each type of scene, and constructing an objective function by taking the minimum total power generation cost of the system as a target;
and under each scene, a constraint condition is constructed by unit characteristic constraint, network security constraint and power balance constraint.
Preferably, the calculation formula of the objective function of the multi-scenario unit combination model is as follows:
f is the total power generation cost of the system, including the unit starting cost and the coal consumption cost, I is the number of thermal power generating units, T is the total scheduling time interval number, SCi，tFor the starting cost of the ith unit in the t period ui，tFor the operation state of the ith unit in the t period, 1 is starting up, 0 is stopping, and N issAs the number of scenes, psIs the probability of scene s, ui，t-1The operation state of the ith unit in the t-1 th time interval is 1, starting up,
and the coal consumption cost of the ith unit in the s-th scene and the t-th time period is obtained.
Preferably, the coal consumption cost of the ith unit in the s-th scene and the t-th time period
Is calculated as follows:
in the formula (I), the compound is shown in the specification,
is the power value of the ith set in the t time interval and s scene, ai、bi、ciAnd the coal consumption cost coefficient of the unit i is obtained.
Preferably, the dispatch plan model includes:
determining the minimum output of the thermoelectric generator set based on the selected wind power prediction maximum scene, and calculating the total power generation cost of the system;
determining the maximum output of the thermoelectric generator set based on the selected wind power prediction minimum scene, and calculating the total power generation cost of the system;
and determining the normal output of the thermoelectric generator set based on the selected normal wind power prediction scene, and calculating the total power generation cost of the system.
Preferably, the scheduling plan model of the wind power prediction maximum scene includes:
the minimum output of the thermoelectric unit is taken as a target;
the method takes the upper limit and the lower limit of the electric heating coupling output, the electric power and electric quantity balance, the unit output constraint, the climbing constraint and the power grid safety as constraint conditions.
Preferably, the calculation formula of the objective function of the minimum output of the thermoelectric power unit is as follows:
wherein F is the total power generation cost of the system, FCi，tRepresents the coal consumption cost, QF 'of the ith thermoelectric unit in the t period'i，tSelecting the wind curtailment power of a wind power plant j in a time period t when the wind power forecast is in the maximum scene, wherein gamma is a penalty factor, Ck，tIs the peak regulation power of the peak regulation thermal power generating unit k in the period t, ηk，tIs peak shavingAnd (4) peak regulation quotation of the thermal power generating unit k in a period T, wherein I, J, K are the number of the thermal power generating units, the wind power plant and the peak regulation units respectively, and T is an operation period.
Preferably, the scheduling plan model of the wind power prediction minimum scene includes:
the maximum output of the thermoelectric unit is taken as a target; the method takes the upper limit and the lower limit of the electric heating coupling output, the electric power and electric quantity balance, the unit output constraint, the climbing constraint and the power grid safety as constraint conditions.
Preferably, the calculation formula of the objective function of the maximum output of the thermoelectric power unit is as follows:
in the formula, QFi，tSelecting the wind curtailment power of the wind power plant j in the t time period when the wind power forecast is in the minimum scene.
Preferably, the scheduling plan model for the normal wind power prediction scenario includes:
constructing a target function of the normal output of the thermoelectric generator set based on the determined startup and shutdown conditions of the generator set and the wind power prediction normal scene;
determining constraint conditions for the target function of the normal output;
wherein the constraint condition comprises: the method comprises the following steps of electric-thermal coupling constraint upper and lower output limit constraint, electric power and electric quantity balance constraint, unit output constraint, climbing constraint and power grid safety constraint.
Preferably, the calculation formula of the objective function of the normal output of the thermoelectric unit is as follows:
of formula (II) FC'i，tThe method comprises the steps of representing the coal consumption cost of the ith thermal power generating unit (including the thermal power generating unit) in the t-th time period under a normal scene, wherein I is the total number of the thermal power generating units, QF' I, and t is the wind abandoning power of a wind power plant j in the t-th time period when the wind power prediction normal scene is selected.
Preferably, the scene probability is calculated as follows:
calculating a wind power output value at the predicted time t based on a predicted value and a predicted error of historical statistics, and obtaining a plurality of predicted wind power states at the time t based on the predicted wind power output value and an actual wind power variation range;
inputting a plurality of t-moment predicted wind power states and t-1-moment predicted wind power states into a state transition matrix to obtain the probability of the t-moment predicted wind power states transitioning to the t-moment predicted wind power states within a preset confidence level, and arranging the t-moment predicted wind power states from large to small according to the probability;
and calculating to obtain a multi-period wind power state scene and scene probability by utilizing the state transition matrix based on the calculation of the predicted wind power state transition from the t-1 moment to the t moment within the preset confidence level.
Based on the same conception, the invention provides an electric heating comprehensive coordination optimization scheduling system, which comprises: the device comprises a processing module, a starting-up condition determining module and a solving module;
the processing module is used for screening the pre-acquired data of the error probability distribution and calculating the scene probability;
the starting-up condition determining module is used for inputting the screened data into a pre-constructed multi-scene unit combination model for solving based on the scene probability and determining the starting-up and stopping conditions of the unit;
the solving module is used for solving a pre-constructed scheduling plan model based on the determined startup and shutdown condition of the unit and the wind power prediction scene to obtain a scheduling strategy;
the multi-scene unit combination model determines the starting and stopping conditions of each unit by taking the minimum total power generation cost of the system as a target under the condition of considering scene types and the occurrence probability of each type of scene;
and the scheduling plan model is used for calculating the total power generation cost of the system by taking the minimum output and the maximum output of the thermoelectric generator set as targets under various scenes based on wind power prediction.
Compared with the prior art, the invention has the beneficial effects that:
1. the invention provides an electric heating comprehensive coordination optimization scheduling method, which comprises the following steps: screening the pre-acquired data of the error probability distribution, and calculating scene probability; inputting the screened data into a pre-constructed multi-scene unit combination model for solving based on the scene probability, and determining the on-off condition of the unit; solving a pre-constructed scheduling plan model based on the determined startup and shutdown conditions of the unit and the wind power prediction scene to obtain a scheduling strategy; the multi-scene unit combination model determines the starting and stopping conditions of each unit by taking the minimum total power generation cost of the system as a target under the condition of considering scene types and the occurrence probability of each type of scene; the scheduling plan model is used for calculating the total power generation cost of the system based on the minimum and maximum output of the thermoelectric generator set under each scene of wind power prediction, improving the reasonability and accuracy of limit setting of the thermoelectric generator set through the multi-scene unit combination model and the scheduling plan model, expanding the space of the thermoelectric combined transport unit participating in peak shaving, and simultaneously reserving more scientific margin for further optimization in subsequent scheduling;
2. according to the electric heating comprehensive coordination optimization scheduling method and system, the unit combination is determined through probability scene generation and screening, requirements such as peak regulation auxiliary service, wind abandoning punishment, thermoelectric unit coupling characteristics and the like are considered, the operation range is given while the power generation plan of the thermoelectric unit is given, flexible handling control of the thermoelectric unit by plan making personnel is facilitated, the flexibility and the peak regulation capability of the system are improved, the new energy fluctuation can be restrained, and the capacity of a power grid for absorbing new energy can be improved.
Drawings
FIG. 1 is a flow chart of a method provided by the present invention;
FIG. 2 is a diagram of an electrothermal coupling relationship of a thermoelectric generator set according to an embodiment of the present invention;
FIG. 3 is a schematic flow chart of an electric-thermal integrated coordination optimization scheduling method according to an embodiment of the present invention;
fig. 4 is a system configuration diagram provided by the present invention.
Detailed Description
The embodiments of the present invention will be further explained with reference to the drawings.
Example 1:
the invention provides an electric heating comprehensive coordination optimization scheduling method, which is introduced by combining with a method flow chart of fig. 1 and specifically comprises the following steps:
step 1: screening the pre-acquired data of the error probability distribution, and calculating scene probability;
step 2: inputting the screened data into a pre-constructed multi-scene unit combination model for solving based on the scene probability, and determining the on-off condition of the unit;
and step 3: solving a pre-constructed scheduling plan model based on the determined startup and shutdown conditions of the unit and the wind power prediction scene to obtain a scheduling strategy;
wherein, the step 1: screening the pre-acquired data of the error probability distribution, and calculating scene probability, specifically comprising:
a wind power scene generation. Wind power prediction and wind power prediction error probability distribution are obtained, and 1000 groups of wind power scene sets are obtained by Monte Carlo simulation.
And selecting wind power scenes. Reducing and selecting 1000 groups of scenes by using a state transition matrix and a synchronous back substitution subtraction method to obtain N wind power scenes and probability P of each scenes(s＝1，2，......，N)。
Generating a scene tree:
(1) and calculating the possible wind power output at the time t. the predicted wind power output value at the time t is Pt，windConsidering the prediction error (with a prediction value and a distribution function of the prediction error) of historical statistics, the actual wind power variation range is considered to be Pt，wind× (1 +/-3 sigma), wherein sigma is the standard deviation of the prediction error, and a plurality of possible wind power states w are obtained according to Monte Carlo simulationi(i＝1，2，3……)。
(2) From the state transition matrix, the slave state w is obtainedt-1Is transferred to wtProbability of possible wind states at any moment
Determining corresponding states w from large to small according to the probabilityi∈wtUntil it is transferred to wtIs greater than a certain confidence level.
(3) And (4) repeating the steps (1) to (2) according to the obtained wind power output state at the time t to obtain the wind power state at the time t + 1.
(4) Repeating the step (3), obtaining wind power state scenes in multiple time periods, wherein the probability of each scene is
(5) And (5) utilizing synchronous back-substitution reduction method to finish scene reduction screening based on the minimum probability distance.
Step 2: inputting the screened data into a pre-constructed multi-scene unit combination model for solving based on the scene probability, and determining the start-up and shut-down conditions of the unit, wherein the method specifically comprises the following steps:
establishing a multi-scenario unit combination model, determining unit start and stop, and introducing by combining with a flow diagram of an electric-thermal comprehensive coordination optimization scheduling method shown in fig. 3.
Determining an objective function, the objective function being as follows:
f is the total power generation cost of the system, including the unit starting cost and the coal consumption cost, I is the number of thermal power generating units, T is the total scheduling time interval number, SCi，tFor the starting cost of the ith unit in the t period ui，tFor the operation state of the ith unit in the t period, 1 is starting up, 0 is stopping, and N issAs the number of scenes, psIs the probability of scene s.
The coal consumption cost of the ith unit in the s scene and the t time period is as follows:
in the formula
Represents the coal consumption cost of the t-th time interval under the s-th scene,
is the power value of the ith set in the t time interval and s scene, ai、bi、ciAnd the coal consumption cost coefficient of the unit i is obtained.
Determining constraints
The constraint conditions include constraints such as unit characteristics, network security and power balance, and need to be satisfied in each scenario.
Model solution
And after the occurrence probability of each scene of the system is obtained, converting the model into a determined unit combination model, and solving the model by using mixed integer programming to obtain the determined unit combination.
And step 3: based on the determined startup and shutdown conditions of the unit and the wind power prediction scene, solving a pre-constructed scheduling plan model to obtain a scheduling strategy, which specifically comprises the following steps:
and selecting a wind power prediction maximum scene, and determining the minimum output of the thermoelectric generator set.
In order to solve the minimum output of the thermoelectric generator set, a scene with the maximum wind power prediction is selected, and a scheduling plan model is established.
Determining an objective function
Wherein: f is the total power generation cost of the system, comprising three parts of coal consumption cost, wind abandoning cost and peak shaving cost of the thermoelectric unit, FCi，tRepresenting the coal consumption cost of the ith thermoelectric unit in the t period, and the calculation method is the same as formula (2), QF'i，tSelecting the wind curtailment power of a wind power plant j in a time period t when the wind power forecast is in the maximum scene, wherein gamma is a penalty factor, Ck，tIs the peak regulation power of the peak regulation thermal power generating unit k in the period t, ηk，tThermal power generator with peak regulationAnd peak regulation quotation of the group k in a period T, I, J, K are the number of the thermoelectric generator set, the wind power plant and the peak regulation generator set respectively, and T is the operation period.
Determining constraints
Including electric power electric quantity balance, unit restraint, climbing restraint, electric wire netting safety restraint etc. thermoelectric power unit still need consider the electric heat coupling restraint, specifically as follows:
the upper limit of the conventional output of the thermoelectric generator set i can be determined by the thermal load by referring to the thermoelectric coupling relation diagram of the thermoelectric generator set of FIG. 2
And lower limit of output
In the formula a1i、b1i、a2i、b2iDenotes a coupling parameter, Hi，tThe representation represents the heat supply demand of the thermoelectric power unit i in the t period, and can be obtained in advance.
And establishing a scheduling plan model, and solving to obtain the output of the thermoelectric unit, namely the minimum output of the thermoelectric unit.
And selecting a wind power prediction minimum scene, and determining the maximum output of the thermoelectric generator set.
In order to solve the maximum output of the thermoelectric generator set, a scene with the minimum wind power prediction is selected, and an objective function is adjusted as follows:
the constraint conditions are the same as the step 4, a scheduling plan model is established, the output of the thermoelectric unit is obtained through solving, namely the maximum output of the thermoelectric unit,QF″i，tselecting the wind curtailment power of the wind power plant j in the t time period when the wind power forecast is in the minimum scene.
And selecting normal wind power prediction data, and determining the reference output of the thermoelectric generator set.
In order to solve the output curve of the thermoelectric generator set, normal wind power prediction data and an objective function are selected
Wherein: FC'i，tRepresenting the coal consumption cost of the ith thermal power generating unit (including the thermal power generating unit) in the tth time period under a normal scene, wherein I is the total number, QF ″, of the thermal power generating units'i，tSelecting the wind curtailment power of the wind power plant j in the time period t when the wind power forecast is in a normal scene.
The constraint conditions comprise electric power and electric quantity balance, unit output constraint, climbing constraint, power grid safety constraint and the like, the thermoelectric unit also needs to consider electric-thermal coupling constraint, a dispatching plan model is established, and the output of the thermoelectric unit, namely the reference output of the thermoelectric unit, is obtained by solving, and is used by dispatching personnel for reference.
Example 2:
based on the same inventive concept, the invention also provides an electric heating comprehensive coordination optimization scheduling system, which is introduced by combining the system structure diagram of fig. 4, and specifically comprises the following steps: the device comprises a processing module, a starting-up condition determining module and a solving module;
the processing module is used for screening the pre-acquired data of the error probability distribution and calculating the scene probability;
the starting-up condition determining module is used for inputting the screened data into a pre-constructed multi-scene unit combination model for solving based on the scene probability and determining the starting-up and stopping conditions of the unit;
the solving module is used for solving a pre-constructed scheduling plan model based on the determined startup and shutdown condition of the unit and the wind power prediction scene to obtain a scheduling strategy;
the multi-scene unit combination model determines the starting and stopping conditions of each unit by taking the minimum total power generation cost of the system as a target under the condition of considering scene types and the occurrence probability of each type of scene;
and the scheduling plan model is used for calculating the total power generation cost of the system by taking the minimum output and the maximum output of the thermoelectric generator set as targets under various scenes based on wind power prediction.
The boot condition determining module includes: a combined model target submodule and a combined model constraint submodule;
the combined model target submodule is used for constructing a target function by taking the minimum total power generation cost of the system as a target in consideration of scene types and the occurrence probability of various types of scenes;
and the combined model constraint submodule is used for constructing constraint conditions by unit characteristic constraint, network security constraint and power balance constraint under each scene.
The solving module comprises: a maximum scene submodule, a minimum scene submodule and a normal scene submodule;
the maximum scene submodule is used for predicting a maximum scene based on the selected wind power, determining the minimum output of the thermoelectric generator set and calculating the total power generation cost of the system;
the minimum scene submodule is used for predicting a minimum scene based on the selected wind power, determining the maximum output of the thermoelectric generator set and calculating the total power generation cost of the system;
and the normal scene submodule is used for determining the normal output of the thermoelectric generator set based on the selected normal wind power prediction scene and calculating the total power generation cost of the system.
The maximum scene submodule includes: a minimum output target unit and a minimum output constraint unit;
the minimum output target unit is used for targeting the minimum output of the thermoelectric unit;
the minimum output constraint unit is used for taking the upper limit and the lower limit of the electrothermal coupling output, the electric power and electric quantity balance, the unit output constraint, the climbing constraint and the power grid safety as constraint conditions.
The minimum scene submodule includes: the maximum output target unit and the maximum output constraint unit; the maximum output target unit is used for taking the maximum output of the thermoelectric unit as a target;
the maximum output constraint unit is used for taking the upper and lower limits of the electrothermal coupling output, the electric power and electric quantity balance, the unit output constraint, the climbing constraint and the power grid safety as constraint conditions.
The normal scene submodule includes: a normal output unit and a normal output restraining unit;
the normal output unit is used for constructing a target function of the normal output of the thermoelectric generator set based on the determined startup and shutdown conditions of the generator set and the wind power prediction normal scene;
the normal output restraining unit is used for determining a restraining condition for the target function of the normal output;
wherein the constraint condition comprises: the method comprises the following steps of electric-thermal coupling constraint upper and lower output limit constraint, electric power and electric quantity balance constraint, unit output constraint, climbing constraint and power grid safety constraint.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The present invention is not limited to the above embodiments, and any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention are included in the scope of the claims of the present invention which are filed as the application.
Claims (13)
1. An electric heating comprehensive coordination optimization scheduling method is characterized by comprising the following steps:
screening the pre-acquired data of the error probability distribution, and calculating scene probability;
inputting the screened data into a pre-constructed multi-scene unit combination model for solving based on the scene probability, and determining the on-off condition of the unit;
solving a pre-constructed scheduling plan model based on the determined startup and shutdown conditions of the unit and the wind power prediction scene to obtain a scheduling strategy;
the multi-scene unit combination model determines the starting and stopping conditions of each unit by taking the minimum total power generation cost of the system as a target under the condition of considering scene types and the occurrence probability of each type of scene;
and the scheduling plan model is used for calculating the total power generation cost of the system by taking the minimum output and the maximum output of the thermoelectric generator set as targets under various scenes based on wind power prediction.
2. The method of claim 1, wherein the constructing of the multi-scenario crew combined model comprises:
considering scene types and the occurrence probability of each type of scene, and constructing an objective function by taking the minimum total power generation cost of the system as a target;
and under each scene, a constraint condition is constructed by unit characteristic constraint, network security constraint and power balance constraint.
3. The method of claim 2, wherein the objective function of the multi-scenario block combination model is calculated as follows:
f is the total power generation cost of the system, including the unit starting cost and the coal consumption cost, I is the number of thermal power generating units, T is the total scheduling time interval number, SCi，tFor the starting cost of the ith unit in the t period ui，tFor the operation state of the ith unit in the t period, 1 is starting up, 0 is stopping, and N issAs the number of scenes, psIs the probability of scene s, ui，t-1The operation state of the ith unit in the t-1 th time interval is 1, starting up,
and the coal consumption cost of the ith unit in the s-th scene and the t-th time period is obtained.
4. The method of claim 3, wherein the coal consumption cost of the ith unit in the s-th scene in the t-th time period
Is calculated as follows:
in the formula (I), the compound is shown in the specification,
is the power value of the ith set in the t time interval and s scene, ai、bi、ciAnd the coal consumption cost coefficient of the unit i is obtained.
5. The method of claim 3, wherein the dispatch plan model comprises:
determining the minimum output of the thermoelectric generator set based on the selected wind power prediction maximum scene, and calculating the total power generation cost of the system;
determining the maximum output of the thermoelectric generator set based on the selected wind power prediction minimum scene, and calculating the total power generation cost of the system;
and determining the normal output of the thermoelectric generator set based on the selected normal wind power prediction scene, and calculating the total power generation cost of the system.
6. The method of claim 5, wherein the dispatch plan model for the wind power forecast maximum scenario comprises:
the minimum output of the thermoelectric unit is taken as a target;
the method takes the upper limit and the lower limit of the electric heating coupling output, the electric power and electric quantity balance, the unit output constraint, the climbing constraint and the power grid safety as constraint conditions.
7. The method of claim 6, wherein the objective function of the minimum output of the thermoelectric generator set is calculated as follows:
wherein F is the total power generation cost of the system, FCi，tRepresents the coal consumption cost, QF 'of the ith thermoelectric unit in the t period'i，tSelecting the wind curtailment power of a wind power plant j in a time period t when the wind power forecast is in the maximum scene, wherein gamma is a penalty factor, Ck，tIs the peak regulation power of the peak regulation thermal power generating unit k in the period t, ηk，tThe peak regulation quotation of the peak regulation thermal power generating unit k in the period T, I, J, K are the number of the thermoelectric power generating units, the wind power plant and the peak regulation units respectively, and T is the operation period.
8. The method of claim 5, wherein the dispatch plan model for the wind power forecast minimum scenario comprises:
the maximum output of the thermoelectric unit is taken as a target; the method takes the upper limit and the lower limit of the electric heating coupling output, the electric power and electric quantity balance, the unit output constraint, the climbing constraint and the power grid safety as constraint conditions.
9. The method of claim 8, wherein the objective function of the maximum output of the thermoelectric generator set is calculated as follows:
in the formula, QFi，tSelecting the wind curtailment power of the wind power plant j in the t time period when the wind power forecast is in the minimum scene.
10. The method of claim 5, wherein the dispatch plan model for the normal wind prediction scenario comprises:
constructing a target function of the normal output of the thermoelectric generator set based on the determined startup and shutdown conditions of the generator set and the wind power prediction normal scene;
determining constraint conditions for the target function of the normal output;
wherein the constraint condition comprises: the method comprises the following steps of electric-thermal coupling constraint upper and lower output limit constraint, electric power and electric quantity balance constraint, unit output constraint, climbing constraint and power grid safety constraint.
11. The method of claim 10, wherein the objective function of the normal output of the thermoelectric generator set is calculated as follows:
of formula (II) FC'i，tRepresenting the coal consumption cost of the ith thermal power generating unit (including the thermal power generating unit) in the tth time period under a normal scene, wherein I is the total number, QF ″, of the thermal power generating units'i，tSelecting the wind curtailment power of the wind power plant j in the time period t when the wind power forecast is in a normal scene.
12. The method of claim 1, wherein the scene probability is calculated as follows:
calculating a wind power output value at the predicted time t based on a predicted value and a predicted error of historical statistics, and obtaining a plurality of predicted wind power states at the time t based on the predicted wind power output value and an actual wind power variation range;
inputting a plurality of t-moment predicted wind power states and t-1-moment predicted wind power states into a state transition matrix to obtain the probability of the t-moment predicted wind power states transitioning to the t-moment predicted wind power states within a preset confidence level, and arranging the t-moment predicted wind power states from large to small according to the probability;
and calculating to obtain a multi-period wind power state scene and scene probability by utilizing the state transition matrix based on the calculation of the predicted wind power state transition from the t-1 moment to the t moment within the preset confidence level.
13. An electric heating comprehensive coordination optimization scheduling system is characterized by comprising: the device comprises a processing module, a starting-up condition determining module and a solving module;
the processing module is used for screening the pre-acquired data of the error probability distribution and calculating the scene probability;
the starting-up condition determining module is used for inputting the screened data into a pre-constructed multi-scene unit combination model for solving based on the scene probability and determining the starting-up and stopping conditions of the unit;
the solving module is used for solving a pre-constructed scheduling plan model based on the determined startup and shutdown condition of the unit and the wind power prediction scene to obtain a scheduling strategy;
the multi-scene unit combination model determines the starting and stopping conditions of each unit by taking the minimum total power generation cost of the system as a target under the condition of considering scene types and the occurrence probability of each type of scene;
and the scheduling plan model is used for calculating the total power generation cost of the system by taking the minimum output and the maximum output of the thermoelectric generator set as targets under various scenes based on wind power prediction.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112365034A (en) * | 2020-10-27 | 2021-02-12 | 燕山大学 | Electric heating comprehensive energy system scheduling method and system |
| CN112366687A (en) * | 2020-10-23 | 2021-02-12 | 国网青海省电力公司经济技术研究院 | Peak-shaving auxiliary service compensation method and device considering green certificate |
| CN114069613A (en) * | 2021-11-03 | 2022-02-18 | 国网山东省电力公司东营供电公司 | Method and system for regulating and controlling participation of self-contained power plant in peak regulation based on enterprise energy utilization characteristics |
| CN116454890A (en) * | 2023-04-20 | 2023-07-18 | 中国南方电网有限责任公司 | Combined control method, device and equipment for unit based on SCUC model |
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2020
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112366687A (en) * | 2020-10-23 | 2021-02-12 | 国网青海省电力公司经济技术研究院 | Peak-shaving auxiliary service compensation method and device considering green certificate |
| CN112366687B (en) * | 2020-10-23 | 2023-01-13 | 国网青海省电力公司经济技术研究院 | Peak-shaving auxiliary service compensation method and device considering green certificate |
| CN112365034A (en) * | 2020-10-27 | 2021-02-12 | 燕山大学 | Electric heating comprehensive energy system scheduling method and system |
| CN114069613A (en) * | 2021-11-03 | 2022-02-18 | 国网山东省电力公司东营供电公司 | Method and system for regulating and controlling participation of self-contained power plant in peak regulation based on enterprise energy utilization characteristics |
| CN114069613B (en) * | 2021-11-03 | 2024-03-12 | 国网山东省电力公司东营供电公司 | Method and system for regulating and controlling participation peak shaving of self-contained power plant based on enterprise energy consumption characteristics |
| CN116454890A (en) * | 2023-04-20 | 2023-07-18 | 中国南方电网有限责任公司 | Combined control method, device and equipment for unit based on SCUC model |
| CN116454890B (en) * | 2023-04-20 | 2024-02-06 | 中国南方电网有限责任公司 | Combined control method, device and equipment for unit based on SCUC model |
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