CN117254503A - Source-net-storage-load double-layer collaborative low-carbon scheduling method, equipment and medium based on wind power consumption - Google Patents

Source-net-storage-load double-layer collaborative low-carbon scheduling method, equipment and medium based on wind power consumption Download PDF

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Publication number
CN117254503A
CN117254503A CN202311239235.1A CN202311239235A CN117254503A CN 117254503 A CN117254503 A CN 117254503A CN 202311239235 A CN202311239235 A CN 202311239235A CN 117254503 A CN117254503 A CN 117254503A
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power
cost
load
storage
wind
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Inventor
孙杰懿
沈阳武
王小源
李帅虎
欧阳中
郝露茜
伍红
曾宪东
叶建兴
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State Grid Corp of China SGCC
Electric Power Research Institute of State Grid Hunan Electric Power Co Ltd
State Grid Hunan Electric Power Co Ltd
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State Grid Corp of China SGCC
Electric Power Research Institute of State Grid Hunan Electric Power Co Ltd
State Grid Hunan Electric Power Co Ltd
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Priority to CN202311239235.1A priority Critical patent/CN117254503A/en
Publication of CN117254503A publication Critical patent/CN117254503A/en
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06313—Resource planning in a project environment
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06315—Needs-based resource requirements planning or analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas or water supply
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/003—Load forecast, e.g. methods or systems for forecasting future load demand
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/004—Generation forecast, e.g. methods or systems for forecasting future energy generation
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/007—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources
    • H02J3/0075—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources for providing alternative feeding paths between load and source according to economic or energy efficiency considerations, e.g. economic dispatch
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
    • H02J3/14—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
    • H02J3/144—Demand-response operation of the power transmission or distribution network
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/28—The renewable source being wind energy
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/40—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation wherein a plurality of decentralised, dispersed or local energy generation technologies are operated simultaneously
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2310/00—The network for supplying or distributing electric power characterised by its spatial reach or by the load
    • H02J2310/50—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads
    • H02J2310/56—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads characterised by the condition upon which the selective controlling is based
    • H02J2310/58—The condition being electrical
    • H02J2310/60—Limiting power consumption in the network or in one section of the network, e.g. load shedding or peak shaving

Abstract

The invention discloses a source-network-storage-load double-layer collaborative low-carbon scheduling method, equipment and medium based on wind power consumption, which comprises the steps of firstly constructing a demand response model with minimum demand response cost as a target as an upper model; secondly, constructing a thermal power unit cost model with the minimum running cost of the thermal power unit as a target, a carbon emission model with the minimum carbon transaction cost as a target, a wind power plant output model with the maximum wind power consumption and the minimum running cost of wind power as a target, and an energy storage model with the minimum energy storage cost and the maximum income as a target, and combining the four models as a lower model; and finally, solving the upper model and the lower model in sequence based on the load prediction curve and the wind power prediction output curve to obtain an optimal scheduling strategy for scheduling the power system. And the carbon transaction cost is considered while the wind power absorption capacity is improved, the demand response and the energy storage system are combined, the source-net-storage-load are combined to cooperatively operate, and the economy is obviously enhanced.

Description

Source-net-storage-load double-layer collaborative low-carbon scheduling method, equipment and medium based on wind power consumption
Technical Field
The invention relates to the technical field of power systems, in particular to a source-network-storage-load double-layer collaborative low-carbon scheduling method, equipment and medium based on wind power consumption.
Background
Greenhouse gas emissions are of worldwide concern, with carbon emissions being a particular concern. Carbon emission quota and trading regime are abbreviated as carbon trade and are currently considered as one of the most effective carbon emission reduction measures. Under the carbon trade system, an upper limit of carbon emissions in a certain area for a certain period of time is regulated by a government, and the trade of carbon emissions rights is allowed. In China, a power system mainly based on thermal power generation is a main source of carbon emission, and carbon emission control and transaction will have fundamental influence on a traditional economic dispatch mode: once the upper carbon emission limit is specified, the power generation plan will have to consider the optimal allocation of carbon emissions rights among the units at the same time, even "fixed electricity on carbon"; and meanwhile, carbon transaction enables carbon emission to have monetary value, so that the carbon emission is a type of schedulable resource. Therefore, the power generation and carbon emission right combined optimization scheduling aims to solve 2 problems: 1) Carbon emissions are below an upper limit; 2) And the electricity generation cost is coordinated with the carbon emission.
In the past, attention has been paid to a scheduling mode in which both the power generation cost and the greenhouse gas emission are considered. Since carbon emissions and power generation costs do not have uniform dimensions, many studies consider only one objective, and treat the other as a constraint. This phenomenon is particularly common in early studies. These efforts do not focus on coordination between electricity and carbon, and decision results tend to be biased toward optimization in a single aspect, so the model is not suitable for coordinated scheduling of electricity generation and carbon emissions.
The utilization of wind power is beneficial to energy conservation and emission reduction, but the uncertainty of wind power makes the dependence relationship between the wind power and a conventional unit quite remarkable, the rapidly-growing wind power grid-connected requirement is difficult to meet only by means of power generation side scheduling, and the wind power absorption capacity of a power grid is limited. Along with the construction of the smart power grid, energy storage and demand response are increasingly paid attention to system scheduling in academia and engineering circles, and the inventor finds that the improvement of wind power absorption capacity by using the energy storage system and the demand response is a technically and economically excellent scheme and has important significance in promoting energy conservation and emission reduction.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a source-network-storage-load double-layer collaborative low-carbon scheduling method, equipment and medium based on wind power consumption, which can realize the combined optimal scheduling of power generation and carbon emission on the basis of improving the wind power consumption capability.
In a first aspect, a source-network-storage-load double-layer collaborative low-carbon scheduling method based on wind power consumption is provided, which comprises the following steps:
constructing a demand response model with minimum demand response cost as an upper model;
constructing a thermal power unit cost model with the minimum running cost of the thermal power unit as a target, a carbon emission model with the minimum carbon transaction cost as a target, a wind power plant output model with the maximum wind power consumption and the minimum running cost of wind power as a target, and an energy storage model with the minimum energy storage cost and the maximum income as a target, and combining the four models as a lower model;
acquiring a load prediction curve, inputting the load prediction curve into an upper model, and solving the upper model to optimize the load prediction curve to obtain an equivalent load curve;
acquiring a wind power predicted output curve, and carrying out multi-objective joint solution on a lower model by combining an equivalent load curve to obtain an optimal scheduling strategy of the power system;
and scheduling the power system based on the optimal scheduling strategy.
Further, the demand response model targeting the minimum demand response cost is expressed as follows:
P elv,t ＝P load,t +ΔP PDRt +ΔP IDRAt
wherein C is 5 Representing demand response costs; ΔP PDRt 、ΔP IDRAt The call quantity of the PDR load and the class A IDR load at the time t is respectively expressed; k (k) PDR 、k IDRA Calling cost coefficients respectively representing the PDR load and the class A IDR load;ΔQ t representing the difference between the load at t and the original predicted load after demand response; p (P) load,min And P load,max Respectively representing the minimum value and the maximum value of the original predicted load in a scheduling period; p (P) load,t Representing the original load at time t; t represents a scheduling period, and Deltat represents a time step; p (P) elv,t Representing the equivalent load at time t, C 6 Representing the difference between the equivalent load curve and the wind power predicted force curve,and predicting a force value for wind power at the moment t of the wind turbine.
Further, the thermal power unit cost model with the minimum running cost of the thermal power unit as the target is expressed as follows:
wherein C is 1 Representing the running cost of the thermal power generating unit; c (C) mh And C qt Respectively representing the coal consumption cost and the start-stop cost of the thermal power unit; a, a j 、b j 、c j The coal consumption coefficient of the j-th thermal power generating unit; p (P) G,j,t The output value of the j-th thermal power unit at the time t is the output value of the j-th thermal power unit at the time t; s is S jt The start-stop cost of the j-th thermal power generating unit; u (u) j,t The power generation system is in a start-stop state of the j-th thermal power generating unit at the moment t; n is n j,t 、m j,t Respectively, the j-th thermal power generating unit is started and stopped at the time tPerforming;and->Respectively the minimum output and the maximum output of the j-th thermal power unit; />The climbing rate of the j-th conventional unit is represented; t represents a scheduling period, N G Is the number of thermal power generating units.
Further, the carbon emission model targeting the minimum carbon trade cost is expressed as follows:
wherein M is L M is the total carbon emission quota of the power system P Is the total carbon emission of the power system in one period,for the unit electric quantity CO of the j-th thermal power generating unit 2 A discharge distribution coefficient; θ and->The carbon emission distribution coefficients of the pumped storage power station hydroelectric generating set and the wind power station wind generating set are respectively; p (P) G,j,t 、P h,t 、P w,t The output of the thermal power unit, the hydroelectric generating set and the wind generating set at the time t is respectively; lambda (lambda) j The carbon emission intensity of the power system in one period is as follows; c2 is the total carbon trade cost; />A trade price for carbon; d is carbon rowThe length of the discharge interval; τ is the carbon trade price amplitude of each step of increasing the carbon emission; t represents a scheduling period, N G Is the number of thermal power generating units.
Further, the wind farm output model with the aim of maximizing wind power consumption and minimizing wind power running cost is expressed as follows:
wherein C is 3 Representing the joint cost constructed by the maximum wind power consumption and the minimum total wind power running cost; c (C) w,t 、C cw,t The operation and maintenance cost and the wind abandoning punishment cost of the wind farm are respectively k w 、k cw The operation cost coefficient and the abandoned wind punishment cost coefficient of the wind farm are respectively P w,t The output of the wind turbine generator at the time t,predicting a force value for wind power at the moment t of the wind turbine; t denotes one scheduling period.
Further, the energy storage model with the minimum energy storage cost and the maximum benefit is expressed as follows:
min C 4 ＝C hydro -C dhx
wherein C is 4 Representing the comprehensive cost of the pumped storage power station and the electrochemical storage power station; c (C) dhx For electrochemical energy storage profit, C sy And C cb Respectively representing the income and the cost of the electrochemical energy storage power station; p is p price The electricity price of the power grid;and->Respectively representing the discharge efficiency and the charge efficiency of the electrochemical energy storage power station; p (P) sd,t And P sc,t Respectively representing the discharge power and the charging power of the electrochemical energy storage power station at the time t; c sc And c sd Respectively representing the charging cost coefficient and the discharging cost coefficient of the electrochemical energy storage power station; c (C) hydro Pumping-generating cost for a pumped storage power station; c f And c c The cost coefficients of power generation and water pumping are respectively; p (P) gen,t And P pump,t Respectively generating power and pumping power of a hydroelectric unit of the pumped storage power station at the time t; s is S t For the electricity storage quantity of the electrochemical energy storage power station at the moment t, theta i The self-loss rate of the electrochemical energy storage power station is; u (u) sc,t And u sd,t Respectively representing a charging point state and a discharging state; s is S t,min And S is t,max The upper and lower limits of the capacity of the electrochemical energy storage power station are set; p (P) sc,max And P sd,max Respectively representing the maximum charging power and the maximum discharging power of the electrochemical energy storage power station; e (E) p,t Reservoir capacity for the pumped storage power station t period; beta c And beta f Respectively pumping efficiency and generating efficiency of the pumped storage power station; />And->The minimum storage capacity and the maximum storage capacity of the pumped storage power station are respectively; ΔP R Representing the climbing rate of the pumped storage power station; t denotes one scheduling period.
Further, the lower model further comprises a power transmission line operation constraint and a power balance constraint, and the power transmission line operation constraint is expressed as follows:
in the method, in the process of the invention,maximum transmission power of a transmission line between the nodes i and j; b (B) ij Is susceptance between nodes i, j; θ i,t For the phase angle, θ, at the moment of the inode t j,t The phase angle of the moment t of the j node is; the transmission line operation constraint belongs to an indirect constraint, such as that a node has two generators to carry out power transmission on a line, so that the total power transmitted by the two generators cannot exceed the upper transmission limit of the line.
The power balance constraint is expressed as follows:
wherein P is G,j,t The output value of the jth thermal power generating unit at the time t is N G The number of the thermal power generating units; p (P) w,t The output of the wind turbine generator at the time t is obtained; p (P) t Erss The total charge and discharge power of the pumped storage power station and the electrochemical energy storage power station at the moment t; p (P) elv,t Representing the equivalent load at time t; p (P) t loss The power loss at time t of the power system is shown.
In a second aspect, there is provided an electronic device comprising:
a memory having a computer program stored thereon;
and the processor is used for loading and executing the computer program to realize the source-net-storage-load double-layer collaborative low-carbon scheduling method based on wind power consumption.
In a third aspect, a computer readable storage medium is provided, on which a computer program is stored, which, when being executed by a processor, implements a source-net-storage-load bi-layer collaborative low-carbon scheduling method based on wind power consumption as described above.
The invention provides a source-network-storage-load double-layer collaborative low-carbon scheduling method, equipment and medium based on wind power consumption, which are used for improving new energy consumption capability of regional power grids, simultaneously considering carbon transaction cost, greatly reducing carbon emission of an electric power system, combining a demand response system and an energy storage system, really combining source-network-storage-load for collaborative operation, and remarkably enhancing economical efficiency.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a diagram of an overall scheduling framework of a power system provided by an embodiment of the invention;
fig. 2 is a flow chart of a source-net-storage-load double-layer collaborative low-carbon scheduling method based on wind power consumption, which is provided by the embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be described in detail below. It will be apparent that the described embodiments are only some, but not all, embodiments of the invention. All other embodiments, based on the examples herein, which are within the scope of the invention as defined by the claims, will be within the scope of the invention as defined by the claims.
Fig. 1 shows an overall dispatching frame diagram of a power system according to an embodiment of the present invention, which includes a power grid side, a source side, a demand side, and an energy storage side.
As shown in fig. 2, for the power system shown in fig. 1, the embodiment of the invention provides a source-network-storage-load double-layer collaborative low-carbon scheduling method based on wind power consumption. The method specifically comprises the following steps:
s1: and constructing a demand response model with minimum demand response cost as an upper model.
The demand response model with the minimum demand response cost as the target is expressed as follows:
P elv,t ＝P load,t +ΔP PDRt +ΔP IDRAt
wherein C is 5 Representing demand response costs; ΔP PDRt 、ΔP IDRAt The call quantity of the PDR load and the class A IDR load at the time t is respectively expressed; k (k) PDR 、k IDRA Calling cost coefficients respectively representing the PDR load and the class A IDR load; ΔQ t Representing the difference between the load at t and the original predicted load after demand response; p (P) load,min And P load,max Respectively representing the minimum value and the maximum value of the original predicted load in a scheduling period; p (P) load,t Representing the original load at time t; t represents a scheduling period, and Deltat represents a time step; p (P) elv,t Representing the equivalent load at time t, C 6 Representing the difference between the equivalent load curve and the wind power predicted force curve,and predicting a force value for wind power at the moment t of the wind turbine.
In the demand response model, ΔP PDRt 、ΔP IDRAt To be solved for the quantity ΔP PDRt 、ΔP IDRAt And (5) as the load adjustment amount, the equivalent load is obtained after adjustment, and then an equivalent load curve is obtained. Wherein the constraint is to ensure that the demand response only shifts load without affecting power usage during the scheduling period, and that the value of the load after shifting is between the maximum and minimum values of the original load during the scheduling period. Min C in demand response model 6 The objective function enables the equivalent load curve obtained through solving to be close to the wind power predicted output curve, so that the wind power digestion capacity is improved.
S2: building a thermal power unit cost model with the minimum running cost of the thermal power unit as a target, a carbon emission model with the minimum carbon transaction cost as a target, a wind power plant output model with the maximum wind power consumption and the minimum running cost of wind power as a target, and an energy storage model with the minimum energy storage cost and the maximum income as a target, and combining the four models as a lower model.
The thermal power unit cost model with the minimum running cost of the thermal power unit as the target is expressed as follows:
wherein C is 1 Representing the running cost of the thermal power generating unit; c (C) mh And C qt Respectively representing the coal consumption cost and the start-stop cost of the thermal power unit; a, a j 、b j 、c j The coal consumption coefficient of the j-th thermal power generating unit; p (P) G,j,t The output value of the j-th thermal power unit at the time t is the output value of the j-th thermal power unit at the time t; s is S jt The start-stop cost of the j-th thermal power generating unit; u (u) j,t The starting and stopping state of the j-th thermal power generating unit at the moment t is represented by 1, and the starting state is represented by 0; n is n j,t 、m j,t The operation is respectively the start-up and stop operation of the jth thermal power generating unit at the time t; n is n j,t When 1 is taken, the machine is started, and when 0 is taken, the machine does not act; m is m j,t Taking 1 to indicate stopping, and taking 0 to indicate no action;and->Respectively the minimum output and the maximum output of the j-th thermal power unit; />The climbing rate of the j-th conventional unit is represented; t represents a scheduling period, N G Is the number of thermal power generating units.
The carbon emission model targeting the minimum carbon trade cost is represented as follows:
wherein M is L M is the total carbon emission quota of the power system P Is the total carbon emission of the power system in one period,for the unit electric quantity CO of the j-th thermal power generating unit 2 A discharge distribution coefficient; θ and->The carbon emission distribution coefficients of the pumped storage power station hydroelectric generating set and the wind power station wind generating set are respectively; p (P) G,j,t 、P h,t 、P w,t The output of the thermal power unit, the hydroelectric generating set and the wind generating set at the time t is respectively; lambda (lambda) j The carbon emission intensity of the power system in one period is as follows; c2 is the total carbon trade cost; />A trade price for carbon; d is the carbon emission interval length; τ is the carbon trade price amplitude of each step of increasing the carbon emission; t represents a scheduling period, N G Is the number of thermal power generating units.
The wind power plant output model with the aim of maximum wind power consumption and minimum wind power running cost is expressed as follows:
wherein C is 3 Representing the joint cost constructed by the maximum wind power consumption and the minimum total wind power running cost; c (C) w,t 、C cw,t The operation and maintenance cost and the wind abandoning punishment cost of the wind farm are respectively k w 、k cw The operation cost coefficient and the abandoned wind punishment cost coefficient of the wind farm are respectively P w,t For windThe output of the motor group at the time t,predicting a force value for wind power at the moment t of the wind turbine; t denotes one scheduling period.
The energy storage model with the aim of minimum energy storage cost and maximum benefit is expressed as follows:
min C 4 ＝C hydro -C dhx
wherein C is 4 Representing the comprehensive cost of the pumped storage power station and the electrochemical storage power station; c (C) dhx For electrochemical energy storage profit, C sy And C cb Respectively representing the income and the cost of the electrochemical energy storage power station; p is p price The electricity price of the power grid;and->Respectively representing the discharge efficiency and the charge efficiency of the electrochemical energy storage power station; p (P) sd,t And P sc,t Respectively representing the discharge power and the charging power of the electrochemical energy storage power station at the time t; c sc And c sd Respectively representing the charging cost coefficient and the discharging cost coefficient of the electrochemical energy storage power station; c (C) hydro Pumping-generating cost for a pumped storage power station; c f And c c The cost coefficients of power generation and water pumping are respectively; p (P) gen,t And P pump,t Respectively generating power and pumping power of a hydroelectric unit of the pumped storage power station at the time t; s is S t For the electricity storage quantity of the electrochemical energy storage power station at the moment t, theta i The self-loss rate of the electrochemical energy storage power station is; u (u) sc,t And u sd,t Respectively representing a charging point state and a discharging state; s is S t,min And S is t,max The upper and lower limits of the capacity of the electrochemical energy storage power station are set; p (P) sc,max And P sd,max Respectively representing the maximum charging power and the maximum discharging power of the electrochemical energy storage power station; e (E) p,t Reservoir capacity for the pumped storage power station t period; beta c And beta f Respectively pumping efficiency and generating efficiency of the pumped storage power station; />And->The minimum storage capacity and the maximum storage capacity of the pumped storage power station are respectively; ΔP R Representing the climbing rate of the pumped storage power station; t denotes one scheduling period.
The lower model also comprises a power transmission line operation constraint and a power balance constraint, wherein the power transmission line operation constraint is expressed as follows:
in the method, in the process of the invention,maximum transmission power of a transmission line between the nodes i and j; b (B) ij Is susceptance between nodes i, j; θ i,t Phase at time i node tAngle, theta j,t The phase angle of the moment t of the j node is; the transmission line operation constraint belongs to an indirect constraint, such as that a node has two generators to carry out power transmission on a line, so that the total power transmitted by the two generators cannot exceed the upper transmission limit of the line.
The power balance constraint is expressed as follows:
wherein P is G,j,t The output value of the jth thermal power generating unit at the time t is N G The number of the thermal power generating units; p (P) w,t The output of the wind turbine generator at the time t is obtained; p (P) t Erss The total charge and discharge power of the pumped storage power station and the electrochemical energy storage power station at the moment t; p (P) elv,t Representing the equivalent load at time t; p (P) t loss The power loss at time t of the power system is shown.
S3: and obtaining a load prediction curve, inputting the load prediction curve into an upper model, and solving the upper model to optimize the load prediction curve to obtain an equivalent load curve.
The upper model mainly utilizes the demand response to optimally adjust the load prediction curve, controls the transferable load quantity at each moment, reduces the peak-valley difference of the load, generates an equivalent load curve, enables the equivalent load curve to be more close to the wind power predicted output curve, and plays roles of improving the wind power absorption rate and improving the peak regulation enthusiasm of the conventional unit. The layer model is a single objective function, has relatively few constraint conditions, and can be solved by calling a YALMIP toolbox Gurobi solver by adopting MATLAB.
S4: and obtaining a wind power predicted output curve, combining an equivalent load curve, carrying out multi-objective joint solution on a lower model to obtain an optimal scheduling strategy of the power system, and scheduling the power system based on the optimal scheduling strategy.
And the lower model relies on an equivalent load curve and a wind power prediction force curve transmitted by the upper model to carry out multi-objective optimization solution so as to generate an optimal scheduling strategy of the power system. The layer model has more constraint conditions and nonlinear objective function, so that the model can be optimized and solved by adopting a particle swarm algorithm based on compression factors. The optimal scheduling strategy of the power system comprises a final output plan of each final unit, wherein the final output plan comprises the output of each thermal power unit and the output of the wind power unit, the output of the wind power unit is the consumed wind energy, and the difference between the output of the wind power unit and the predicted output of the wind power unit is the wasted wind energy.
The embodiment of the invention also provides electronic equipment, which comprises:
a memory having a computer program stored thereon;
and the processor is used for loading and executing the computer program to realize the source-net-storage-load double-layer collaborative low-carbon scheduling method based on wind power consumption according to the embodiment.
In a third aspect, a computer readable storage medium is provided, on which a computer program is stored, which when being executed by a processor, implements a source-net-storage-load double-layer collaborative low-carbon scheduling method based on wind power consumption as described in the above embodiments.
It is to be understood that the same or similar parts in the above embodiments may be referred to each other, and that in some embodiments, the same or similar parts in other embodiments may be referred to.
It will be appreciated by those skilled in the art that 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 flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations 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.
While embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the invention, and that variations, modifications, alternatives and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the invention.

Claims (9)

1. A source-network-storage-load double-layer collaborative low-carbon scheduling method based on wind power consumption is characterized by comprising the following steps of:
constructing a demand response model with minimum demand response cost as an upper model;
constructing a thermal power unit cost model with the minimum running cost of the thermal power unit as a target, a carbon emission model with the minimum carbon transaction cost as a target, a wind power plant output model with the maximum wind power consumption and the minimum running cost of wind power as a target, and an energy storage model with the minimum energy storage cost and the maximum income as a target, and combining the four models as a lower model;
acquiring a load prediction curve, inputting the load prediction curve into an upper model, and solving the upper model to optimize the load prediction curve to obtain an equivalent load curve;
acquiring a wind power predicted output curve, and carrying out multi-objective joint solution on a lower model by combining an equivalent load curve to obtain an optimal scheduling strategy of the power system;
and scheduling the power system based on the optimal scheduling strategy.
2. The source-net-storage-load double-layer collaborative low-carbon scheduling method based on wind power consumption according to claim 1, wherein the demand response model targeting at minimum demand response cost is expressed as follows:
P elv,t ＝P load,t +ΔP PDRt +ΔP IDRAt
wherein C is 5 Representing demand response costs; ΔP PDRt 、ΔP IDRAt The call quantity of the PDR load and the class A IDR load at the time t is respectively expressed; k (k) PDR 、k IDRA Calling cost coefficients respectively representing the PDR load and the class A IDR load; ΔQ t Representing the difference between the load at t and the original predicted load after demand response; p (P) load,min And P load,max Representing the most of the original predicted loads in a scheduling periodSmall and maximum values; p (P) load,t Representing the original load at time t; t represents a scheduling period, and Deltat represents a time step; p (P) elv,t Representing the equivalent load at time t, C 6 Representing the difference between the equivalent load curve and the wind power predicted force curve,and predicting a force value for wind power at the moment t of the wind turbine.
3. The source-network-storage-load double-layer collaborative low-carbon scheduling method based on wind power consumption according to claim 1, wherein the thermal power unit cost model targeting at the minimum thermal power unit operation cost is represented as follows:
wherein C is 1 Representing the running cost of the thermal power generating unit; c (C) mh And C qt Respectively representing the coal consumption cost and the start-stop cost of the thermal power unit; a, a j 、b j 、c j The coal consumption coefficient of the j-th thermal power generating unit; p (P) G,j,t The output value of the j-th thermal power unit at the time t is the output value of the j-th thermal power unit at the time t; s is S jt The start-stop cost of the j-th thermal power generating unit; u (u) j,t The power generation system is in a start-stop state of the j-th thermal power generating unit at the moment t; n is n j,t 、m j,t Respectively the firstj thermal power generating units are started and stopped at the moment t;and->Respectively the minimum output and the maximum output of the j-th thermal power unit;the climbing rate of the j-th conventional unit is represented; t represents a scheduling period, N G Is the number of thermal power generating units.
4. The source-net-storage-load double-layer collaborative low-carbon scheduling method based on wind power consumption according to claim 1, wherein a carbon emission model targeting the minimum carbon transaction cost is expressed as follows:
wherein M is L M is the total carbon emission quota of the power system P The total carbon emission of the power system in one period;for the unit electric quantity CO of the j-th thermal power generating unit 2 A discharge distribution coefficient; θ and->The carbon emission distribution coefficients of the pumped storage power station hydroelectric generating set and the wind power station wind generating set are respectively; p (P) G,j,t 、P h,t 、P w,t Respectively a thermal power generating unit, a hydroelectric generating unit and windThe output of the motor unit at the time t; lambda (lambda) j The carbon emission intensity of the power system in one period is as follows; c2 is the total carbon trade cost; />A trade price for carbon; d is the carbon emission interval length; τ is the carbon trade price amplitude of each step of increasing the carbon emission; t represents a scheduling period, N G Is the number of thermal power generating units.
5. The source-net-storage-load double-layer collaborative low-carbon scheduling method based on wind power consumption according to claim 1, wherein the wind power plant output model targeting the maximum wind power consumption and the minimum wind power running cost is represented as follows:
wherein C is 3 Representing the joint cost constructed by the maximum wind power consumption and the minimum total wind power running cost; c (C) w,t 、C cw,t The operation and maintenance cost and the wind abandoning punishment cost of the wind farm are respectively k w 、k cw The operation cost coefficient and the abandoned wind punishment cost coefficient of the wind farm are respectively P w,t The output of the wind turbine generator at the time t,predicting a force value for wind power at the moment t of the wind turbine; t denotes one scheduling period.
6. The source-net-storage-load double-layer collaborative low-carbon scheduling method based on wind power consumption according to claim 1, wherein the energy storage model with the aim of minimum energy storage cost and maximum benefit is expressed as follows:
min C 4 ＝C hydro -C dhx
wherein C is 4 Representing the comprehensive cost of the pumped storage power station and the electrochemical storage power station; c (C) dhx For electrochemical energy storage profit, C sy And C cb Respectively representing the income and the cost of the electrochemical energy storage power station; p is p price The electricity price of the power grid;and->Respectively representing the discharge efficiency and the charge efficiency of the electrochemical energy storage power station; p (P) sd,t And P sc,t Respectively representing the discharge power and the charging power of the electrochemical energy storage power station at the time t; c sc And c sd Respectively representing the charging cost coefficient and the discharging cost coefficient of the electrochemical energy storage power station; c (C) hydro For pumped storage plantsPumping-generating cost; c f And c c The cost coefficients of power generation and water pumping are respectively; p (P) gen,t And P pump,t Respectively generating power and pumping power of a hydroelectric unit of the pumped storage power station at the time t; s is S t For the electricity storage quantity of the electrochemical energy storage power station at the moment t, theta i The self-loss rate of the electrochemical energy storage power station is; u (u) sc,t And u sd,t Respectively representing a charging point state and a discharging state; s is S t,min And S is t,max The upper and lower limits of the capacity of the electrochemical energy storage power station are set; p (P) sc,max And P sd,max Respectively representing the maximum charging power and the maximum discharging power of the electrochemical energy storage power station; e (E) p,t Reservoir capacity for the pumped storage power station t period; beta c And beta f Respectively pumping efficiency and generating efficiency of the pumped storage power station; />And->The minimum storage capacity and the maximum storage capacity of the pumped storage power station are respectively; ΔP R Representing the climbing rate of the pumped storage power station; t denotes one scheduling period.
7. The source-network-storage-load double-layer collaborative low-carbon scheduling method based on wind power consumption according to claim 1, wherein the lower model further comprises a power transmission line operation constraint and a power balance constraint, and the power transmission line operation constraint is expressed as follows:
in the method, in the process of the invention,maximum transmission power of a transmission line between the nodes i and j; b (B) ij Is susceptance between nodes i, j; θ i,t For the phase angle, θ, at the moment of the inode t j,t The phase angle of the moment t of the j node is;
the power balance constraint is expressed as follows:
wherein P is G,j,t The output value of the jth thermal power generating unit at the time t is N G The number of the thermal power generating units; p (P) w,t The output of the wind turbine generator at the time t is obtained; p (P) t Erss The total charge and discharge power of the pumped storage power station and the electrochemical energy storage power station at the moment t; p (P) elv,t Representing the equivalent load at time t; p (P) t loss The power loss at time t of the power system is shown.
8. An electronic device, comprising:
a memory having a computer program stored thereon;
a processor for loading and executing the computer program to implement the source-grid-storage-load double-layer collaborative low-carbon scheduling method based on wind power consumption according to any one of claims 1 to 7.
9. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements a source-net-storage-load bi-layer collaborative low-carbon scheduling method based on wind power consumption according to any one of claims 1 to 7.
CN202311239235.1A 2023-09-25 2023-09-25 Source-net-storage-load double-layer collaborative low-carbon scheduling method, equipment and medium based on wind power consumption Pending CN117254503A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117689184A (en) * 2024-02-02 2024-03-12 山东科技大学 Power system planning method and system considering cooperation of load side and low carbon-economy

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117689184A (en) * 2024-02-02 2024-03-12 山东科技大学 Power system planning method and system considering cooperation of load side and low carbon-economy
CN117689184B (en) * 2024-02-02 2024-04-19 山东科技大学 Power system planning method and system considering cooperation of load side and low carbon-economy

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