CN114744687A - Energy regulation and control method and system of virtual power plant - Google Patents

Energy regulation and control method and system of virtual power plant Download PDF

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CN114744687A
CN114744687A CN202210661850.0A CN202210661850A CN114744687A CN 114744687 A CN114744687 A CN 114744687A CN 202210661850 A CN202210661850 A CN 202210661850A CN 114744687 A CN114744687 A CN 114744687A
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power
energy
power plant
capacity
photovoltaic
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CN114744687B (en
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饶亦然
唐猛
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Shenzhen Kezhongyun Technology 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
    • H02J3/38—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
    • 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
    • G06Q10/06312—Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
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    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
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    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • 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
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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
    • H02J3/004—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
    • H02J3/24—Arrangements for preventing or reducing oscillations of power in networks
    • 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
    • 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
    • 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
    • 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
    • H02J2310/62—The condition being non-electrical, e.g. temperature
    • H02J2310/64—The condition being economic, e.g. tariff based load management
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Abstract

The invention discloses an energy regulation and control method and system of a virtual power plant, wherein energy information in VPP of the virtual power plant is obtained, output models of wind power and photovoltaic in distributed energy are analyzed and corrected, different units in the output models are subjected to homogenization treatment, a VPP reliability evaluation model of confidence capacity is established, a flexible load energy model is established, uncertainty treatment is carried out on flexible load to obtain an adjustable power domain of the VPP, and the VPP reliability evaluation model and the adjustable power domain regulate and control the energy of the virtual power plant. The renewable energy output model is corrected, different units are homogenized in combination with confidence capacity, a virtual power plant reliability assessment model of the confidence capacity is established, a virtual power plant dynamic aggregation model is established by taking the reliability index as a target function, the influence of output seasonality and uncertainty on power supply capacity is reduced, the online electricity quantity is also improved, the overall flexible regulation and control capacity of VPP is improved, and resource waste is reduced.

Description

Energy regulation and control method and system of virtual power plant
Technical Field
The invention belongs to the technical field of virtual power plant energy regulation, and particularly relates to an energy regulation and control method and system for a virtual power plant.
Background
At present, with the increase of installed capacity of non-aqueous renewable energy units represented by wind and light, a grid-connection mode is changed from local grid-connection to multi-area centralized and distributed grid-connection, so that the uncertainties of a power generation side and a load side are both greatly increased, higher requirements are put forward for flexible resources of different time scales, and a power system is gradually changed into a renewable energy power-dominant multi-energy complementary power system. However, the power system directly schedules and manages these heterogeneous, distributed and diverse random power sources and flexible resources, which not only cannot bring higher economic benefits to both parties, but also creates a plurality of technical difficulties in stable operation. When pursuit of profit maximization is achieved through static aggregation of Virtual Power Plants (VPPs), important factors needing to be considered when dynamic aggregation of the VPPs are often ignored, namely, reliability of renewable energy Power generation can be achieved, so that the initiative of a Power system for scheduling the VPPs is not high, the phenomenon of wind and light abandonment is serious, and a large amount of resources are wasted.
Disclosure of Invention
In view of the above, the invention provides an energy regulation and control method and system for a virtual power plant, which are implemented by using VPP reliability assessment as an important basis for power grid scheduling and performing outage probability modeling on equipment in each link inside a renewable energy power generation side, so that VPP power supply reliability can be improved, the positivity of a power system for scheduling the power system is improved, and the renewable energy consumption is also improved.
In a first aspect, the invention provides an energy regulation and control method for a virtual power plant, which comprises the following steps:
acquiring energy information in a virtual power plant VPP, and analyzing and correcting a wind power and photovoltaic output model in distributed energy, wherein the energy information comprises distributed energy and flexible load energy;
homogenizing different units in the output model, and establishing a VPP reliability evaluation model of confidence capacity, wherein the VPP reliability evaluation model considering the confidence capacity is established for reliability evaluation of a single wind power plant and a single photovoltaic power station, and the output and load power of the units are kept unchanged within a unit hour;
constructing a flexible load energy model and carrying out uncertainty processing on the flexible load to obtain an adjustable power domain of the VPP;
and regulating and controlling the energy of the virtual power plant based on the VPP reliability evaluation model and the adjustable power domain.
As a further improvement of the above technical solution, constructing a flexible load energy model and performing uncertainty processing on the flexible load to obtain an adjustable power domain of a VPP, comprising: the power inequality constraints of the power generation side unit in the virtual power plant comprise power constraints, climbing constraints and capacity constraints, and the expressions are respectively:
Figure 132418DEST_PATH_IMAGE001
wherein
Figure 780568DEST_PATH_IMAGE002
For the lower power limit of the distributed energy at the moment t,
Figure 55692DEST_PATH_IMAGE003
the method comprises the steps of setting a distributed energy source power upper limit at the moment T, setting a distributed energy source actual power at the moment T, and setting T as a regulation and control time period of the distributed energy source;
the expression of the climbing constraint is
Figure 495900DEST_PATH_IMAGE005
In which
Figure 565488DEST_PATH_IMAGE006
For the lower limit of the distributed energy t time climbing,
Figure 465048DEST_PATH_IMAGE007
the upper limit of the distributed energy climbing at the moment t is; the capacity constraint is expressed as
Figure 75021DEST_PATH_IMAGE009
Wherein
Figure 307419DEST_PATH_IMAGE010
The energy stored for the distributed energy source at time t,
Figure 220012DEST_PATH_IMAGE011
for the rate of energy dissipation of the distributed energy source,
Figure 232967DEST_PATH_IMAGE012
for the charging power at the distributed energy source time t,
Figure 849893DEST_PATH_IMAGE013
in order to increase the charging efficiency of the distributed energy,
Figure 608902DEST_PATH_IMAGE014
for the discharge power at the moment t of the distributed energy source,
Figure 551450DEST_PATH_IMAGE015
in order to achieve the efficiency of the discharge of the distributed energy source,
Figure 520543DEST_PATH_IMAGE016
for a lower limit of the amount of storable energy at time t for a distributed energy source,
Figure 347685DEST_PATH_IMAGE017
an upper limit value of the storable energy for the distributed energy at the moment t;
the process of the adjustable power domain aggregation algorithm of the virtual power plant comprises the following steps:
determining respective adjustable power domains based on power constraints of the distributed energy sources, expressed as
Figure 616992DEST_PATH_IMAGE019
Wherein
Figure 199283DEST_PATH_IMAGE020
The adjustable power domain that satisfies the power constraint for the distributed energy source j,
Figure 298082DEST_PATH_IMAGE020
for regulating power by distributed energy j at each moment in the scheduling period T
Figure 256811DEST_PATH_IMAGE021
A constructed column vector element;
aggregating the adjustable power domains of the distributed energy sources to obtain the adjustable power domains of the virtual power plants, wherein the expression is
Figure 380625DEST_PATH_IMAGE023
Wherein
Figure 399397DEST_PATH_IMAGE024
To satisfy the adjustable power domain of all distributed energy power constraints for a virtual power plant,
Figure 484027DEST_PATH_IMAGE025
for regulating power by virtual power plants at various times during the scheduling period T
Figure 980868DEST_PATH_IMAGE026
The formed column vector elements, J is the quantity of distributed energy sources in the virtual power plant;
and removing all distributed energy source adjusting power variables in the adjustable power domain of the virtual power plant, and reserving the adjusting power variables of the virtual power plant to obtain an adjustable power domain aggregation model of the virtual power plant.
As a further improvement of the above technical solution, when the power inequality constraint of the distributed energy resources contains a discrete variable, aggregating the adjustable power domains of the distributed energy resources containing the discrete variable in the power inequality constraint with the same type and parameter includes:
carrying out transformation processing on the representation forms of the distributed energy source adjustable power domains to enable the representation forms of the various distributed energy source adjustable power domains to have the same structure and different parameters;
and combining power constraints of all distributed energy sources, mapping the adjustable power domain of the virtual power plant to a geometric space to be a high-dimensional convex polyhedron, adopting the selected high-dimensional convex polyhedron to approximately solve the high-dimensional convex polyhedron from inside or outside, and using the convex polyhedron obtained by the approximate approximation solution to represent the adjustable power domain of the virtual power plant.
As a further improvement of the above technical solution, the homogenization treatment of different units in the output model includes:
reliability indexes of the power shortage time probability, the power shortage time expectation and the power shortage expectation value are selected, and the reliability of the wind power plant and the photovoltaic power station is evaluated from the power failure probability, the power failure time and the power failure power quantity respectively; the expected value of the electric quantity shortage represents the power failure times, the average duration and the average power failure power, and the probability expressions of the electric quantity shortage time of the single wind power output unit and the photovoltaic output unit are
Figure 959188DEST_PATH_IMAGE027
Wherein
Figure 148861DEST_PATH_IMAGE028
The probability of the power shortage time is obtained,
Figure 720788DEST_PATH_IMAGE029
the probability of outage occurring while in system state i,
Figure 21319DEST_PATH_IMAGE030
the time length of shutdown when the system is in a system state l;
the expected expressions of the insufficient power time of the single wind power output unit and the photovoltaic output unit are
Figure 854146DEST_PATH_IMAGE031
In which
Figure 214720DEST_PATH_IMAGE032
For the expectation of the time when the power is insufficient,
Figure 8364DEST_PATH_IMAGE033
the probability that the outage capacity of the flight group is greater than or equal to the spare capacity at the z-th day of the e-th time period,
Figure 643744DEST_PATH_IMAGE034
for the installed capacity of the system for the e-th time slot,
Figure 970558DEST_PATH_IMAGE035
the peak load at day z for the e-th session,
Figure 502034DEST_PATH_IMAGE036
is the number of time segments in a year,
Figure 642028DEST_PATH_IMAGE037
the index can judge the probability that the outage capacity of the power system unit is greater than or equal to the spare capacity in the number of days in the z-th time period;
the expression of the expected value of insufficient electric quantity of a single wind power output unit and a single photovoltaic output unit is
Figure 487624DEST_PATH_IMAGE038
Wherein
Figure 29464DEST_PATH_IMAGE039
In order to have the expected value of the power shortage,
Figure 731841DEST_PATH_IMAGE040
is as follows
Figure 500077DEST_PATH_IMAGE042
The outage capacity of the hour unit is more than or equal to
Figure 680522DEST_PATH_IMAGE044
The probability of (a) of (b) being,
Figure 811289DEST_PATH_IMAGE045
is a first
Figure 746884DEST_PATH_IMAGE046
The installed capacity in the system is one hour,
Figure 64733DEST_PATH_IMAGE047
is as follows
Figure 986553DEST_PATH_IMAGE048
The load of the hour, T is the number of simulated hours, and the index is used for reflecting the expected value of reducing power supply for users when the power system unit is forced to stop.
As a further improvement of the above technical solution, reliability evaluation of a wind farm and a photovoltaic power station is performed by accumulating time sequence state distributions of all wind power output units and photovoltaic output units in the station to obtain time sequence state distributions of a single wind farm and a single photovoltaic power station on the basis of obtaining time sequence state distributions of a single wind power output unit and a single photovoltaic output unit by calculation; calculating the reliability indexes of the single wind power plant and the single photovoltaic power station according to the time sequence state distribution, wherein the expression is
Figure 706247DEST_PATH_IMAGE049
，
Figure 812743DEST_PATH_IMAGE051
In which
Figure 617888DEST_PATH_IMAGE052
In order to be a function of the low battery expectation,
Figure 856582DEST_PATH_IMAGE053
for the system state at the qth time point in the Y simulation,
Figure 430783DEST_PATH_IMAGE054
for the system to be in a state
Figure 708181DEST_PATH_IMAGE055
The duration of the time period of the first,
Figure 735043DEST_PATH_IMAGE056
is the number of the states of the system,
Figure 264244DEST_PATH_IMAGE057
in order to simulate the number of calculations,
Figure 692951DEST_PATH_IMAGE058
and calculating the expected value of the insufficient electric quantity of the wind power plant or the photovoltaic power plant for the Yth time.
As a further improvement of the technical scheme, the establishment of a VPP reliability evaluation model of confidence capacity comprises the following steps:
the method comprises the steps of using the capacity of a wind power plant or a photovoltaic power station instead of a conventional unit to evaluate the confidence capacity of the power plant or the power station, and obtaining the reliability indexes of the wind power plant and the photovoltaic power station by adopting sequential Monte Carlo calculation
Figure 141250DEST_PATH_IMAGE059
；
Photovoltaic power station installed capacity according to wind power occasion
Figure 920987DEST_PATH_IMAGE060
Obtaining corresponding reliability indexes and drawing to obtain the wind power station and the photovoltaic power station
Figure 988301DEST_PATH_IMAGE061
A curve;
adopting wind power plant to replace conventional unit, and installing capacity according to conventional unit
Figure 271514DEST_PATH_IMAGE062
Obtaining corresponding reliability indexes by biological difference, and drawing wind power plant to replace conventional unit
Figure 890715DEST_PATH_IMAGE063
With curved and photovoltaic power stations replacing conventional units
Figure 829852DEST_PATH_IMAGE064
A curve;
when the capacity of the wind farm is
Figure 294331DEST_PATH_IMAGE065
At first, firstly
Figure 697631DEST_PATH_IMAGE066
On the curveFinding out wind farm capacity
Figure 127213DEST_PATH_IMAGE067
Corresponding reliability index
Figure 350384DEST_PATH_IMAGE068
Then according to the value
Figure 149712DEST_PATH_IMAGE069
Finding the corresponding capacity on the curve
Figure 79622DEST_PATH_IMAGE070
The product is
Figure 978308DEST_PATH_IMAGE071
The value is the confidence capacity of the wind power plant, and the confidence capacity of the photovoltaic power station is correspondingly obtained.
As a further improvement of the technical scheme, the confidence capacity calculation formula of the wind power plant and the photovoltaic power station is
Figure 16671DEST_PATH_IMAGE072
Wherein
Figure 291795DEST_PATH_IMAGE073
In order to be a function of the low battery expectation,
Figure 341790DEST_PATH_IMAGE074
is a power system load;
the total confidence capacity calculation expression of all wind power plants and photovoltaic power stations is
Figure 676957DEST_PATH_IMAGE076
Wherein
Figure 937037DEST_PATH_IMAGE077
The total confidence capacity of all wind power plants and photovoltaic power stations, M is the number of all wind power plants and photovoltaic power stations,
Figure 15851DEST_PATH_IMAGE078
the confidence capacity of the u wind power plant or photovoltaic power plant;
the reliability index of VPP is calculated by the following formula
Figure 513829DEST_PATH_IMAGE080
The expression of the total confidence capacity of all wind power plants and photovoltaic power stations is combined to obtain
Figure 692000DEST_PATH_IMAGE082
The reliability of a VPP constructed from different types of energy sources can be evaluated by this method.
As a further improvement of the above technical solution, analyzing and correcting the output model of wind power and photovoltaic in the distributed energy includes:
the virtual power plant predicts the output of the next-day distributed renewable energy according to historical data statistics and prediction information, and a wind speed probability density function based on parameter Weibull distribution is
Figure 642639DEST_PATH_IMAGE084
Wherein v is the wind speed value, k and c are the shape parameter and the proportion parameter respectively, and satisfy
Figure 321882DEST_PATH_IMAGE085
；
The Beta distribution-based illumination intensity probability density function is
Figure 408786DEST_PATH_IMAGE087
Where w is the intensity of the illumination, and the subscript max indicates its maximum value,
Figure 259324DEST_PATH_IMAGE088
respectively the shape parameters of the Beta distribution,
Figure 431680DEST_PATH_IMAGE089
is a gamma function. As a further improvement of the above technical solution, acquiring energy information in the VPP of the virtual power plant includes:
the method comprises the steps of respectively modeling various flexible loads and energy equipment to obtain various energy sources to carry out coordinated optimization scheduling, wherein the flexible loads comprise translatable loads, translatable loads and reducible loads, and the energy equipment comprises a wind generating set, a photovoltaic generating set, a combined heat and power generation set and energy storage equipment.
In a second aspect, the present invention further provides an energy regulation and control system of a virtual power plant, including:
the acquisition module is used for acquiring energy information in a virtual power plant VPP, and analyzing and correcting a wind power and photovoltaic output model in distributed energy, wherein the energy information comprises distributed energy and flexible load energy;
the first construction module is used for carrying out homogenization treatment on different units in the output model and establishing a VPP reliability evaluation model of confidence capacity, wherein the VPP reliability evaluation model considering the confidence capacity is constructed for reliability evaluation of a single wind power plant and a single photovoltaic power station, and the output and load power of the units are kept unchanged in unit hour;
the second construction module is used for constructing a flexible load energy model and carrying out uncertainty processing on the flexible load to obtain an adjustable power domain of the VPP;
and the regulation and control module is used for regulating and controlling the energy of the virtual power plant based on the VPP reliability assessment model and the adjustable power domain.
The invention provides an energy regulation and control method and system of a virtual power plant, wherein output models of wind power and photovoltaic in distributed energy are analyzed and corrected by acquiring energy information in VPP of the virtual power plant, different units in the output models are subjected to homogenization treatment, a VPP reliability evaluation model of confidence capacity is established, a flexible load energy model is established, uncertainty treatment is carried out on flexible load to obtain an adjustable power domain of the VPP, and the energy of the virtual power plant is regulated and controlled based on the VPP reliability evaluation model and the adjustable power domain. The output model is corrected by using the outage probability of elements in the renewable energy output model, different units are subjected to homogenization treatment by combining with the confidence capacity, a virtual power plant reliability assessment model of the confidence capacity is established, a dynamic aggregation model of the virtual power plant is established by taking the reliability index as a target function, the influence of the seasonality and uncertainty of the output of the virtual power plant on the power supply capacity can be reduced, the online electric quantity can be improved, the overall flexible regulation and control capacity of the VPP is further improved, and therefore the resource waste is reduced.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
FIG. 1 is a flow chart of a method for energy regulation of a virtual power plant according to the present invention;
FIG. 2 is a flow diagram of a confidence-capability VPP reliability evaluation model of the present invention;
fig. 3 is a block diagram of an energy regulation system of a virtual power plant according to the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
Referring to fig. 1, the invention provides an energy regulation method of a virtual power plant, comprising the following steps:
s1: acquiring energy information in a virtual power plant VPP, and analyzing and correcting a wind power and photovoltaic output model in distributed energy, wherein the energy information comprises distributed energy and flexible load energy;
s2: homogenizing different units in the output model, and establishing a VPP reliability evaluation model of confidence capacity, wherein the VPP reliability evaluation model considering the confidence capacity is established for reliability evaluation of a single wind power plant and a single photovoltaic power station, and the output and load power of the units are kept unchanged within a unit hour;
s3: constructing a flexible load energy model and carrying out uncertainty processing on the flexible load to obtain an adjustable power domain of the VPP;
s4: and regulating and controlling the energy of the virtual power plant based on the VPP reliability evaluation model and the adjustable power domain.
In the embodiment, the virtual power plant is not limited by an energy framework of an original power grid, scattered and fragmented distributed energy sources and flexible loads on a user side are aggregated by an advanced communication technology and a control technology, a high-power and high-capacity stably adjustable resource pool is formed, the regional limitation is avoided, an existing mode that multiple energy sources are separately planned and independently operated is broken, and the virtual power plant is an effective mode for promoting complementation among different energy sources, improving renewable energy consumption and enhancing flexible load management and optimization. Under the coordination of the power generation side unit and the user side unit, the overall operation characteristic of the virtual power plant can be greatly improved. The cost-benefit functions of the power generation side units in the virtual power plant mainly comprise power generation cost and power selling income, although the investment cost of new energy generating sets such as wind power generation, photovoltaic power generation and the like is high, the operation cost is low, the cost-benefit functions generally only consider the power selling income, the cost-benefit functions of conventional controllable sets such as gas turbines, fuel oil sets and the like are composed of the power selling income, the fuel cost and the environmental cost, the relation between the adjusting power and the adjusting cost can be described by using a multivariate quadratic function, and the cost-benefit functions of energy storage devices such as chemical energy storage, pumped storage, hydrogen energy storage and the like are composed of net benefits under the charging and discharging power. The user side unit of the virtual power plant relates to temperature control load, electric automobile and other resident loads, and the cost benefit function of the virtual power plant needs to be a nonlinear function of comfort loss cost and power regulation income brought by demand response besides electricity purchasing cost.
It should be noted that, multiple flexible loads and energy devices are respectively modeled to obtain multiple energy sources for coordinated optimization scheduling, the flexible loads include translatable loads, translatable loads and reducible loads, and the energy devices include wind generating sets, photovoltaic generating sets, cogeneration sets and energy storage devices. The technical characteristics of the distributed energy are described in a power inequality constraint mode, the power inequality constraints of various distributed energy have large difference, and power variables at various moments in the power inequality constraints have coupling relations. The user side unit of the virtual power plant can be divided into four categories of rigid load, transferable load, interruptable load and reducible load according to different load characteristics, the rigid load is a load which has a large influence on the life of a user and meets the power consumption requirement of the user immediately, the power of the user at each moment is a fixed value, the transferable load continuously works until the task is completed when running, the user can not interrupt but can integrally advance or delay the working period, the interruptable load can interrupt the running during the task completion process, but the accumulated running time is unchanged, the load can be reduced, the power consumption can be reduced in the power consumption peak period, and the power consumption can be increased in the power consumption valley period.
It should be understood that the adjustable power domains of the virtual power plant are collectively represented by the adjustable power domains of several distributed energy clusters within the virtual power plant. The VPP dynamic polymerization process is as follows: the method comprises the steps of firstly analyzing and correcting output model models of wind power and photovoltaic, then carrying out homogenization treatment on different units, establishing a VPP reliability evaluation model of Carlo confidence capacity, and then constructing a VPP dynamic aggregation model of renewable energy reliability by taking the minimum expected value of insufficient electric quantity as an optimization target. The flexible load is subjected to uncertainty processing to fully excavate the potential of a user side unit, namely a load side flexible energy, a plurality of flexible load mathematical models are established, including the flexible load which can be translated, transferred and reduced, the diversified utilization of energy can be promoted, the flexible loads are classified according to different operating characteristics of the user side load, the plurality of flexible loads and the VPP reliability assessment model are coordinated and optimized, the peak power supply pressure can be effectively relieved, the load fluctuation is reduced, and the zero-flower regulation capacity of a virtual power plant is enhanced.
Optionally, constructing a flexible load energy model and performing uncertainty processing on the flexible load to obtain an adjustable power domain of the VPP, including:
the power inequality constraints of the power generation side unit in the virtual power plant comprise power constraints, climbing constraints and capacity constraints, and the expressions are respectively:
Figure 914613DEST_PATH_IMAGE090
wherein
Figure 856025DEST_PATH_IMAGE091
For the lower power limit of the distributed energy at the moment t,
Figure 641578DEST_PATH_IMAGE092
for the upper power limit of the distributed energy source at the moment t,
Figure 301230DEST_PATH_IMAGE093
actual power at the moment T of the distributed energy, wherein T is the regulation and control time period of the distributed energy;
the expression of the climbing constraint is
Figure 322275DEST_PATH_IMAGE094
Wherein
Figure 383772DEST_PATH_IMAGE095
For the lower limit of the distributed energy t time climbing,
Figure 340227DEST_PATH_IMAGE096
the climbing upper limit at the moment t of the distributed energy; the capacity constraint is expressed as
Figure 487174DEST_PATH_IMAGE097
Wherein
Figure 249594DEST_PATH_IMAGE098
The energy stored for the distributed energy source at time t,
Figure 227914DEST_PATH_IMAGE099
for the rate of energy dissipation of the distributed energy source,
Figure 89691DEST_PATH_IMAGE100
for the charging power at the distributed energy source time t,
Figure 723935DEST_PATH_IMAGE101
in order to increase the charging efficiency of the distributed energy,
Figure 86783DEST_PATH_IMAGE102
for the discharge power at the moment t of the distributed energy source,
Figure 857293DEST_PATH_IMAGE103
in order to achieve the efficiency of the discharge of the distributed energy source,
Figure 654085DEST_PATH_IMAGE104
for a lower limit of the amount of storable energy at time t for a distributed energy source,
Figure 510046DEST_PATH_IMAGE105
an upper limit value of the storable energy for the distributed energy at the moment t;
the process of the adjustable power domain aggregation algorithm of the virtual power plant comprises the following steps:
determining respective adjustable power domains based on power constraints of the distributed energy sources, expressed as
Figure 676585DEST_PATH_IMAGE107
Wherein
Figure 567181DEST_PATH_IMAGE109
The adjustable power domain that satisfies the power constraint for distributed energy j,
Figure 36339DEST_PATH_IMAGE111
for regulating power by distributed energy j at each moment in the scheduling period T
Figure 176333DEST_PATH_IMAGE112
The column vector elements of the construct;
aggregating the adjustable power domains of the distributed energy sources to obtain the adjustable power domains of the virtual power plants, wherein the expression is
Figure 818667DEST_PATH_IMAGE114
Wherein
Figure 501453DEST_PATH_IMAGE115
Is deficiency ofThe proposed power plant satisfies the adjustable power domain of all distributed energy power constraints,
Figure 203829DEST_PATH_IMAGE116
for regulating power by virtual power plants at various times during the scheduling period T
Figure 96699DEST_PATH_IMAGE117
The formed column vector elements, J is the quantity of distributed energy sources in the virtual power plant; and removing all distributed energy source adjusting power variables in the adjustable power domain of the virtual power plant, and reserving the adjusting power variables of the virtual power plant to obtain an adjustable power domain aggregation model of the virtual power plant.
In this embodiment, when the power inequality constraint of the distributed energy resource includes a discrete variable, aggregating the adjustable power domain of the distributed energy resource including the discrete variable in the power inequality constraint with the same type and parameter includes: carrying out transformation processing on the representation forms of the distributed energy source adjustable power domains to enable the representation forms of the various distributed energy source adjustable power domains to have the same structure and different parameters; and combining power constraints of all distributed energy sources, mapping the adjustable power domain of the virtual power plant to a geometric space to be a high-dimensional convex polyhedron, adopting the selected high-dimensional convex polyhedron to approximately solve the high-dimensional convex polyhedron from inside or outside, and using the convex polyhedron obtained by the approximate approximation solution to represent the adjustable power domain of the virtual power plant. The convex polyhedron obtained by approximate approximation solution is used for ensuring the adjustable power domain of the virtual power plant, and the mathematical model corresponding to the selected high-dimensional convex polyhedron comprises a virtual battery model and a virtual generator model.
It should be noted that, the overall approximate solution process of the virtual battery model is as follows: the virtual battery model is suitable for a virtual power plant consisting of an energy storage device or a flexible load, and the mathematical model is
Figure 808303DEST_PATH_IMAGE119
Wherein
Figure 345595DEST_PATH_IMAGE120
The power domain may be adjusted for the virtual power plant described by the virtual battery model,
Figure 218873DEST_PATH_IMAGE121
for regulating power by virtual power plants at various times during the scheduling period T
Figure 333459DEST_PATH_IMAGE122
The elements of the column vector of the construct,
Figure 583175DEST_PATH_IMAGE123
for the lower power limit of the virtual battery model,
Figure 742017DEST_PATH_IMAGE124
is the upper power limit of the virtual battery model,
Figure 786197DEST_PATH_IMAGE125
the electrical energy stored for the virtual battery model at time t,
Figure 388079DEST_PATH_IMAGE126
is the lower limit value of the electric energy of the virtual battery model,
Figure 175907DEST_PATH_IMAGE127
the electric energy upper limit value of the virtual battery model. In order to represent the adjustable power domain of the virtual power plant, the upper and lower power limits and the upper and lower electric energy limits of the virtual battery model need to be determined according to the technical characteristics of all distributed energy sources in the virtual power plant, and the method can be approximately equivalent to searching the inscribed right-angle pyramid with the longest side length on the high-dimensional convex polyhedron corresponding to the adjustable power domain of the virtual power plant.
In addition, the overall approximate solving process of the virtual generator model comprises the following steps: the virtual generator model is suitable for a virtual power plant consisting of wind power generation, photovoltaic power generation or a conventional controllable unit, and the mathematical model is
Figure 422212DEST_PATH_IMAGE129
Wherein
Figure 699609DEST_PATH_IMAGE130
The power domain may be adjusted for the virtual plant described by the virtual generator model,
Figure 992050DEST_PATH_IMAGE131
for regulating power by virtual power plants at various times during a scheduling period
Figure 255672DEST_PATH_IMAGE132
The elements of the column vector of the construct,
Figure 949959DEST_PATH_IMAGE133
the lower power limit of the virtual generator model,
Figure 398258DEST_PATH_IMAGE134
for the upper power limit of the virtual generator model,
Figure 912416DEST_PATH_IMAGE135
is the lower limit of the climbing of the virtual generator model,
Figure 979729DEST_PATH_IMAGE136
the upper limit of the climbing of the virtual generator model. In order to represent the adjustable power domain of the virtual power plant, the upper limit and the lower limit of the power of a virtual generator model and the upper limit and the lower limit of the climbing slope need to be determined according to the technical characteristics of all distributed energy sources in the virtual power plant, and the method can be approximately equivalent to searching an inscribed square polyhedron with the longest side length in a high-dimensional convex polyhedron corresponding to the adjustable power domain of the virtual power plant, so that the solving process of the adjustable power domain of the virtual power plant is simplified.
Optionally, the homogenization treatment is performed on different units in the output model, including:
reliability indexes of the power shortage time probability, the power shortage time expectation and the power shortage expectation value are selected, and the reliability of the wind power plant and the photovoltaic power station is evaluated from the power failure probability, the power failure time and the power failure power quantity respectively;
the expected value of insufficient electric quantity indicates the number of power failure times, average duration and average stop power, and the probability expressions of insufficient electric time of the single wind power output unit and the single photovoltaic output unit are
Figure 528522DEST_PATH_IMAGE137
In which
Figure 882143DEST_PATH_IMAGE138
The probability of the power shortage time is obtained,
Figure 319815DEST_PATH_IMAGE139
the probability of outage occurring while in system state l,
Figure 253136DEST_PATH_IMAGE140
the time length of shutdown when the system is in a system state l;
the expected expression of insufficient power time of a single wind power output unit and a single photovoltaic output unit is
Figure 718753DEST_PATH_IMAGE142
Wherein
Figure 446537DEST_PATH_IMAGE143
For the expectation of the power shortage time,
Figure 872970DEST_PATH_IMAGE144
the probability that the outage capacity of the flight group is greater than or equal to the spare capacity at the z-th day of the e-th time period,
Figure 609982DEST_PATH_IMAGE145
for the installed capacity of the system for the e-th time slot,
Figure 930105DEST_PATH_IMAGE146
the peak load at day z for the e-th session,
Figure 828791DEST_PATH_IMAGE147
is the number of time segments in a year,
Figure 742520DEST_PATH_IMAGE148
the index can judge the probability that the outage capacity of the power system unit is greater than or equal to the spare capacity in the number of days in the z-th time period;
the expression of the expected value of insufficient electric quantity of a single wind power output unit and a single photovoltaic output unit is
Figure 17644DEST_PATH_IMAGE150
Wherein
Figure 192273DEST_PATH_IMAGE151
In order to have the expected value of the power shortage,
Figure 527440DEST_PATH_IMAGE152
is as follows
Figure 662886DEST_PATH_IMAGE154
The outage capacity of the hour unit is more than or equal to
Figure 741700DEST_PATH_IMAGE155
The probability of (a) of (b) being,
Figure 770836DEST_PATH_IMAGE156
is as follows
Figure 276904DEST_PATH_IMAGE157
The installed capacity in the system is one hour,
Figure 135532DEST_PATH_IMAGE158
is as follows
Figure 814775DEST_PATH_IMAGE159
The load of the hour, T is the number of simulated hours, and the index is used for reflecting the expected value of reducing power supply for users when the power system unit is forced to stop.
In the embodiment, the reliability evaluation of the wind power plant and the photovoltaic power station is to calculate the time sequence state distribution of the single wind power output unit and the single photovoltaic output unitOn the basis, the time sequence state distribution of all wind power output units and all photovoltaic output units in the station is accumulated to obtain the time sequence state distribution of a single wind power plant and a single photovoltaic power station; calculating the reliability indexes of the single wind power plant and the single photovoltaic power station according to the time sequence state distribution, wherein the expression is
Figure 901680DEST_PATH_IMAGE160
，
Figure 516332DEST_PATH_IMAGE162
Wherein
Figure 688687DEST_PATH_IMAGE163
In order to be a function of the low battery expectation,
Figure 171621DEST_PATH_IMAGE164
for the system state at the qth time point in the Y simulation,
Figure 785136DEST_PATH_IMAGE165
for the system to be in a state
Figure 633006DEST_PATH_IMAGE166
The duration of the time period of the first,
Figure 354975DEST_PATH_IMAGE167
is the number of the states of the system,
Figure 313704DEST_PATH_IMAGE168
in order to simulate the number of calculations,
Figure 312884DEST_PATH_IMAGE169
and calculating the expected value of the insufficient electric quantity of the wind power plant or the photovoltaic power plant for the Yth time.
It should be noted that, because the wind power and photovoltaic output characteristics have large differences and belong to different types of units, the reliability index of the determined conventional unit cannot be directly used for the reliability assessment of the VPP, and the index of the confidence capacity not only can enable a wind power plant and a photovoltaic power station to be equivalent to a conventional power plant of the same type, but also reflects the capability of different wind power and photovoltaic power stations to be compared with the conventional power plant.
Referring to FIG. 2, a VPP reliability assessment model of confidence capacity is established, comprising:
s10: the method comprises the steps of using the capacity of a wind power plant or a photovoltaic power station instead of a conventional unit to evaluate the confidence capacity of the power plant or the power station, and obtaining the reliability indexes of the wind power plant and the photovoltaic power station by adopting sequential Monte Carlo calculation
Figure 66076DEST_PATH_IMAGE170
；
S11: photovoltaic power station installed capacity according to wind power occasion
Figure 275340DEST_PATH_IMAGE171
Obtaining corresponding reliability indexes, and drawing to obtain the reliability indexes of the wind power station and the photovoltaic power station
Figure 37760DEST_PATH_IMAGE172
A curve;
s12: the wind power plant is adopted to replace a conventional unit according to the installed capacity of the conventional unit
Figure 389982DEST_PATH_IMAGE173
Obtaining corresponding reliability indexes by biological difference, and drawing wind power plant to replace conventional unit
Figure 579655DEST_PATH_IMAGE174
With curved and photovoltaic power stations replacing conventional units
Figure 10636DEST_PATH_IMAGE175
A curve;
s14: when the wind farm capacity is
Figure 983271DEST_PATH_IMAGE176
At first, firstly
Figure 19360DEST_PATH_IMAGE177
Finding out the capacity of wind power plant on the curve
Figure 442251DEST_PATH_IMAGE178
Corresponding reliability index
Figure 298212DEST_PATH_IMAGE179
Then according to the value
Figure 340117DEST_PATH_IMAGE180
Finding the corresponding capacity on the curve
Figure 230713DEST_PATH_IMAGE181
The method comprises
Figure 824505DEST_PATH_IMAGE182
The value is the confidence capacity of the wind power plant, and the confidence capacity of the photovoltaic power station is correspondingly obtained.
In the embodiment, the confidence capacity calculation formula of the wind power plant and the photovoltaic power station is
Figure 839866DEST_PATH_IMAGE183
In which
Figure 482200DEST_PATH_IMAGE184
In order to be a function of the low battery expectation,
Figure 289619DEST_PATH_IMAGE185
is a power system load; the total confidence capacity calculation expression of all wind power plants and photovoltaic power stations is
Figure 991995DEST_PATH_IMAGE186
Wherein
Figure 984398DEST_PATH_IMAGE187
The total confidence capacity of all wind power plants and photovoltaic power stations, M is the number of all wind power plants and photovoltaic power stations,
Figure 492740DEST_PATH_IMAGE188
the confidence capacity of the u wind power plant or photovoltaic power plant; the reliability index of VPP is calculated by the following formula
Figure 92349DEST_PATH_IMAGE189
The expression of the total confidence capacity of all wind power plants and photovoltaic power stations can be combined
Figure 903310DEST_PATH_IMAGE190
The reliability of a VPP constructed from different types of energy sources can be evaluated by this method.
Optionally, analyzing and correcting the output model of the wind power and the photovoltaic in the distributed energy source includes:
the virtual power plant predicts the output of the next-day distributed renewable energy according to historical data statistics and prediction information, and a wind speed probability density function based on parameter Weibull distribution is
Figure 17896DEST_PATH_IMAGE191
Wherein v is the wind speed value, k and c are the shape parameter and the proportion parameter respectively, and satisfy
Figure 267612DEST_PATH_IMAGE192
；
The Beta distribution-based illumination intensity probability density function is
Figure 659410DEST_PATH_IMAGE193
Where w is the intensity of the illumination, and the subscript max indicates its maximum value,
Figure 703590DEST_PATH_IMAGE194
respectively the shape parameters of the Beta distribution,
Figure 571051DEST_PATH_IMAGE195
is a gamma function.
In the embodiment, the uncertainty of the wind power generator set and the photovoltaic generator set is related to factors such as the position, the altitude, the season and the like, and belongs to uncontrollable variables, but the virtual power plant predicts the output of the next-day distributed renewable energy according to long-term historical data statistics and prediction information, comprehensively considers the coordination and optimization configuration of various flexible loads, and then the partial load power in the peak period of power supply is transferred, translated and reduced in space and time, the smoothness of a load curve is obviously improved, the peak power supply pressure is relieved, and the stability of system operation is also improved.
Referring to fig. 3, the present invention also provides an energy regulation system of a virtual power plant, including:
the acquisition module is used for acquiring energy information in a virtual power plant VPP, and analyzing and correcting a wind power and photovoltaic output model in distributed energy, wherein the energy information comprises distributed energy and flexible load energy;
the first construction module is used for carrying out homogenization treatment on different units in the output model and establishing a VPP reliability evaluation model of confidence capacity, wherein the VPP reliability evaluation model considering the confidence capacity is constructed for reliability evaluation of a single wind power plant and a single photovoltaic power station, and the output and load power of the units are kept unchanged in unit hour;
the second construction module is used for constructing a flexible load energy model and carrying out uncertainty processing on the flexible load to obtain an adjustable power domain of the VPP;
and the regulation and control module is used for regulating and controlling the energy of the virtual power plant based on the VPP reliability evaluation model and the adjustable power domain.
In the embodiment, the load type comprises four load types, namely a basic load, a transferable load, a translatable load and a reducible load according to different operation characteristics of the load at the user side, wherein the basic load does not participate in demand response, and the system cannot adjust or change the energy using mode of the system, so that the load is the load with the largest proportion of users, and the necessary requirements of basic life and social development of people are met. The transferable load is that the power consumption of each time section can be flexibly adjusted according to the change of the scheduling polarization, and the total load quantity before and after the transfer is kept unchanged. The translatable load is a load which is translated continuously in a fixed working time length according to a scheduling plan in a multi-period manner on a time axis. The load which can be reduced is to partially or totally reduce the load which can bear certain interruption or power reduction operation according to the supply and demand conditions. The flexible load participates in the energy regulation and control operation of the virtual power plant, so that the flexible regulation capability of the system can be greatly improved, and the flexible load is an important flexible resource capable of being regulated and controlled on a user side.
It should be noted that, a wind farm and a photovoltaic power station in a certain area are aggregated according to a specified principle by taking a certain quarter as a cycle to participate in the scheduling of the power system, the minimum expected value of the power shortage is taken as an optimization target of a dynamic aggregation model of the wind farm and the photovoltaic power station, and the dynamic aggregation of the wind farm and the photovoltaic power station can reduce the influence of the seasonality and uncertainty of the output of the wind farm and the photovoltaic power station on the power supply capacity. Respectively calculating the reliability indexes of each wind power plant and each photovoltaic power station in the VPP, and accumulating the reliability indexes of all the stations to serve as the reliability indexes of the VPP; on the basis of calculating the reliability indexes of each wind power plant and each photovoltaic power station, the confidence capacities of the wind power plants and the photovoltaic power stations are solved and summed, and the reliability index of a conventional unit corresponding to the total confidence capacity of all the wind power plants and the photovoltaic power stations is calculated to serve as the VPP reliability index. The reliability evaluation of the confidence capacity provides a powerful reference for the evaluation of the VPP reliability, and provides correct guidance for a power grid, so that the resource utilization rate is improved.
In all examples shown and described herein, any particular value should be construed as merely exemplary, and not as a limitation, and thus other examples of example embodiments may have different values.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
The above examples are merely illustrative of several embodiments of the present invention, and the description thereof is more specific and detailed, but not to be construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention.

Claims (10)

1. An energy regulation and control method of a virtual power plant is characterized by comprising the following steps:
acquiring energy information in a VPP of a virtual power plant, and analyzing and correcting a wind power and photovoltaic output model in distributed energy, wherein the energy information comprises distributed energy and flexible load energy;
homogenizing different units in the output model, and establishing a VPP reliability evaluation model of confidence capacity, wherein the VPP reliability evaluation model considering the confidence capacity is established for reliability evaluation of a single wind power plant and a single photovoltaic power station, and the output and load power of the units are kept unchanged within a unit hour;
constructing a flexible load energy model and carrying out uncertainty processing on the flexible load to obtain an adjustable power domain of the VPP;
and regulating and controlling the energy of the virtual power plant based on the VPP reliability evaluation model and the adjustable power domain.
2. The method for energy regulation and control of a virtual power plant according to claim 1, wherein constructing a flexible load energy model and performing uncertainty processing on the flexible load to obtain an adjustable power domain of the VPP comprises:
the power inequality constraints of the power generation side unit in the virtual power plant comprise power constraints, climbing constraints and capacity constraints, and the expressions are respectively:
Figure 246297DEST_PATH_IMAGE001
wherein
Figure 172665DEST_PATH_IMAGE002
For the lower power limit of the distributed energy at the moment t,
Figure 393562DEST_PATH_IMAGE003
for the upper power limit of the distributed energy source at the moment t,
Figure 747183DEST_PATH_IMAGE004
the actual power of the distributed energy at the moment T is obtained, and T is the regulation and control time period of the distributed energy;
the expression of the climbing constraint is
Figure 686320DEST_PATH_IMAGE005
Wherein
Figure 619641DEST_PATH_IMAGE006
For the lower limit of the distributed energy t time climbing,
Figure 85257DEST_PATH_IMAGE007
the upper limit of the distributed energy climbing at the moment t is; the capacity constraint is expressed as
Figure DEST_PATH_IMAGE008
Wherein
Figure 983681DEST_PATH_IMAGE009
The energy stored for the distributed energy source at time t,
Figure 472431DEST_PATH_IMAGE010
for the rate of energy dissipation of the distributed energy source,
Figure 6181DEST_PATH_IMAGE011
for the charging power at the distributed energy source time t,
Figure 263987DEST_PATH_IMAGE012
in order to increase the charging efficiency of the distributed energy,
Figure 365935DEST_PATH_IMAGE013
for the discharge power at the moment t of the distributed energy source,
Figure 341981DEST_PATH_IMAGE014
for the efficiency of the discharge of the distributed energy source,
Figure 413842DEST_PATH_IMAGE015
for a lower limit of the amount of storable energy at time t for a distributed energy source,
Figure 791734DEST_PATH_IMAGE016
an upper limit value of the storable energy for the distributed energy at the moment t;
the process of the adjustable power domain aggregation algorithm of the virtual power plant comprises the following steps:
determining respective adjustable power domains based on power constraints of the distributed energy sources, expressed as
Figure 799004DEST_PATH_IMAGE017
Wherein
Figure 262347DEST_PATH_IMAGE018
The adjustable power domain that satisfies the power constraint for distributed energy j,
Figure 137899DEST_PATH_IMAGE018
for regulating power by distributed energy j at each moment in the scheduling period T
Figure 370297DEST_PATH_IMAGE019
A constructed column vector element;
aggregating the adjustable power domains of the distributed energy sources to obtain the adjustable power domains of the virtual power plants, wherein the expression is
Figure 814048DEST_PATH_IMAGE020
Wherein
Figure DEST_PATH_IMAGE021
To satisfy the adjustable power domain of all distributed energy power constraints for a virtual power plant,
Figure 499107DEST_PATH_IMAGE021
for regulating power by virtual power plants at various times during the scheduling period T
Figure 912771DEST_PATH_IMAGE022
The formed column vector elements, J is the quantity of distributed energy sources in the virtual power plant;
and removing all distributed energy source adjusting power variables in the adjustable power domain of the virtual power plant, and reserving the adjusting power variables of the virtual power plant to obtain an adjustable power domain aggregation model of the virtual power plant.
3. The method of claim 2, wherein when the power inequality constraint of the distributed energy resources includes a discrete variable, aggregating the adjustable power domains of the distributed energy resources including the discrete variable in the power inequality constraint with the same type and parameter comprises:
carrying out transformation processing on the representation forms of the distributed energy source adjustable power domains to enable the representation forms of the various distributed energy source adjustable power domains to have the same structure and different parameters;
and combining power constraints of all distributed energy sources, mapping the adjustable power domain of the virtual power plant to a geometric space to be a high-dimensional convex polyhedron, adopting the selected high-dimensional convex polyhedron to approximately solve the high-dimensional convex polyhedron from inside or outside, and using the convex polyhedron obtained by the approximate approximation solution to represent the adjustable power domain of the virtual power plant.
4. The method of claim 1, wherein the homogenizing different units in the output model comprises:
reliability indexes of the power shortage time probability, the power shortage time expectation and the power shortage expectation value are selected, and the reliability of the wind power plant and the photovoltaic power station is evaluated from the power failure probability, the power failure time and the power failure power quantity respectively;
the expected value of insufficient electric quantity indicates the number of power failure times, average duration and average stop power, and the probability expressions of insufficient electric time of the single wind power output unit and the single photovoltaic output unit are
Figure 438823DEST_PATH_IMAGE023
Wherein
Figure 115792DEST_PATH_IMAGE024
Is electricityThe probability of the force being insufficient for the time,
Figure 350465DEST_PATH_IMAGE025
to be in a system state
Figure 505502DEST_PATH_IMAGE026
The probability of a stoppage occurring at the time,
Figure 650176DEST_PATH_IMAGE027
to be in a system state
Figure 294784DEST_PATH_IMAGE028
The length of time that the outage occurred;
the expected expression of insufficient power time of a single wind power output unit and a single photovoltaic output unit is
Figure 954435DEST_PATH_IMAGE029
Wherein
Figure 850847DEST_PATH_IMAGE030
For the expectation of the power shortage time,
Figure 646765DEST_PATH_IMAGE031
the probability that the outage capacity of the flight group is greater than or equal to the spare capacity at the z-th day of the e-th time period,
Figure 727853DEST_PATH_IMAGE032
for the installed capacity of the system for the e-th time slot,
Figure 874801DEST_PATH_IMAGE033
the peak load at day z for the e-th session,
Figure 574904DEST_PATH_IMAGE034
is the number of time segments in a year,
Figure 490907DEST_PATH_IMAGE035
the index can judge the probability that the outage capacity of the power system unit is greater than or equal to the spare capacity in the number of days in the z-th time period;
the expressions of the expected values of the electric quantity insufficiency of the single wind power output unit and the single photovoltaic output unit are
Figure 913536DEST_PATH_IMAGE036
In which
Figure 547780DEST_PATH_IMAGE037
In order to have the expected value of the power shortage,
Figure DEST_PATH_IMAGE038
is as follows
Figure 520415DEST_PATH_IMAGE039
The outage capacity of the hour unit is more than or equal to
Figure 618821DEST_PATH_IMAGE040
The probability of (a) of (b) being,
Figure 713816DEST_PATH_IMAGE041
is as follows
Figure DEST_PATH_IMAGE042
The installed capacity in the system is measured in hours,
Figure 304197DEST_PATH_IMAGE043
is as follows
Figure DEST_PATH_IMAGE044
The load of the hour, T is the number of simulated hours, and the index is used for reflecting the expected value of reducing power supply for users when the power system unit is forced to stop.
5. The method for energy regulation and control of a virtual power plant according to claim 4, characterized in that the reliability evaluation of the wind farm and the photovoltaic power station is performed by accumulating the time sequence state distributions of all the wind power output units and the photovoltaic output units in the plant station on the basis of calculating the time sequence state distribution of a single wind power output unit and a single photovoltaic output unit to obtain the time sequence state distribution of the single wind farm and the single photovoltaic power station;
calculating the reliability indexes of the single wind power plant and the single photovoltaic power station according to the time sequence state distribution, wherein the expression is
Figure 80523DEST_PATH_IMAGE045
，
Figure 705540DEST_PATH_IMAGE046
Wherein
Figure 299332DEST_PATH_IMAGE047
In order to be a function of the low battery expectation,
Figure 642589DEST_PATH_IMAGE048
for the system state at the qth time point in the Y simulation,
Figure 735789DEST_PATH_IMAGE049
for the system to be in a state
Figure 543208DEST_PATH_IMAGE050
The duration of the time period of the first,
Figure 245585DEST_PATH_IMAGE051
is the number of the states of the system,
Figure 748242DEST_PATH_IMAGE052
in order to simulate the number of calculations,
Figure 256583DEST_PATH_IMAGE053
and calculating the expected value of the insufficient electric quantity of the wind power plant or the photovoltaic power plant for the Yth time.
6. The method of claim 1, wherein establishing a confidence capacity VPP reliability assessment model comprises:
the method comprises the steps of using the capacity of a wind power plant or a photovoltaic power station instead of a conventional unit to evaluate the confidence capacity of the power plant or the power station, and obtaining the reliability indexes of the wind power plant and the photovoltaic power station by adopting sequential Monte Carlo calculation
Figure 856192DEST_PATH_IMAGE054
；
Photovoltaic power station installed capacity according to wind power occasion
Figure 667153DEST_PATH_IMAGE055
Obtaining corresponding reliability indexes and drawing to obtain the wind power station and the photovoltaic power station
Figure 985002DEST_PATH_IMAGE056
A curve;
the wind power plant is adopted to replace a conventional unit according to the installed capacity of the conventional unit
Figure 31456DEST_PATH_IMAGE057
Obtaining corresponding reliability indexes by biological difference, drawing wind power plant to replace conventional set
Figure 423254DEST_PATH_IMAGE058
With curved and photovoltaic power stations replacing conventional units
Figure DEST_PATH_IMAGE059
A curve;
when the capacity of the wind farm is
Figure 264171DEST_PATH_IMAGE060
At first, firstly
Figure 69316DEST_PATH_IMAGE061
Finding out the capacity of wind power plant on the curve
Figure 293361DEST_PATH_IMAGE062
Corresponding reliability index
Figure 867562DEST_PATH_IMAGE063
Then according to the value at
Figure 144960DEST_PATH_IMAGE064
Finding the corresponding capacity on the curve
Figure 171822DEST_PATH_IMAGE065
The product is
Figure 435444DEST_PATH_IMAGE066
The value is the confidence capacity of the wind power plant, and the confidence capacity of the photovoltaic power station is correspondingly obtained.
7. The method for regulating and controlling energy of a virtual power plant according to claim 6, wherein the confidence capacity calculation formula of the wind power plant and the photovoltaic power plant is
Figure 926468DEST_PATH_IMAGE067
In which
Figure 312450DEST_PATH_IMAGE068
In order to be a function of the low battery expectation,
Figure 29870DEST_PATH_IMAGE069
is a power system load;
the total confidence capacity calculation expression of all wind power plants and photovoltaic power stations is
Figure 159500DEST_PATH_IMAGE070
Wherein
Figure 505031DEST_PATH_IMAGE071
For the total confidence capacity of all wind farms and photovoltaic power stations, M is the total confidence capacity of all wind farmsThe number of photovoltaic power stations,
Figure 999597DEST_PATH_IMAGE072
the confidence capacity of the u wind power plant or photovoltaic power plant;
the reliability index of VPP is calculated by the following formula
Figure 1051DEST_PATH_IMAGE073
The expression of the total confidence capacity of all wind power plants and photovoltaic power stations is combined to obtain
Figure 996689DEST_PATH_IMAGE074
The reliability of a VPP constructed from different types of energy sources can be evaluated by this method.
8. The method for energy regulation and control of a virtual power plant according to claim 1, wherein analyzing and modifying the output model of wind power and photovoltaic in the distributed energy comprises:
the virtual power plant predicts the output of the next-day distributed renewable energy according to historical data statistics and prediction information, and a wind speed probability density function based on parameter Weibull distribution is
Figure 134410DEST_PATH_IMAGE075
Wherein v is the wind speed value, k and c are the shape parameter and the proportion parameter respectively, and satisfy
Figure 566921DEST_PATH_IMAGE076
；
The Beta distribution-based illumination intensity probability density function is
Figure 852409DEST_PATH_IMAGE077
Where w is the illumination intensity and the subscript max indicates its maximum value,
Figure 323842DEST_PATH_IMAGE078
respectively the shape parameters of the Beta distribution,
Figure 519331DEST_PATH_IMAGE079
is a gamma function.
9. The method of claim 1, wherein the obtaining of the energy information in the VPP of the virtual power plant comprises:
the method comprises the steps of respectively modeling various flexible loads and energy equipment to obtain various energy sources to carry out coordinated optimization scheduling, wherein the flexible loads comprise translatable loads, translatable loads and reducible loads, and the energy equipment comprises a wind generating set, a photovoltaic generating set, a combined heat and power generation set and energy storage equipment.
10. An energy regulation system of a virtual power plant according to the energy regulation method of the virtual power plant of any one of claims 1 to 9, comprising:
the acquisition module is used for acquiring energy information in a virtual power plant VPP, and analyzing and correcting a wind power and photovoltaic output model in distributed energy, wherein the energy information comprises distributed energy and flexible load energy;
the first construction module is used for carrying out homogenization treatment on different units in the output model and establishing a VPP reliability evaluation model of confidence capacity, wherein the VPP reliability evaluation model considering the confidence capacity is constructed for reliability evaluation of a single wind power plant and a single photovoltaic power station, and the output and load power of the units are kept unchanged in unit hour;
the second construction module is used for constructing a flexible load energy model and carrying out uncertainty processing on the flexible load to obtain an adjustable power domain of the VPP;
and the regulation and control module is used for regulating and controlling the energy of the virtual power plant based on the VPP reliability assessment model and the adjustable power domain.
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Cited By (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115149586A (en) * 2022-08-01 2022-10-04 华北电力大学 Distributed energy aggregation regulation and autonomous regulation and control cooperative optimization method and system
CN115237080A (en) * 2022-09-19 2022-10-25 国网信息通信产业集团有限公司 Equipment regulation and control method, device, equipment and readable medium based on virtual power plant
CN115293595A (en) * 2022-08-10 2022-11-04 国网山东省电力公司青岛供电公司 Virtual power plant polymerization capacity assessment method considering photovoltaic output uncertainty
CN116542439A (en) * 2023-03-29 2023-08-04 国网上海市电力公司 Optimal operation method and system for multi-energy response of virtual power plant
CN116976601A (en) * 2023-07-19 2023-10-31 深圳市科中云技术有限公司 Virtual power plant flexible adjustable resource optimal scheduling method and system
CN117096948A (en) * 2023-08-21 2023-11-21 湖北清江水电开发有限责任公司 Virtual power plant scheduling method, equipment and storage medium based on wind power and hydropower
WO2024060413A1 (en) * 2022-09-20 2024-03-28 国网上海能源互联网研究院有限公司 Method and apparatus for constructing adjustable capacity of virtual power plant, electronic device, storage medium, program, and program product
CN117791627A (en) * 2024-02-26 2024-03-29 国网山东省电力公司东营供电公司 Flexible load dynamic aggregation method and system considering uncertainty of virtual power plant
CN118523312A (en) * 2024-07-18 2024-08-20 国网山东省电力公司烟台供电公司 Active distributed resource optimization scheduling method based on virtual power plant

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111262242A (en) * 2020-03-03 2020-06-09 上海电力大学 Multi-scene technology-based cooling, heating and power virtual power plant operation method
CN112529256A (en) * 2020-11-24 2021-03-19 华中科技大学 Distributed power supply cluster day-ahead scheduling method and system considering multiple uncertainties
CN112785027A (en) * 2020-06-22 2021-05-11 国网江苏省电力有限公司经济技术研究院 Wind-solar-storage combined power generation system confidence capacity evaluation method and system
EP3822881A1 (en) * 2019-11-14 2021-05-19 Google LLC Compute load shaping using virtual capacity and preferential location real time scheduling
CN112836849A (en) * 2020-12-21 2021-05-25 北京华能新锐控制技术有限公司 Virtual power plant scheduling method considering wind power uncertainty
CN113489066A (en) * 2021-07-08 2021-10-08 华翔翔能科技股份有限公司 Power supply reliability assessment method for energy storage-containing power grid interval considering supply and demand uncertainty
CN113919717A (en) * 2021-10-18 2022-01-11 内蒙古电力（集团）有限责任公司内蒙古电力经济技术研究院分公司 Multi-objective synchronous optimization oriented virtual power plant resource scheduling method and device

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP3822881A1 (en) * 2019-11-14 2021-05-19 Google LLC Compute load shaping using virtual capacity and preferential location real time scheduling
CN111262242A (en) * 2020-03-03 2020-06-09 上海电力大学 Multi-scene technology-based cooling, heating and power virtual power plant operation method
CN112785027A (en) * 2020-06-22 2021-05-11 国网江苏省电力有限公司经济技术研究院 Wind-solar-storage combined power generation system confidence capacity evaluation method and system
CN112529256A (en) * 2020-11-24 2021-03-19 华中科技大学 Distributed power supply cluster day-ahead scheduling method and system considering multiple uncertainties
CN112836849A (en) * 2020-12-21 2021-05-25 北京华能新锐控制技术有限公司 Virtual power plant scheduling method considering wind power uncertainty
CN113489066A (en) * 2021-07-08 2021-10-08 华翔翔能科技股份有限公司 Power supply reliability assessment method for energy storage-containing power grid interval considering supply and demand uncertainty
CN113919717A (en) * 2021-10-18 2022-01-11 内蒙古电力（集团）有限责任公司内蒙古电力经济技术研究院分公司 Multi-objective synchronous optimization oriented virtual power plant resource scheduling method and device

Cited By (14)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115149586A (en) * 2022-08-01 2022-10-04 华北电力大学 Distributed energy aggregation regulation and autonomous regulation and control cooperative optimization method and system
CN115293595A (en) * 2022-08-10 2022-11-04 国网山东省电力公司青岛供电公司 Virtual power plant polymerization capacity assessment method considering photovoltaic output uncertainty
CN115293595B (en) * 2022-08-10 2024-07-02 国网山东省电力公司青岛供电公司 Virtual power plant aggregation capability assessment method considering uncertainty of photovoltaic output
CN115237080A (en) * 2022-09-19 2022-10-25 国网信息通信产业集团有限公司 Equipment regulation and control method, device, equipment and readable medium based on virtual power plant
CN115237080B (en) * 2022-09-19 2022-12-09 国网信息通信产业集团有限公司 Virtual power plant based equipment regulation and control method, device, equipment and readable medium
WO2024060413A1 (en) * 2022-09-20 2024-03-28 国网上海能源互联网研究院有限公司 Method and apparatus for constructing adjustable capacity of virtual power plant, electronic device, storage medium, program, and program product
CN116542439A (en) * 2023-03-29 2023-08-04 国网上海市电力公司 Optimal operation method and system for multi-energy response of virtual power plant
CN116976601B (en) * 2023-07-19 2024-04-26 深圳市科中云技术有限公司 Virtual power plant flexible adjustable resource optimal scheduling method and system
CN116976601A (en) * 2023-07-19 2023-10-31 深圳市科中云技术有限公司 Virtual power plant flexible adjustable resource optimal scheduling method and system
CN117096948A (en) * 2023-08-21 2023-11-21 湖北清江水电开发有限责任公司 Virtual power plant scheduling method, equipment and storage medium based on wind power and hydropower
CN117096948B (en) * 2023-08-21 2024-05-03 湖北清江水电开发有限责任公司 Virtual power plant scheduling method, equipment and storage medium based on wind power and hydropower
CN117791627A (en) * 2024-02-26 2024-03-29 国网山东省电力公司东营供电公司 Flexible load dynamic aggregation method and system considering uncertainty of virtual power plant
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CN118523312A (en) * 2024-07-18 2024-08-20 国网山东省电力公司烟台供电公司 Active distributed resource optimization scheduling method based on virtual power plant

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