CN114662741A - Method for aggregation management of wind power, photovoltaic power generation and energy storage in virtual power plant - Google Patents

Method for aggregation management of wind power, photovoltaic power generation and energy storage in virtual power plant Download PDF

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CN114662741A
CN114662741A CN202210222724.5A CN202210222724A CN114662741A CN 114662741 A CN114662741 A CN 114662741A CN 202210222724 A CN202210222724 A CN 202210222724A CN 114662741 A CN114662741 A CN 114662741A
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energy storage
power
storage device
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李凌
卓毅鑫
胡甲秋
黄馗
唐健
李秋文
莫东
梁振成
邓秋荃
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Guangxi Power Grid Co Ltd
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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
    • H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
    • 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
    • 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
    • H02J3/28—Arrangements for balancing of the load in a network by storage of 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
    • 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
    • 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
    • H02J3/381—Dispersed generators
    • 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
    • H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
    • 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
    • H02J2300/22—The renewable source being solar energy
    • H02J2300/24—The renewable source being solar energy of photovoltaic origin
    • 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

Abstract

The invention discloses a method for aggregation management of wind power, photovoltaic power generation and energy storage in a virtual power plant; preprocessing the electric power data before the operation day; establishing a primary optimized scheduling model according to the preprocessed data; obtaining scheduling comprehensive data through a primary optimization scheduling model; establishing a real-time convex quadratic optimization model according to scheduling data; obtaining a real-time charging and discharging power curve of the energy storage equipment through a real-time convex secondary optimization model; the aim of minimizing the energy exchange cost between the virtual power plant and the power grid is achieved, and a day-ahead charging and discharging plan of the energy storage equipment is determined; the virtual power plant operates a power plant power generation plan issued by the day before based on the power spot market, and combines real-time output prediction of the wind power and the photovoltaic unit and conventional load prediction to carry out charge and discharge scheduling on the energy storage equipment, so that the real-time output plan of the virtual power plant is consistent with the output plan before the operation day.

Description

Method for aggregation management of wind power, photovoltaic power generation and energy storage in virtual power plant
Technical Field
The invention relates to the technical field of electric power energy application, in particular to a method for aggregation management of wind power generation, photovoltaic power generation and energy storage in a virtual power plant.
Background
With the increasingly prominent problems of energy shortage, environmental pollution and the like in the world, new energy power generation such as wind power generation and photovoltaic power generation is adopted and valued by more and more countries and regions. The virtual power plant can aggregate various energy resources (including controllable loads, an energy storage system, a new energy unit, an electric automobile, distributed energy and the like), can be used as a positive power plant to supply power to the system for peak shaving, can also be used as a negative power plant to increase load consumption, and is matched with the system for valley filling. The virtual power plant obtains profits by regulating and controlling aggregated resources of the virtual power plant, and carries out economic compensation on the participation excitation type demand response load. Therefore, how to guarantee the benefits of the virtual power plant in the electric power spot market and reduce the benefits fluctuation is an important problem to be solved by the optimized scheduling of the virtual power plant.
In the aspect of the day ahead of the operation, based on the short-term (day ahead of the operation) output prediction of the wind and light unit and the short-term (day ahead of the operation) prediction of the conventional load, the charging and discharging plans of the energy storage device in each period are determined by combining the historical charging and discharging curves of the energy storage device similar to the actual day, and then the output plan of the virtual power plant participating in the electric power spot market is determined.
In a real-time level, a day-ahead method is continuously used, and the charging and discharging plan of the energy storage equipment in the real-time level is determined by using the ultra-short-term (real-time) output prediction of the wind and light unit and the ultra-short-term (real-time) prediction of the conventional load, but the deviation between the day-ahead prediction and the real-time prediction of the wind and light unit and the conventional load may cause the plan of the virtual power plant in the real-time level and the plan declared in the day-ahead, so that the virtual power plant is examined by the electric power spot market, and the final income of the virtual power plant is influenced.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a method for aggregation management of wind power, photovoltaic power generation and energy storage in a virtual power plant, which minimizes the energy exchange cost between the virtual power plant and a power grid and enables the output plan of the virtual power plant in real time to be consistent with the output plan before the operation day.
In order to achieve the purpose, the invention adopts the following technical scheme:
s101, preprocessing power data before an operation day;
s102, establishing a primary optimized scheduling model according to the preprocessed data;
s103, obtaining scheduling comprehensive data through a primary optimization scheduling model;
s104, establishing a real-time convex quadratic optimization model according to the scheduling comprehensive data;
and S105, obtaining a real-time charge and discharge power curve of the energy storage device through the real-time convex quadratic optimization model.
Further, the power data comprises a wind turbine generator output plan, a photovoltaic generator output plan, energy storage equipment charging and discharging, a conventional load and a clearing price.
Further, the pretreatment comprises the following steps:
determining the lowest cost of energy conversion between the virtual power plant and the power grid;
and performing charging and discharging scheduling processing on the energy storage equipment.
Further, the pretreatment further comprises the following steps: and performing condition constraint on energy conversion between the virtual power plant and the power grid.
Further, the objective function of the primary optimization scheduling model is as follows:
Figure BDA0003538100450000021
Figure BDA0003538100450000022
wherein the content of the first and second substances,
Figure BDA0003538100450000023
the state of charge at the ith energy storage device time t,
Figure BDA0003538100450000024
the state of charge of the ith energy storage device at time t-1,
Figure BDA0003538100450000025
for the charging efficiency of the ith energy storage device,
Figure BDA0003538100450000026
for the discharge efficiency of the ith energy storage device,
Figure BDA0003538100450000027
for the charging power of the ith energy storage device for the period t,
Figure BDA0003538100450000028
for the discharge power of the ith energy storage device for the t period,
Figure BDA0003538100450000031
is the rated capacity of the ith energy storage device, deltat is the time difference between the t-1 moment and the t moment,
Figure BDA0003538100450000032
Figure BDA0003538100450000033
the minimum value and the maximum value of the charge state of the ith energy storage device on the premise of ensuring the safety of the device,
Figure BDA0003538100450000034
and determining the range of the calculation time period for the set of the grid-connected energy storage equipment in the time period T, wherein T is the set of the time period T.
Further, the scheduling comprehensive data comprises a planned output curve before the operation day of the virtual power plant, expected power of all energy storage devices, a wind turbine generator output plan, a photovoltaic generator output plan, a conventional load prediction and a charging and discharging plan before the operation day of the energy storage devices.
Further, the expected power calculation formula for all energy storage devices is as follows:
Figure BDA0003538100450000035
wherein the content of the first and second substances,
Figure BDA0003538100450000036
for the expected power of all energy storage devices at time step t,
Figure BDA0003538100450000037
for real-time scheduling of a tracked day-ahead plan,
Figure BDA0003538100450000038
for the normal load power at the time of real-time scheduling,
Figure BDA0003538100450000039
for the output of the photovoltaic unit during real-time scheduling,
Figure BDA00035381004500000310
is made ofAnd the wind turbine generator outputs power during scheduling.
Further, the real-time convex quadratic optimization model objective function is as follows:
Figure BDA00035381004500000311
wherein the content of the first and second substances,
Figure BDA00035381004500000312
for a set of k-period grid-connected energy storage devices,
Figure BDA00035381004500000313
for the charging power of the ith energy storage device for the period t,
Figure BDA00035381004500000314
for the discharge power of the ith energy storage device for the t period,
Figure BDA00035381004500000315
the expected power for all energy storage devices at time step k.
Furthermore, the real-time convex quadratic optimization model is constrained, and the calculation formula of the constraint condition of the real-time convex quadratic optimization model is as follows:
Figure BDA00035381004500000316
Figure BDA00035381004500000317
Figure BDA00035381004500000318
Figure BDA00035381004500000319
Figure BDA00035381004500000320
wherein k ∈ (t, τ) is a result of calculating time τ at and after time t,
Figure BDA0003538100450000041
the rated charging power of the ith energy storage device,
Figure BDA0003538100450000042
is the rated discharge power of the ith energy storage device,
Figure BDA0003538100450000043
for the charging power of the ith energy storage device for the k period,
Figure BDA0003538100450000044
for the discharge power of the ith energy storage device for the k period,
Figure BDA0003538100450000045
determining the charging state of the ith energy storage device in the k period as a variable from 0 to 1,
Figure BDA0003538100450000046
determining the discharge state of the ith energy storage device in the k time period for the variable 0-1, NkFor the number of grid-connected energy storage devices during the k time period,
Figure BDA0003538100450000047
the charging and discharging power of the energy storage equipment in the non-scheduling time range is 0 at the starting time when the ith energy storage equipment can be scheduled,
Figure BDA0003538100450000048
and for the scheduled termination time of the ith energy storage device, the charging and discharging power of the energy storage device in the non-scheduling time range is 0.
Further, a calculation formula of the real-time charge and discharge power curve of the energy storage device is as follows:
Figure BDA0003538100450000049
Figure BDA00035381004500000410
wherein the content of the first and second substances,
Figure BDA00035381004500000411
the state of charge at time k for the ith energy storage device,
Figure BDA00035381004500000412
is the state of charge, η, at time k-1 of the ith energy storage devicei,cFor charging efficiency of the ith energy storage device, ηi,dThe discharge efficiency of the ith energy storage device, the physical parameters of the energy storage device,
Figure BDA00035381004500000413
for the charging power of the ith energy storage device for the k period,
Figure BDA00035381004500000414
for the discharge power of the ith energy storage device for the k period,
Figure BDA00035381004500000415
the rated capacity of the ith energy storage device is shown, and delta t is the time difference between the moment k-1 and the moment k;
Figure BDA00035381004500000416
the minimum value of the charge state of the ith energy storage device on the premise of ensuring the safety of the device,
Figure BDA00035381004500000417
the maximum value of the state of charge, N, of the ith energy storage device on the premise of ensuring the safety of the devicekAnd the k-time period is a set of grid-connected energy storage devices.
The invention has the beneficial effects that: a method for wind power, photovoltaic power generation and energy storage aggregation management in a virtual power plant is characterized in that power data before an operation day is preprocessed; establishing a primary optimized scheduling model according to the preprocessed data; obtaining scheduling comprehensive data through a primary optimization scheduling model; establishing a real-time convex quadratic optimization model according to scheduling data; obtaining a real-time charging and discharging power curve of the energy storage equipment through a real-time convex secondary optimization model; the aim of minimizing the energy exchange cost between the virtual power plant and the power grid is achieved, and a day-ahead charging and discharging plan of the energy storage equipment is determined; the virtual power plant operates a power plant power generation plan published day before on the basis of the power spot market, and combines real-time output prediction and conventional load prediction of the wind power and the photovoltaic generator set to perform charge and discharge scheduling on the energy storage equipment, so that the real-time output plan of the virtual power plant is consistent with the output plan before the operation day, and the assessment of the power spot market caused by output deviation is avoided; the virtual power plant generates more power at the load peak moment (higher electricity price) and uses more power at the load valley moment (lower electricity price), so that the cost of purchasing power from the power grid in the early stage of the operation day of the virtual power plant is minimized, and the pursuit of the profit maximization is realized; in the aspect of real time, the power plant power generation plan published before the operation day is taken as a target, the real-time power generation plan is ensured to be consistent with the power generation plan before the operation day, the risk of checking the spot market of the electric power is reduced, and the benefit of a virtual power plant is ensured.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic diagram of the steps of a method for aggregation management of wind power, photovoltaic power generation and energy storage in a virtual power plant according to the present invention.
Detailed Description
The embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.
The embodiments of the present disclosure are described below with specific examples, and other advantages and effects of the present disclosure will be readily apparent to those skilled in the art from the disclosure in the specification. It is to be understood that the described embodiments are merely illustrative of some, and not restrictive, of the embodiments of the disclosure. The disclosure may be embodied or carried out in various other specific embodiments, and various modifications and changes may be made in the details within the description without departing from the spirit of the disclosure. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict. All other embodiments, which can be derived by a person skilled in the art from the embodiments disclosed herein without making any creative effort, shall fall within the protection scope of the present disclosure.
The first embodiment is as follows:
s101, preprocessing power data before an operation day;
preprocessing electric power data before the operation day, wherein the electric power data comprises an output plan of a wind turbine generator, an output plan of a photovoltaic generator, charging and discharging of energy storage equipment, a conventional load and a clearing price, and the preprocessing comprises the following steps:
determining the lowest cost of energy conversion between the virtual power plant and the power grid;
performing condition constraint on energy conversion between the virtual power plant and the power grid;
and performing charging and discharging scheduling processing on the energy storage equipment.
Determining the lowest cost of energy conversion between the virtual power plant and the power grid, wherein the calculation formula of the energy conversion between the power plant and the power grid is as follows:
Figure BDA0003538100450000061
wherein λ istIn order to operate the day-ahead market electricity prices,
Figure BDA0003538100450000062
for the exchange power between the virtual power plant and the grid in each time step, Δ t is operationAs the granularity of the market time before the day,
Figure BDA0003538100450000063
the power of the power grid is accessed for the virtual power plant,
Figure BDA0003538100450000064
and sending out the power of the power grid for the virtual power plant.
In each time step, in order to make the energy conversion between the virtual power plant and the power grid more accurate, the energy conversion between the virtual power plant and the power grid is constrained, and the constraint condition formula of the energy conversion between the virtual power plant and the power grid is as follows:
Figure BDA0003538100450000065
wherein the content of the first and second substances,
Figure BDA0003538100450000066
the charging power for the ith energy storage device,
Figure BDA0003538100450000067
for the discharge power of the ith energy storage device,
Figure BDA0003538100450000068
for the number of grid-connected energy storage devices during the period t,
Figure BDA0003538100450000069
the predicted power for the regular load is,
Figure BDA00035381004500000610
for the predicted power of the photovoltaic unit for the period t,
Figure BDA00035381004500000611
and the predicted power of the wind turbine generator is obtained in the t time period.
Figure BDA00035381004500000612
Wherein the content of the first and second substances,
Figure BDA00035381004500000613
for the upper limit of the energy exchanged by the virtual power plant to the grid,
Figure BDA00035381004500000614
and exchanging the energy of the virtual power plant to the lower limit value of the power grid.
The energy storage equipment is subjected to charge and discharge scheduling processing, and a charge and discharge scheduling calculation formula of the energy storage equipment is as follows:
Figure BDA0003538100450000071
Figure BDA0003538100450000072
Figure BDA0003538100450000073
Figure BDA0003538100450000074
Figure BDA0003538100450000075
wherein the content of the first and second substances,
Figure BDA0003538100450000076
the rated charging power of the ith energy storage device,
Figure BDA0003538100450000077
rated discharge power for the ith energy storage device,
Figure BDA0003538100450000078
for the charging power of the ith energy storage device for the period t,
Figure BDA0003538100450000079
for the discharge power of the ith energy storage device for the t period,
Figure BDA00035381004500000710
the charging state of the ith energy storage device in the t period is determined for the variable 0-1,
Figure BDA00035381004500000711
the discharge state of the ith energy storage device in the t period is determined for the variable 0-1,
Figure BDA00035381004500000712
determining the range of the calculation time period for the number of the grid-connected devices in the time period T and the set of the time period T,
Figure BDA00035381004500000713
the charging and discharging power of the energy storage equipment in the non-scheduling time range is 0 at the starting time when the ith energy storage equipment can be scheduled,
Figure BDA00035381004500000714
and for the scheduled termination time of the ith energy storage device, the charging and discharging power of the energy storage device in the non-scheduling time range is 0.
S102, establishing a primary optimized scheduling model according to the preprocessed data;
establishing a primary optimization scheduling model according to the preprocessed data, wherein an objective function of the primary optimization scheduling model is as follows:
Figure BDA00035381004500000715
Figure BDA00035381004500000716
wherein the content of the first and second substances,
Figure BDA00035381004500000717
the state of charge at the ith energy storage device time t,
Figure BDA00035381004500000718
the state of charge of the ith energy storage device at time t-1,
Figure BDA00035381004500000719
for the charging efficiency of the ith energy storage device,
Figure BDA00035381004500000720
for the discharge efficiency of the ith energy storage device,
Figure BDA00035381004500000721
for the charging power of the ith energy storage device for the period t,
Figure BDA00035381004500000722
for the discharge power of the ith energy storage device for the t period,
Figure BDA00035381004500000723
is the rated capacity of the ith energy storage device, deltat is the time difference between the t-1 moment and the t moment,
Figure BDA00035381004500000724
Figure BDA00035381004500000725
the minimum value and the maximum value of the charge state of the ith energy storage device on the premise of ensuring the safety of the device,
Figure BDA00035381004500000726
and determining the range of the calculation time period for the set of the grid-connected energy storage equipment in the time period T, wherein T is the set of the time period T.
S103, obtaining scheduling comprehensive data through a primary optimization scheduling model;
and obtaining scheduling comprehensive data through a primary optimization scheduling model, wherein the scheduling comprehensive data comprises a planned output curve before the operation day of the virtual power plant, the expected power of all energy storage equipment, a wind turbine output plan, a photovoltaic unit output plan and a charging and discharging plan before the operation day of the conventional load prediction energy storage equipment.
The calculation formula of the planned output curve before the operation day of the virtual power plant is as follows:
Figure BDA0003538100450000081
wherein the content of the first and second substances,
Figure BDA0003538100450000082
for a day-ahead power plan of a virtual power plant,
Figure BDA0003538100450000083
for each time step the power is exchanged between the virtual power plant and the power grid.
It should be noted that, because the actual output of the wind power generator and the photovoltaic generator set is uncertain and cannot be flexibly scheduled, and the conventional load has no regulation capability, the virtual power plant can only realize the tracking of the day-ahead power plan by scheduling the charging and discharging power of the energy storage device.
The desired power calculation for all energy storage devices is as follows:
Figure BDA0003538100450000084
wherein the content of the first and second substances,
Figure BDA0003538100450000085
for the expected power of all energy storage devices at time step t,
Figure BDA0003538100450000086
for real-time scheduling of a tracked day-ahead plan,
Figure BDA0003538100450000087
for frequent use in real-time schedulingThe power of the load is regulated,
Figure BDA0003538100450000088
for the output of the photovoltaic unit during real-time scheduling,
Figure BDA0003538100450000089
the output of the wind turbine generator is real-time dispatching.
It should be noted that, in order to meet the energy demand for a longer period of time, a rolling time series optimization algorithm is adopted, that is, at the beginning of each time interval, the target time t and the time τ after the target time t are simultaneously optimized. The length of the rolling time window can be set according to actual needs, and the aim of comprehensively scheduling the energy storage equipment to schedule the minimum electric quantity is taken as a target.
S104, establishing a real-time convex quadratic optimization model according to the scheduling comprehensive data;
and establishing a real-time convex quadratic optimization model according to the data, wherein the objective function of the real-time convex quadratic optimization model is as follows:
Figure BDA00035381004500000810
H＝{t+1,t+2,...,t+H}
wherein the content of the first and second substances,
Figure BDA0003538100450000091
for a set of t-period grid-connected energy storage devices,
Figure BDA0003538100450000092
for the charging power of the ith energy storage device for the period t,
Figure BDA0003538100450000093
for the discharge power of the ith energy storage device for the t period,
Figure BDA0003538100450000094
for the expected power of all energy storage devices at time step t,
Figure BDA0003538100450000095
the charging power of the ith energy storage device for period tau,
Figure BDA0003538100450000096
the discharge power of the ith energy storage device for period tau,
Figure BDA0003538100450000097
the expected power for all energy storage devices over the period of τ.
Simplifying the real-time convex quadratic optimization model into:
Figure BDA0003538100450000098
wherein the content of the first and second substances,
Figure BDA0003538100450000099
for a set of k-period grid-connected energy storage devices,
Figure BDA00035381004500000910
for the charging power of the ith energy storage device for the k period,
Figure BDA00035381004500000911
for the discharge power of the ith energy storage device for the k period,
Figure BDA00035381004500000912
the expected power for all energy storage devices at time step k.
And (3) constraining the real-time convex quadratic optimization model, wherein a calculation formula of constraint conditions of the real-time convex quadratic optimization model is as follows:
Figure BDA00035381004500000913
Figure BDA00035381004500000914
Figure BDA00035381004500000915
Figure BDA00035381004500000916
Figure BDA00035381004500000917
wherein k ∈ (t, τ) is a result of calculating time τ at and after time t,
Figure BDA00035381004500000918
the rated charging power of the ith energy storage device,
Figure BDA00035381004500000919
is the rated discharge power of the ith energy storage device,
Figure BDA00035381004500000920
for the charging power of the ith energy storage device during the k period,
Figure BDA00035381004500000921
for the discharge power of the ith energy storage device during the k period,
Figure BDA00035381004500000922
determining the charging state of the ith energy storage device in the k period as a variable from 0 to 1,
Figure BDA00035381004500000923
determining the discharge state of the ith energy storage device in the k period for a variable of 0-1kFor the number of grid-connected energy storage devices for the period k,
Figure BDA00035381004500000924
setting the energy storage within the non-scheduling time range for the scheduled starting time of the ith energy storage deviceThe charge and discharge power of the battery is 0,
Figure BDA00035381004500000925
and for the scheduled termination time of the ith energy storage device, the charging and discharging power of the energy storage device in the non-scheduling time range is 0.
S105, obtaining a real-time charging and discharging power curve of the energy storage device through a real-time convex secondary optimization model;
obtaining a real-time charge and discharge power curve of the energy storage device through the real-time convex quadratic optimization model, wherein a calculation formula of the real-time charge and discharge power curve of the energy storage device is as follows:
Figure BDA0003538100450000101
Figure BDA0003538100450000102
wherein the content of the first and second substances,
Figure BDA0003538100450000103
the state of charge at time k for the ith energy storage device,
Figure BDA0003538100450000104
is the state of charge, η, at time k-1 of the ith energy storage devicei,cFor charging efficiency of the ith energy storage device, ηi,dThe discharge efficiency of the ith energy storage device, the physical parameters of the energy storage device,
Figure BDA0003538100450000105
for the charging power of the ith energy storage device for the k period,
Figure BDA0003538100450000106
for the discharge power of the ith energy storage device for the k period,
Figure BDA0003538100450000107
for the ith energy-storage deviceRated capacity, delta t is the time difference between the k-1 moment and the k moment;
Figure BDA0003538100450000108
the minimum value of the charge state of the ith energy storage device on the premise of ensuring the safety of the device,
Figure BDA0003538100450000109
the maximum value of the state of charge, N, of the ith energy storage device on the premise of ensuring the safety of the equipmentkAnd the k-time period is a set of grid-connected energy storage devices.
In the description of the present invention, it should be noted that the terms "first" and "second" are used only for distinguishing between the descriptions and are not to be construed as indicating or implying relative importance.
The above description is for the purpose of illustrating embodiments of the invention and is not intended to limit the invention, and it will be apparent to those skilled in the art that any modification, equivalent replacement, or improvement made without departing from the spirit and principle of the invention shall fall within the protection scope of the invention.

Claims (10)

1. A method for aggregation management of wind power, photovoltaic power generation and energy storage in a virtual power plant is characterized by comprising the following steps:
s101, preprocessing power data before an operation day;
s102, establishing a primary optimized scheduling model according to the preprocessed data;
s103, obtaining scheduling comprehensive data through the primary optimization scheduling model;
s104, establishing a real-time convex quadratic optimization model according to the scheduling comprehensive data;
and S105, obtaining a real-time charge and discharge power curve of the energy storage device through the real-time convex quadratic optimization model.
2. The method for aggregate management of wind power, photovoltaic power generation and energy storage in a virtual power plant according to claim 1, characterized in that said pre-processing comprises the following steps:
determining the lowest cost of energy conversion between the virtual power plant and the power grid;
and carrying out charging and discharging scheduling processing on the energy storage equipment.
3. The method for aggregate management of wind power, photovoltaic power generation and energy storage in a virtual power plant according to claim 2, wherein the preprocessing further comprises the steps of:
and performing condition constraint on energy conversion between the virtual power plant and the power grid.
4. The method for aggregation management of wind power, photovoltaic power generation and energy storage in a virtual power plant according to claim 1, wherein the objective function of the primary optimization scheduling model is as follows:
Figure FDA0003538100440000011
Figure FDA0003538100440000012
wherein the content of the first and second substances,
Figure FDA0003538100440000013
the state of charge at the ith energy storage device time t,
Figure FDA0003538100440000014
the state of charge of the ith energy storage device at time t-1,
Figure FDA0003538100440000015
for the charging efficiency of the ith energy storage device,
Figure FDA0003538100440000016
for the discharge efficiency of the ith energy storage device,
Figure FDA0003538100440000017
for the charging power of the ith energy storage device for the period t,
Figure FDA0003538100440000018
for the discharge power of the ith energy storage device for the t period,
Figure FDA0003538100440000019
is the rated capacity of the ith energy storage device, deltat is the time difference between the t-1 moment and the t moment,
Figure FDA00035381004400000110
Figure FDA00035381004400000111
the minimum value and the maximum value of the charge state of the ith energy storage device on the premise of ensuring the safety of the device,
Figure FDA00035381004400000112
and determining the range of the calculation time period for the set of the grid-connected energy storage equipment at the time period T, wherein T is the set of the time period T.
5. The method of claim 1, wherein the scheduling integration data comprises planned output curves of the virtual power plant before the operation day, expected powers of all energy storage devices, wind turbine output plans, photovoltaic output plans, routine load predictions, and charging and discharging plans of the energy storage devices before the operation day.
6. The method for aggregation management of wind power, photovoltaic power generation and energy storage in a virtual power plant according to claim 5, wherein the expected power calculation formula of all the energy storage devices is as follows:
Figure FDA0003538100440000021
wherein the content of the first and second substances,
Figure FDA0003538100440000022
for the expected power of all energy storage devices at time step t,
Figure FDA0003538100440000023
for real-time scheduling of a tracked day-ahead plan,
Figure FDA0003538100440000024
for the regular load power when scheduled in real time,
Figure FDA0003538100440000025
the photovoltaic unit output is the photovoltaic unit output in real-time dispatching,
Figure FDA0003538100440000026
the output of the wind turbine generator is real-time dispatching.
7. The method for aggregation management of wind power, photovoltaic power generation and energy storage in a virtual power plant according to claim 1, wherein the objective function of the real-time convex quadratic optimization model is as follows:
Figure FDA0003538100440000027
wherein the content of the first and second substances,
Figure FDA0003538100440000028
for a set of k-period grid-connected energy storage devices,
Figure FDA0003538100440000029
for the charging power of the ith energy storage device for the k period,
Figure FDA00035381004400000210
for the discharge power of the ith energy storage device during the k period,
Figure FDA00035381004400000211
the expected power for all energy storage devices at time step k.
8. The method for aggregation management of wind power, photovoltaic power generation and energy storage in a virtual power plant according to claim 1, wherein the real-time convex quadratic optimization model is constrained, and a calculation formula of constraint conditions of the real-time convex quadratic optimization model is as follows:
Figure FDA00035381004400000212
Figure FDA00035381004400000213
Figure FDA00035381004400000214
Figure FDA0003538100440000031
Figure FDA0003538100440000032
wherein k ∈ (t, τ) is a result of calculating time τ at and after time t,
Figure FDA0003538100440000033
the rated charging power of the ith energy storage device,
Figure FDA0003538100440000034
is the rated discharge power of the ith energy storage device,
Figure FDA0003538100440000035
for the charging power of the ith energy storage device for the k period,
Figure FDA0003538100440000036
for the discharge power of the ith energy storage device during the k period,
Figure FDA0003538100440000037
is a variable between 0 and 1, determines the charging state of the ith energy storage device in the k period,
Figure FDA0003538100440000038
determining the discharge state of the ith energy storage device in the k period for a variable of 0-1kFor the number of grid-connected energy storage devices for the period k,
Figure FDA0003538100440000039
the charging and discharging power of the energy storage equipment in the non-scheduling time range is 0 at the starting time when the ith energy storage equipment can be scheduled,
Figure FDA00035381004400000310
and for the scheduled termination time of the ith energy storage device, the charging and discharging power of the energy storage device in the non-scheduling time range is 0.
9. The method for aggregation management of wind power, photovoltaic power generation and energy storage in a virtual power plant according to claim 1, wherein a calculation formula of a real-time charge and discharge power curve of the energy storage device is as follows:
Figure FDA00035381004400000311
Figure FDA00035381004400000312
wherein the content of the first and second substances,
Figure FDA00035381004400000313
the state of charge at time k for the ith energy storage device,
Figure FDA00035381004400000314
is the state of charge, η, at time k-1 of the ith energy storage devicei,cFor charging efficiency of the ith energy storage device, ηi,dThe discharge efficiency of the ith energy storage device, the physical parameters of the energy storage device,
Figure FDA00035381004400000315
for the charging power of the ith energy storage device during the k period,
Figure FDA00035381004400000316
for the discharge power of the ith energy storage device for the k period,
Figure FDA00035381004400000317
the rated capacity of the ith energy storage device is shown, and delta t is the time difference between the moment k-1 and the moment k;
Figure FDA00035381004400000318
the minimum value of the charge state of the ith energy storage device on the premise of ensuring the safety of the equipment,
Figure FDA00035381004400000319
the maximum value of the state of charge, N, of the ith energy storage device on the premise of ensuring the safety of the devicetAnd the k-time period is a set of grid-connected energy storage devices.
10. The method for aggregate management of wind power, photovoltaic power generation and energy storage in a virtual power plant according to claim 1, wherein the power data comprises a wind turbine output plan, a photovoltaic output plan, energy storage device charging and discharging, regular load and clearing price.
CN202210222724.5A 2022-03-09 2022-03-09 Method for aggregation management of wind power, photovoltaic power generation and energy storage in virtual power plant Pending CN114662741A (en)

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