CN114744687B - 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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CN114744687B
CN114744687B CN202210661850.0A CN202210661850A CN114744687B CN 114744687 B CN114744687 B CN 114744687B CN 202210661850 A CN202210661850 A CN 202210661850A CN 114744687 B CN114744687 B CN 114744687B
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capacity
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CN114744687A (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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    • 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
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    • 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, which are used for acquiring energy information in VPP (virtual Power plant), analyzing and correcting output models of wind power and photovoltaic in distributed energy, homogenizing different units in the output models, establishing a VPP reliability evaluation model of confidence capacity, establishing a flexible load energy model, carrying out uncertainty processing on flexible load to obtain an adjustable power domain of VPP, and regulating and controlling the energy of the virtual power plant by the VPP reliability evaluation model and the adjustable power domain. The renewable energy output model is corrected, different units are subjected to homogenization treatment by combining 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 the seasonality and uncertainty of the output on the power supply capacity is reduced, the online electric quantity is also improved, the overall flexible regulation and control capacity of the VPP is improved, and the 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 energy regulation of virtual power plants, and particularly relates to an energy regulation and control method and system of a virtual power plant.
Background
At present, with the increase of installed capacity of a non-water renewable energy source unit represented by wind and light, a grid-connected mode is changed from local grid connection to multi-area centralized and distributed grid connection, so that the uncertainty of a power generation side and a load side is greatly increased, higher requirements are put on flexible resources of different time scales, and a power system is gradually changed into a renewable energy source 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 this, the invention provides an energy regulation and control method and system for a virtual power plant, which are implemented by using reliability assessment of a virtual power plant VPP 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 the VPP power supply reliability of the virtual power plant can be improved, the positivity of a power system for scheduling the virtual power plant VPP 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 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 virtual power plant VPP reliability assessment model of confidence capacity, wherein the virtual power plant VPP reliability assessment model considering the confidence capacity is established for the reliability assessment of a single wind power plant and a photovoltaic power station, and the output and the load power of the units are kept unchanged in 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 of the virtual power plant;
and regulating and controlling the energy of the virtual power plant based on the virtual power plant VPP reliability assessment 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 virtual power plant 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 326054DEST_PATH_IMAGE001
wherein
Figure 303237DEST_PATH_IMAGE002
For the lower power limit of the distributed energy source at the moment t,
Figure 637267DEST_PATH_IMAGE003
for the upper power limit of the distributed energy source at the moment t,
Figure 41703DEST_PATH_IMAGE004
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 687448DEST_PATH_IMAGE005
Wherein
Figure 671585DEST_PATH_IMAGE006
For the lower limit of the distributed energy t time climbing,
Figure 922437DEST_PATH_IMAGE007
the upper limit of the distributed energy climbing at the moment t is; the capacity constraint is expressed as
Figure 61557DEST_PATH_IMAGE008
Wherein
Figure 132281DEST_PATH_IMAGE009
The energy stored for the distributed energy source at time t,
Figure 388950DEST_PATH_IMAGE010
for the rate of energy dissipation of the distributed energy source,
Figure 228730DEST_PATH_IMAGE011
for the charging power at the distributed energy source time t,
Figure 302865DEST_PATH_IMAGE012
for the efficiency of the charging of the distributed energy source,
Figure 126465DEST_PATH_IMAGE013
for the discharge power at the moment t of the distributed energy source,
Figure 921246DEST_PATH_IMAGE014
for the efficiency of the discharge of the distributed energy source,
Figure 146691DEST_PATH_IMAGE015
for a lower limit of the amount of storable energy at time t for a distributed energy source,
Figure 126148DEST_PATH_IMAGE016
the upper limit value of the storable energy at the moment t for the distributed energy sources;
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 171464DEST_PATH_IMAGE017
Wherein
Figure 769936DEST_PATH_IMAGE018
The adjustable power domain that satisfies the power constraint for the distributed energy source j,
Figure 849887DEST_PATH_IMAGE019
for regulating power by distributed energy j at each moment in the scheduling period T
Figure 246DEST_PATH_IMAGE020
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 532858DEST_PATH_IMAGE021
Wherein
Figure 935021DEST_PATH_IMAGE022
To satisfy the adjustable power domain of all distributed energy power constraints for a virtual power plant,
Figure 603900DEST_PATH_IMAGE023
for regulating power by virtual power plants at various times during the scheduling period T
Figure 862843DEST_PATH_IMAGE024
The formed column vector elements, J is the quantity of distributed energy sources in the virtual power plant;
and removing all distributed energy source regulation power variables in the adjustable power domain of the virtual power plant, and keeping the virtual power plant regulation power variables 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 resource contains a discrete variable, aggregating the adjustable power domains of the distributed energy resource containing the discrete variable in the power inequality constraint with the same type and parameter includes:
the characterization forms of the distributed energy adjustable power domains are subjected to conversion processing, so that the characterization forms of the distributed energy adjustable power domains 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:
selecting reliability indexes of the power shortage time probability, the power shortage time expectation and the power shortage expectation, and evaluating the reliability of the wind power plant and the photovoltaic power station respectively according to the power failure probability, the power failure time and the power failure power;
the expected value of insufficient electric quantity represents the power failure times, the average duration and the average power failure rate, and a single wind power output unitThe power shortage time probability expression of the photovoltaic output unit is
Figure 443603DEST_PATH_IMAGE025
Wherein
Figure 711774DEST_PATH_IMAGE026
The probability of the power shortage time is obtained,
Figure 438421DEST_PATH_IMAGE027
to be in a system state
Figure 868266DEST_PATH_IMAGE028
The probability of a stoppage occurring at the time,
Figure 437787DEST_PATH_IMAGE029
to be in a system state
Figure 978490DEST_PATH_IMAGE030
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 621961DEST_PATH_IMAGE031
Wherein
Figure 425969DEST_PATH_IMAGE032
For the expectation of the time when the power is insufficient,
Figure 154890DEST_PATH_IMAGE033
the probability that the outage capacity of the flight set is greater than or equal to the spare capacity at the z th day of the e-th time period,
Figure 499284DEST_PATH_IMAGE034
for the installed capacity of the system for the e-th time slot,
Figure 59578DEST_PATH_IMAGE035
the peak load at day z for the e-th session,
Figure 96804DEST_PATH_IMAGE036
is the number of time segments in a year,
Figure 250705DEST_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 398790DEST_PATH_IMAGE038
In which
Figure 79170DEST_PATH_IMAGE039
In order to have the expected value of the power shortage,
Figure 287297DEST_PATH_IMAGE040
the probability that the unit outage capacity is larger than or equal to i in the ith hour,
Figure 928494DEST_PATH_IMAGE041
is the installed capacity in the ith hour system,
Figure 880270DEST_PATH_IMAGE042
the load for the ith hour is the load,
Figure DEST_PATH_IMAGE043
to simulate the number of hours, the indicator is used to reflect the expected value of forced outage of the power system unit to reduce power supply to the customer.
As a further improvement of the above technical solution, reliability evaluation of the wind power plant and the photovoltaic power station is to accumulate time sequence state distributions of all wind power output units and photovoltaic output units in the plant station on the basis of obtaining time sequence state distributions of a single wind power output unit and a single photovoltaic output unit through calculation, so as to obtain time sequence state distributions of the single wind power plant 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 182200DEST_PATH_IMAGE044
Wherein
Figure 498912DEST_PATH_IMAGE045
In order to be a function of the low battery expectation,
Figure 424143DEST_PATH_IMAGE046
for the system state at the qth time point in the Y-th simulation,
Figure 914030DEST_PATH_IMAGE047
is the system in a state
Figure 569002DEST_PATH_IMAGE048
The duration of the time period of the first,
Figure 853353DEST_PATH_IMAGE049
is the number of states of the system,
Figure 469142DEST_PATH_IMAGE050
the number of times was calculated for the simulation.
As a further improvement of the technical scheme, the establishment of the virtual power plant VPP reliability assessment 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 762720DEST_PATH_IMAGE051
；
According to installed capacity of wind power plant and photovoltaic power plant
Figure 944303DEST_PATH_IMAGE052
Obtaining corresponding reliability indexes and drawing to obtain the wind power station and the photovoltaic power station
Figure 727451DEST_PATH_IMAGE053
A curve;
the wind power plant is adopted to replace a conventional unit according to the installed capacity of the conventional unit
Figure 627274DEST_PATH_IMAGE054
Obtaining corresponding reliability indexes, and drawing a wind power plant to replace a conventional unit
Figure 396647DEST_PATH_IMAGE055
With curved and photovoltaic power stations replacing conventional units
Figure 698315DEST_PATH_IMAGE056
A curve;
when the wind farm capacity is
Figure 590048DEST_PATH_IMAGE057
At first, firstly
Figure 39484DEST_PATH_IMAGE058
Finding out the capacity of wind power plant on the curve
Figure 674865DEST_PATH_IMAGE059
Corresponding reliability index
Figure 34302DEST_PATH_IMAGE060
Then according to the value
Figure 96936DEST_PATH_IMAGE061
Finding the corresponding capacity on the curve
Figure 526343DEST_PATH_IMAGE062
The product is
Figure 699836DEST_PATH_IMAGE062
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 calculation formula of the confidence capacity of the wind power plant and the photovoltaic power station is as follows
Figure 179359DEST_PATH_IMAGE063
Wherein
Figure 475211DEST_PATH_IMAGE064
In order to be a function of the low battery expectation,
Figure 836922DEST_PATH_IMAGE065
is a power system load;
the total confidence capacity calculation expression of all wind power plants and photovoltaic power stations is
Figure 751788DEST_PATH_IMAGE066
In which
Figure 210452DEST_PATH_IMAGE067
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 614888DEST_PATH_IMAGE068
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 667158DEST_PATH_IMAGE069
The expression of the total confidence capacity of all wind power plants and photovoltaic power stations is combined to obtain
Figure 510349DEST_PATH_IMAGE070
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 698885DEST_PATH_IMAGE071
Which isV is the wind speed value, k and c are the shape parameter and the proportion parameter respectively, and satisfy
Figure 274222DEST_PATH_IMAGE072
；
The Beta distribution-based illumination intensity probability density function is
Figure 439887DEST_PATH_IMAGE073
Where w is the intensity of the illumination, and the subscript max indicates its maximum value,
Figure 696556DEST_PATH_IMAGE074
respectively the shape parameters of the Beta distribution,
Figure 801915DEST_PATH_IMAGE075
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 for carrying out coordinated optimization scheduling, wherein the flexible loads comprise translatable loads, transferable loads and reducible loads, and the energy equipment comprises a wind generating set, a photovoltaic generating set, a cogeneration unit 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 system comprises a first construction module, a second construction module and a third construction module, wherein the first construction module is used for carrying out homogenization treatment on different units in an output model and establishing a VPP reliability evaluation model of confidence capacity, the virtual power plant VPP reliability evaluation model considering the confidence capacity is constructed for the 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 of the virtual power plant;
and the regulation and control module is used for regulating and controlling the energy of the virtual power plant based on the virtual power plant 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 a wind power and photovoltaic output model in distributed energy is analyzed and corrected by acquiring energy information in a virtual power plant VPP, different units in the output model are subjected to homogenization treatment, a virtual power plant VPP reliability evaluation model with confidence capacity is established, a flexible load energy model is established, flexible load is subjected to uncertainty treatment to obtain an adjustable power domain of the virtual power plant VPP, and the energy of the virtual power plant is regulated and controlled based on the virtual power plant 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 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 reliability indexes as objective functions, the influence of the output seasonality and uncertainty 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 of the virtual power plant is further improved, and therefore 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 chart of a virtual power plant VPP reliability assessment model of confidence capacity 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 virtual power plant VPP reliability assessment model of confidence capacity, wherein the virtual power plant VPP reliability assessment model considering the confidence capacity is established for the reliability assessment of a single wind power plant and a photovoltaic power station, and the output and the load power of the units are kept unchanged in 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 of the virtual power plant;
s4: and regulating and controlling the energy of the virtual power plant based on the virtual power plant VPP reliability assessment 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 sale profit, although investment cost of new energy generating sets such as wind power generation and photovoltaic power generation is high, operation cost is low, the cost-benefit functions generally only consider the power sale profit, the cost-benefit functions of conventional controllable sets such as gas turbines and fuel oil sets are composed of the power sale profit, fuel cost and environment cost, the relation between adjusting power and 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 and hydrogen energy storage are composed of net benefits under charging and discharging power. The user side unit of the virtual power plant relates to temperature control load, electric vehicle and other residential loads, and the cost benefit function of the unit is a nonlinear function which needs comfort loss cost and power regulation income brought by demand response besides electricity purchasing cost.
It should be noted that, a plurality of flexible loads and energy devices are respectively modeled to obtain a plurality of energy sources for coordinated optimization scheduling, the flexible loads include translatable loads, translatable loads and reducible loads, and the energy devices include a wind generating set, a photovoltaic generating set, a cogeneration set and an energy storage device. 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: analyzing and correcting the output model models of wind power and photovoltaic, homogenizing different units, establishing a VPP reliability evaluation model of the Carlo confidence capacity, and then establishing a VPP dynamic polymerization model of the 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 876050DEST_PATH_IMAGE076
wherein
Figure 637333DEST_PATH_IMAGE077
For the lower power limit of the distributed energy at the moment t,
Figure 494431DEST_PATH_IMAGE078
for the upper power limit of the distributed energy source at the moment t,
Figure 516613DEST_PATH_IMAGE079
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 formula of the climbing constraint is
Figure 699333DEST_PATH_IMAGE080
In which
Figure 947912DEST_PATH_IMAGE081
For the lower limit of the distributed energy t time climbing,
Figure 671017DEST_PATH_IMAGE082
the upper limit of the distributed energy climbing at the moment t is; the capacity constraint is expressed as
Figure 954231DEST_PATH_IMAGE083
Wherein
Figure 42272DEST_PATH_IMAGE084
The energy stored for the distributed energy source at time t,
Figure 135737DEST_PATH_IMAGE085
for the rate of energy dissipation of the distributed energy source,
Figure 600216DEST_PATH_IMAGE086
for the charging power at the moment t of the distributed energy source,
Figure 472357DEST_PATH_IMAGE087
in order to increase the charging efficiency of the distributed energy,
Figure 59197DEST_PATH_IMAGE088
for the discharge power at the moment t of the distributed energy source,
Figure 79105DEST_PATH_IMAGE089
in order to achieve the efficiency of the discharge of the distributed energy source,
Figure 816117DEST_PATH_IMAGE090
for a distributed energy source to store a lower limit of energy at time t,
Figure 542765DEST_PATH_IMAGE091
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:
according to distributed energyDetermines respective adjustable power domains expressed as
Figure 238188DEST_PATH_IMAGE092
Wherein
Figure 807710DEST_PATH_IMAGE093
The adjustable power domain that satisfies the power constraint for the distributed energy source j,
Figure 348413DEST_PATH_IMAGE094
for regulating power by distributed energy j at each moment in the scheduling period T
Figure 991883DEST_PATH_IMAGE095
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 530312DEST_PATH_IMAGE096
Wherein
Figure 852709DEST_PATH_IMAGE097
To satisfy the adjustable power domain of all distributed energy power constraints for a virtual power plant,
Figure 931524DEST_PATH_IMAGE098
for regulating power by virtual power plants at various times during the scheduling period T
Figure 695080DEST_PATH_IMAGE099
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 669989DEST_PATH_IMAGE100
Wherein
Figure 886207DEST_PATH_IMAGE101
The power domain may be adjusted for the virtual power plant described by the virtual battery model,
Figure 598073DEST_PATH_IMAGE102
for regulating power by virtual power plants at various times during the scheduling period T
Figure 216137DEST_PATH_IMAGE103
The elements of the column vector of the construct,
Figure 361947DEST_PATH_IMAGE104
for the lower power limit of the virtual battery model,
Figure 65461DEST_PATH_IMAGE105
is the upper power limit of the virtual battery model,
Figure 813974DEST_PATH_IMAGE106
the stored power for the virtual battery model at time t,
Figure 755385DEST_PATH_IMAGE107
is the lower limit value of the electric energy of the virtual battery model,
Figure 134414DEST_PATH_IMAGE108
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 387541DEST_PATH_IMAGE109
In which
Figure 877428DEST_PATH_IMAGE110
The power domain may be adjusted for the virtual plant described by the virtual generator model,
Figure 142187DEST_PATH_IMAGE111
for regulating power by virtual power plants at various times during a scheduling period
Figure 426538DEST_PATH_IMAGE112
The elements of the column vector of the construct,
Figure 104644DEST_PATH_IMAGE113
to be the lower power limit of the virtual generator model,
Figure 460539DEST_PATH_IMAGE114
for the upper power limit of the virtual generator model,
Figure 907701DEST_PATH_IMAGE115
for the lower limit of the climbing of the virtual generator model,
Figure 300636DEST_PATH_IMAGE116
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 the inscribed square polyhedron with the longest side length in the 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, all the adjustable power domains of the distributed energy sources are solved by adopting the same initialization power set, the precision of a calculation result is reduced, and the consistency and the timeliness of reasonable regulation and control of the energy sources of a power generation side unit and a power utilization side unit are ensured in a certain layer degree.
Optionally, the homogenization processing is performed on different units in the output model, and 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 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 466038DEST_PATH_IMAGE117
Wherein
Figure 864439DEST_PATH_IMAGE118
The probability of the power shortage time is obtained,
Figure 431687DEST_PATH_IMAGE119
to be in a system state
Figure 792261DEST_PATH_IMAGE120
The probability of a stoppage occurring at the time,
Figure 382643DEST_PATH_IMAGE121
to be in a system state
Figure 18023DEST_PATH_IMAGE122
The length of time that the outage occurs;
the expected expression of insufficient power time of a single wind power output unit and a single photovoltaic output unit is
Figure 236515DEST_PATH_IMAGE123
Wherein
Figure 299149DEST_PATH_IMAGE124
For the expectation of the time when the power is insufficient,
Figure 111247DEST_PATH_IMAGE125
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 284739DEST_PATH_IMAGE126
for the installed capacity of the system for the e-th time slot,
Figure 561000DEST_PATH_IMAGE127
the peak load at day z for the e-th session,
Figure 856852DEST_PATH_IMAGE128
is the number of time segments in a year,
Figure 218563DEST_PATH_IMAGE129
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 133430DEST_PATH_IMAGE130
Wherein
Figure 264197DEST_PATH_IMAGE131
In order to have the expected value of the power shortage,
Figure 730950DEST_PATH_IMAGE132
the probability that the unit outage capacity is larger than or equal to i in the ith hour,
Figure 783220DEST_PATH_IMAGE133
for the installed capacity in the system at the ith hour,
Figure 298515DEST_PATH_IMAGE134
the load for the ith hour is the load,
Figure 113149DEST_PATH_IMAGE135
to simulate the number of hours, the indicator is used to reflect the expected value of a forced outage of the power system unit to reduce the power supply to the customer.
In the embodiment, the reliability evaluation of the wind power plant and the photovoltaic power station is to accumulate the time sequence state distribution of all the wind power output units and the photovoltaic output units in the station on the basis of obtaining the time sequence state distribution of a single wind power output unit and a single photovoltaic output unit through calculation to obtain the time sequence state distribution of the single wind power plant and the single photovoltaic power station; calculating the reliability index of a single wind power plant and a single photovoltaic power station according to the time sequence state distribution, wherein the expression formula is
Figure DEST_PATH_IMAGE136
，
Figure 891750DEST_PATH_IMAGE137
Wherein
Figure 228053DEST_PATH_IMAGE138
In order to be a function of the low battery expectation,
Figure 609356DEST_PATH_IMAGE139
for the system state at the qth time point in the Y simulation,
Figure 714715DEST_PATH_IMAGE140
for the system to be in a state
Figure 398637DEST_PATH_IMAGE141
The duration of the time period of the first,
Figure 956657DEST_PATH_IMAGE142
is the number of the states of the system,
Figure 141651DEST_PATH_IMAGE143
in order to simulate the number of times of calculation,
Figure 101517DEST_PATH_IMAGE144
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 956340DEST_PATH_IMAGE145
；
S11: according to installed capacity of wind power plant and photovoltaic power plant
Figure 267236DEST_PATH_IMAGE146
Obtaining corresponding reliability indexes, and drawing to obtain the reliability indexes of the wind power station and the photovoltaic power station
Figure 990341DEST_PATH_IMAGE147
A curve;
s12: the wind power plant is adopted to replace a conventional unit according to the installed capacity of the conventional unit
Figure 804714DEST_PATH_IMAGE148
Obtaining corresponding reliability indexes, and drawing the wind power plant to replace the conventional unit
Figure 96018DEST_PATH_IMAGE149
With curved and photovoltaic power stations replacing conventional units
Figure 628630DEST_PATH_IMAGE150
A curve;
s14: when the wind farm capacity is
Figure 653962DEST_PATH_IMAGE151
At first, firstly
Figure 322840DEST_PATH_IMAGE152
Finding out the capacity of wind power plant on the curve
Figure 785046DEST_PATH_IMAGE153
Corresponding reliability index
Figure 804954DEST_PATH_IMAGE154
Then according to the value
Figure 869862DEST_PATH_IMAGE155
Finding the corresponding capacity on the curve
Figure 393248DEST_PATH_IMAGE156
The product is
Figure 88671DEST_PATH_IMAGE156
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 267980DEST_PATH_IMAGE157
Wherein
Figure 74262DEST_PATH_IMAGE158
In order to be a function of the low battery expectation,
Figure 45629DEST_PATH_IMAGE159
is a power system load; the total confidence capacity calculation expression of all wind power plants and photovoltaic power stations is
Figure 646374DEST_PATH_IMAGE160
Wherein
Figure 578558DEST_PATH_IMAGE161
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 922952DEST_PATH_IMAGE162
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 748825DEST_PATH_IMAGE163
The expression of the total confidence capacity of all wind power plants and photovoltaic power stations is combined to obtain
Figure 520472DEST_PATH_IMAGE164
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 939952DEST_PATH_IMAGE165
Wherein v is the wind speed value, k and c are the shape parameter and the proportion parameter respectively, and satisfy
Figure 88037DEST_PATH_IMAGE166
；
The Beta distribution-based illumination intensity probability density function is
Figure 269882DEST_PATH_IMAGE167
Where w is the intensity of the illumination, and the subscript max indicates its maximum value,
Figure 478009DEST_PATH_IMAGE168
respectively the shape parameters of the Beta distribution,
Figure 119206DEST_PATH_IMAGE169
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 various changes and modifications can be made by those skilled in the art without departing from the spirit of the invention, and these changes and modifications are all within the scope of the invention.

Claims (9)

1. An energy regulation and control method of a virtual power plant is characterized by comprising the following steps:
acquiring energy information in 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 reliability evaluation model of a confidence capacity virtual power plant, wherein the reliability evaluation model of a single wind power plant and a single photovoltaic power station is established, the reliability evaluation model of the confidence capacity virtual power plant is considered, and the output and the load power of the units are kept unchanged in 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 virtual power plant;
regulating and controlling the energy of the virtual power plant based on the virtual power plant reliability evaluation model and the adjustable power domain;
the method comprises the following steps of constructing a flexible load energy model and carrying out uncertainty processing on the flexible load to obtain an adjustable power domain of a virtual power plant, wherein the method comprises the following steps of:
the power inequality constraints of the power generation side units in the virtual power plant comprise power constraints, climbing constraints and capacity constraints, and the expressions are as follows:
Figure 226632DEST_PATH_IMAGE001
wherein
Figure 386218DEST_PATH_IMAGE002
For the lower power limit of the distributed energy at the moment t,
Figure 891148DEST_PATH_IMAGE003
for the upper power limit of the distributed energy source at the moment t,
Figure 110777DEST_PATH_IMAGE004
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 763475DEST_PATH_IMAGE005
In which
Figure 70960DEST_PATH_IMAGE006
For the lower limit of the distributed energy t time climbing,
Figure 492714DEST_PATH_IMAGE007
the upper limit of the distributed energy climbing at the moment t is; the capacity constraint is expressed as
Figure 617665DEST_PATH_IMAGE008
Wherein
Figure 492080DEST_PATH_IMAGE009
The energy stored for the distributed energy source at time t,
Figure 868834DEST_PATH_IMAGE010
for the rate of energy dissipation of the distributed energy source,
Figure 145095DEST_PATH_IMAGE011
for the charging power at the distributed energy source time t,
Figure 942412DEST_PATH_IMAGE012
for the efficiency of the charging of the distributed energy source,
Figure 304123DEST_PATH_IMAGE013
for the discharge power at the moment t of the distributed energy source,
Figure 953410DEST_PATH_IMAGE014
for the efficiency of the discharge of the distributed energy source,
Figure 349757DEST_PATH_IMAGE015
for a lower limit of the amount of storable energy at time t for a distributed energy source,
Figure 816510DEST_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 399938DEST_PATH_IMAGE017
Wherein
Figure 118495DEST_PATH_IMAGE018
The adjustable power domain that satisfies the power constraint for the distributed energy source j,
Figure 103769DEST_PATH_IMAGE019
for regulating power by distributed energy j at each moment in the scheduling period T
Figure 679107DEST_PATH_IMAGE020
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 77727DEST_PATH_IMAGE021
In which
Figure 396713DEST_PATH_IMAGE022
To satisfy the adjustable power domain of all distributed energy power constraints for a virtual power plant,
Figure 439755DEST_PATH_IMAGE023
for regulating power by virtual power plants at various times during the scheduling period T
Figure 920415DEST_PATH_IMAGE024
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.
2. The method for energy regulation and control of a virtual power plant according to claim 1, wherein when the power inequality constraints of the distributed energy resources contain discrete variables, aggregating the adjustable power domains of the distributed energy resources containing discrete variables in the power inequality constraints with the same type and parameters 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.
3. The method according to claim 1, wherein the step of 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 478436DEST_PATH_IMAGE025
In which
Figure 663429DEST_PATH_IMAGE026
The probability of the power shortage time is obtained,
Figure 623295DEST_PATH_IMAGE027
to be in a system state
Figure 478119DEST_PATH_IMAGE028
The probability of the occurrence of outage in time,
Figure 789014DEST_PATH_IMAGE029
to be in a system state
Figure 184223DEST_PATH_IMAGE030
The length of time that the outage occurred;
the expected expressions of the insufficient power time of the single wind power output unit and the photovoltaic output unit are
Figure 655105DEST_PATH_IMAGE031
Wherein
Figure 946409DEST_PATH_IMAGE032
For the expectation of the time when the power is insufficient,
Figure 213442DEST_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 412343DEST_PATH_IMAGE034
for the installed capacity of the system for the e-th time slot,
Figure 409117DEST_PATH_IMAGE035
the peak load at day z for the e-th session,
Figure 933640DEST_PATH_IMAGE036
is the number of time segments in a year,
Figure 625652DEST_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 221719DEST_PATH_IMAGE038
Wherein
Figure 745104DEST_PATH_IMAGE039
In order to have the expected value of the power shortage,
Figure 174948DEST_PATH_IMAGE040
the probability that the outage capacity of the unit is larger than or equal to i at the ith hour,
Figure 885415DEST_PATH_IMAGE041
in the ith hour systemThe installed capacity of the air conditioner is reduced,
Figure 754014DEST_PATH_IMAGE042
the load for the ith hour is the load,
Figure 131906DEST_PATH_IMAGE043
to simulate the number of hours, the indicator is used to reflect the expected value of a forced outage of the power system unit to reduce the power supply to the customer.
4. The method for energy regulation and control of a virtual power plant according to claim 3, 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 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 998231DEST_PATH_IMAGE044
Wherein
Figure 664835DEST_PATH_IMAGE045
In order to be a function of the low battery expectation,
Figure 274808DEST_PATH_IMAGE046
for the system state at the qth time point in the Y simulation,
Figure 602147DEST_PATH_IMAGE047
for the system to be in a state
Figure 373793DEST_PATH_IMAGE048
The duration of the time period of the first,
Figure 793274DEST_PATH_IMAGE049
is the number of the states of the system,
Figure 941358DEST_PATH_IMAGE050
the number of times was calculated for the simulation.
5. The method of claim 1, wherein establishing a confidence capacity virtual plant 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 293842DEST_PATH_IMAGE051
；
According to installed capacity of wind power plant and photovoltaic power plant
Figure 298707DEST_PATH_IMAGE052
Obtaining corresponding reliability indexes, and drawing to obtain the reliability indexes of the wind power station and the photovoltaic power station
Figure 2221DEST_PATH_IMAGE053
A curve;
the wind power plant is adopted to replace a conventional unit according to the installed capacity of the conventional unit
Figure 891680DEST_PATH_IMAGE054
Obtaining corresponding reliability indexes, and drawing the wind power plant to replace the conventional unit
Figure 364249DEST_PATH_IMAGE055
With curved and photovoltaic power stations replacing conventional units
Figure 805595DEST_PATH_IMAGE056
A curve;
when the capacity of the wind farm is
Figure 996405DEST_PATH_IMAGE057
At first, firstly
Figure 220713DEST_PATH_IMAGE058
Finding out the capacity of wind power plant on the curve
Figure 751051DEST_PATH_IMAGE059
Corresponding reliability index
Figure 300981DEST_PATH_IMAGE060
Then according to the value
Figure 775825DEST_PATH_IMAGE061
Finding the corresponding capacity on the curve
Figure 69403DEST_PATH_IMAGE062
The product is
Figure 454248DEST_PATH_IMAGE063
The value is the confidence capacity of the wind power plant, and the confidence capacity of the photovoltaic power station is correspondingly obtained.
6. The method for controlling energy of a virtual power plant according to claim 5, wherein the confidence capacity calculation formula of the wind farm and the photovoltaic power plant is
Figure 909500DEST_PATH_IMAGE064
Wherein
Figure 74902DEST_PATH_IMAGE065
In order to be a function of the low battery expectation,
Figure 467444DEST_PATH_IMAGE066
is a power system load;
the total confidence capacity calculation expression of all wind power plants and photovoltaic power stations is
Figure 769112DEST_PATH_IMAGE067
Wherein
Figure 598528DEST_PATH_IMAGE068
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 985647DEST_PATH_IMAGE069
the confidence capacity of the u wind power plant or photovoltaic power plant;
the reliability index of the virtual power plant is calculated by the formula
Figure 683345DEST_PATH_IMAGE070
The expression of the total confidence capacity of all wind power plants and photovoltaic power stations is combined to obtain
Figure 839519DEST_PATH_IMAGE071
The reliability of a virtual power plant constructed by different types of energy sources can be evaluated by the method.
7. 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 902153DEST_PATH_IMAGE072
Wherein v is the wind speed value, k and c are the shape parameter and the proportion parameter respectively, and satisfy
Figure 714252DEST_PATH_IMAGE073
；
The Beta distribution-based illumination intensity probability density function is
Figure 887744DEST_PATH_IMAGE074
Where w is the intensity of the illumination, and the subscript max indicates its maximum value,
Figure 226321DEST_PATH_IMAGE075
respectively the shape parameters of the Beta distribution,
Figure 194277DEST_PATH_IMAGE076
is a gamma function.
8. The method of claim 1, wherein the obtaining of the energy information in the virtual power plant comprises:
the method comprises the steps of respectively modeling various flexible loads and energy equipment to obtain various energy sources for carrying out coordinated optimization scheduling, wherein the flexible loads comprise translatable loads, transferable loads and reducible loads, and the energy equipment comprises a wind generating set, a photovoltaic generating set, a cogeneration unit and energy storage equipment.
9. 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 8, comprising:
the system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring energy information in a virtual power plant, and analyzing and correcting output models of wind power and photovoltaic in distributed energy, and the energy information comprises distributed energy and flexible load energy;
the system comprises a first construction module, a second construction module and a third construction module, wherein the first construction module is used for carrying out homogenization treatment on different units in an output model and establishing a virtual power plant reliability evaluation model with confidence capacity, the virtual power plant reliability evaluation model considering the confidence capacity is constructed for the 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 virtual power plant;
and the regulation and control module is used for regulating and controlling the energy of the virtual power plant based on the virtual power plant reliability assessment model and the adjustable power domain.
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