CN112039080A - Garden energy differentiation scheduling method and system based on Monte Carlo simulation - Google Patents

Garden energy differentiation scheduling method and system based on Monte Carlo simulation Download PDF

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CN112039080A
CN112039080A CN202010659298.2A CN202010659298A CN112039080A CN 112039080 A CN112039080 A CN 112039080A CN 202010659298 A CN202010659298 A CN 202010659298A CN 112039080 A CN112039080 A CN 112039080A
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power
distribution network
voltage
photovoltaic
node
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胡宁
司君诚
刘航航
王元元
刘彧挥
刘琪
季兴龙
孙名妤
马晓祎
任敬刚
蔡言斌
谢芸
张秋瑞
苏小向
张丹
王燕
吕风磊
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State Grid Corp of China SGCC
Dongying Power Supply Co of State Grid Shandong Electric Power Co Ltd
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State Grid Corp of China SGCC
Dongying Power Supply Co of State Grid Shandong Electric Power 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
    • H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
    • B60L53/60—Monitoring or controlling charging stations
    • B60L53/63—Monitoring or controlling charging stations in response to network capacity
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • H02J3/322—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means the battery being on-board an electric or hybrid vehicle, e.g. vehicle to grid arrangements [V2G], power aggregation, use of the battery for network load balancing, coordinated or cooperative battery charging
    • 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
    • H02J2203/20—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
    • H02J2310/00—The network for supplying or distributing electric power characterised by its spatial reach or by the load
    • H02J2310/40—The network being an on-board power network, i.e. within a vehicle
    • H02J2310/48—The network being an on-board power network, i.e. within a vehicle for electric vehicles [EV] or hybrid vehicles [HEV]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/50—Photovoltaic [PV] energy
    • Y02E10/56—Power conversion systems, e.g. maximum power point trackers
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E70/00—Other energy conversion or management systems reducing GHG emissions
    • Y02E70/30—Systems combining energy storage with energy generation of non-fossil origin
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/70—Energy storage systems for electromobility, e.g. batteries
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/7072—Electromobility specific charging systems or methods for batteries, ultracapacitors, supercapacitors or double-layer capacitors
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02T90/10—Technologies relating to charging of electric vehicles
    • Y02T90/12—Electric charging stations

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  • Power Engineering (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Supply And Distribution Of Alternating Current (AREA)

Abstract

The invention discloses a park energy differentiated scheduling method and system based on Monte Carlo simulation, which comprises the steps of constructing a regulation objective function by taking the minimum photovoltaic station reactive power output cost, the electric automobile compensation cost and the node voltage qualification rate shortage punishment cost as targets according to electric power operation data of a photovoltaic power supply and an electric automobile node accessed in a power distribution network; constraint conditions are established according to tidal current results meeting the balance of active power and reactive power of the power distribution network, the uncertainty of photovoltaic and the uncertainty of electric vehicle charging are set at random through Monte Carlo simulation, a regulation objective function is solved, the optimal power generation power and the optimal charging and discharging power of the photovoltaic power supply and the electric vehicle after the photovoltaic power supply and the electric vehicle are connected to the power distribution network are obtained, and the voltage of the power distribution network access node is coordinated and controlled. The electric automobile and the photovoltaic power supply are matched to optimize and regulate the voltage of a power grid, and the regulation and control of the node voltage of the power distribution network are realized on the basis of the charging and discharging uncertainty of the electric automobile and the influence of grid-connected operation on the voltage.

Description

Garden energy differentiation scheduling method and system based on Monte Carlo simulation
Technical Field
The invention relates to the technical field of power grid dispatching, in particular to a park energy differentiated dispatching method and system based on Monte Carlo simulation.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
The country strongly supports the development and demonstration engineering construction of a multi-energy complementary system and promotes the application of distributed power supplies and clean energy. The continuous increase of user energy systems such as garden photovoltaic's construction scale, the load and the power characteristic difference in different gardens are more showing, make the safe economic operation of joining in marriage net receive bigger influence. For example, the centralized photovoltaic power station causes the voltage rise in holidays, but the influence degree is different, some loads can be complemented and smoothed in a garden, and if the difference of the property and the adjusting capacity of a garden energy system is not considered, the photovoltaic power station is off-line in the light-load period of holidays, so that the full utilization of renewable energy sources is not facilitated, and the efficiency and the fairness of dispatching are not facilitated.
Aiming at the problem of voltage out-of-limit caused by grid connection of a Photovoltaic power supply, the existing research determines the equality of active reduction of Photovoltaic (PV) by performing coordinated optimization on node voltage control curve parameters through voltage sensitivity analysis; but voltage control at the expense of reduced energy generation does not improve the ability of the distribution network to absorb clean power. The distributed energy storage also has better power and voltage regulation characteristics; in the existing research, a power distribution network reactive voltage coordination optimization method considering EV stochastic charging load is provided, but the method is not used as a control method measure to participate in voltage regulation; in the prior art, a microgrid voltage regulation model considering active power regulation of a distributed power supply, an EV and a load is established, but the cost problem of operation of each regulation measure is not considered; the existing research provides a reactive compensation method for regulating and controlling voltage by utilizing the variable power factor operating characteristic of an EV charger, proves that the method relieves the low-voltage phenomenon, and does not prove the condition of overvoltage regulation.
Along with energy storage, electric automobile charges, but the extensive access of resources such as interrupt load in the garden, its power and load characteristic are more various, and the operation uncertainty is stronger, and the relevant factor of dispatch is more, and the degree of difficulty of dispatch control is bigger. The utilization of stored energy to improve the safety and stability level of the power grid has become a necessary choice for a high-proportion new energy power grid in the future. However, the inventor thinks that, although there are related researches at present, how to comprehensively consider randomness parameters such as states and positions of distributed energy storage to realize voltage scheduling of a power distribution network and scheduling of distributed energy storage for an electric vehicle as distributed energy storage still needs to be further researched.
Disclosure of Invention
In order to solve the problems, the invention provides a park energy differentiated scheduling method and system based on Monte Carlo simulation, electric vehicles and photovoltaic power supplies are matched to optimally regulate and control the voltage of a power grid, Monte Carlo simulation is introduced on the basis of the charging and discharging uncertainty, the photovoltaic uncertainty and the influence of grid-connected operation on the voltage of the electric vehicles, a distributed energy storage regulation and control model with the aims of photovoltaic power generation cost, electric vehicle compensation cost and minimum node voltage qualification rate punishment cost is established, and the control of photovoltaic access node voltage is realized.
In order to achieve the purpose, the invention adopts the following technical scheme:
in a first aspect, the present invention provides a park energy differentiation scheduling method based on monte carlo simulation, including:
according to the obtained power operation data of the nodes of the photovoltaic power supply and the electric automobile connected to the power distribution network, a distribution and regulation objective function is constructed by taking the minimum reactive power output cost of the photovoltaic station, the compensation cost of the electric automobile and the penalty cost for insufficient node voltage qualified rate as targets;
constraint conditions are established according to the load flow results, battery charging and discharging characteristics and states which meet the balance of active power and reactive power of the power distribution network, and the uncertainty of photovoltaic and the uncertainty of electric vehicle charging are set randomly through Monte Carlo simulation;
and solving a power distribution network voltage regulation target function according to the constraint conditions to obtain the optimal power generation power and charge-discharge power of the photovoltaic power supply and the electric automobile connected to the power distribution network under different working conditions, so as to perform coordination control on the voltage of the power distribution network access node.
In a second aspect, the present invention provides a park energy differentiation scheduling system based on monte carlo simulation, including:
the target function building module is used for building a distribution and control target function by taking the minimum photovoltaic station reactive power output cost, the electric automobile compensation cost and the node voltage qualification rate shortage penalty cost as targets according to the obtained electric power operation data of the photovoltaic power supply and the electric automobile nodes accessed in the power distribution network;
the constraint condition module is used for constructing constraint conditions according to the load flow results, battery charging and discharging characteristics and states which meet the balance of active power and reactive power of the power distribution network, and randomly setting the uncertainty of the photovoltaic and the uncertainty of charging of the electric automobile through Monte Carlo simulation;
and the regulating and controlling module is used for solving a power distribution network voltage regulating and controlling objective function according to the constraint condition to obtain the optimal power generation power and charge-discharge power of the photovoltaic power supply and the electric automobile which are connected to the power distribution network under different working conditions, so that the voltage of the power distribution network access node is coordinately controlled.
In a third aspect, the present invention provides an electronic device comprising a memory and a processor, and computer instructions stored on the memory and executed on the processor, wherein when the computer instructions are executed by the processor, the method of the first aspect is performed.
In a fourth aspect, the present invention provides a computer readable storage medium for storing computer instructions which, when executed by a processor, perform the method of the first aspect.
Compared with the prior art, the invention has the beneficial effects that:
the method provided by the invention has the advantages that the electric automobile and the photovoltaic power supply are matched to optimally regulate and control the voltage of the power grid, and on the basis of the charging and discharging uncertainty of the electric automobile and the influence of grid-connected operation on the voltage, a power distribution network voltage regulation and control model with the target of photovoltaic power generation cost, electric automobile compensation cost and node voltage qualification rate insufficiency penalty cost is established, so that the voltage of each access node of the power distribution network is coordinately controlled.
The invention fully plays the voltage regulation role of the distributed power supply and the electric automobile connected to the power distribution network, effectively reduces the voltage fluctuation and the equipment protection action times, and improves the operation level of the power distribution network.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.
Fig. 1 is a flowchart of a campus energy differentiation scheduling based on monte carlo simulation according to embodiment 1 of the present invention;
fig. 2 is a flowchart of a method for solving a voltage regulation objective function of a power distribution network according to embodiment 1 of the present invention.
The specific implementation mode is as follows:
the invention is further described with reference to the following figures and examples.
It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention. As used herein, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise, and it should be understood that the terms "comprises" and "comprising", and any variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The embodiments and features of the embodiments of the present invention may be combined with each other without conflict.
Example 1
As shown in fig. 1, the present embodiment provides a park energy differentiation scheduling system based on monte carlo simulation, including:
s1: according to the obtained power operation data of the nodes of the photovoltaic power supply and the electric automobile connected to the power distribution network, a distribution and regulation objective function is constructed by taking the minimum reactive power output cost of the photovoltaic station, the compensation cost of the electric automobile and the penalty cost for insufficient node voltage qualified rate as targets;
s2: constraint conditions are established according to the load flow results, battery charging and discharging characteristics and states which meet the balance of active power and reactive power of the power distribution network, and the uncertainty of photovoltaic and the uncertainty of electric vehicle charging are set randomly through Monte Carlo simulation;
s4: and solving a power distribution network voltage regulation target function according to the constraint conditions to obtain the optimal power generation power and charge-discharge power of the photovoltaic power supply and the electric automobile connected to the power distribution network under different working conditions, so as to perform coordination control on the voltage of the power distribution network access node.
In the step S1, according to the characteristics of the controllable resources in the system, the output of each controllable resource is reasonably arranged with the minimum control cost F as the optimization target, wherein the output includes the photovoltaic reactive output control cost CPV,iElectric automobile compensation cost CEV,iPenalty charge C for insufficient node voltage qualification rateC：
Figure BDA0002577921860000061
In the formula, n is the number of photovoltaic power supply access nodes in the power distribution network; n is the number of electric automobile grid-connected nodes; omega1、ω2、ω3Weighting coefficients for each controllable resource participating in voltage regulation;
when in use
Figure BDA0002577921860000062
While being establishedThe electric automobile assists the control model of cooperation photovoltaic linkage control voltage.
In the step S2, in the solving process of the optimization problem, the active and reactive power balance equality constraints of the power distribution network node, that is, the power flow equation constraints, the photovoltaic reactive power output, the charging and discharging power inequality constraints of the electric vehicle, and whether the scheduling constraints are accepted or not, are specifically:
(1) equality constraints, i.e. nodal power flow constraints:
Figure BDA0002577921860000063
Figure BDA0002577921860000064
in the formula, PPVi、QPViActive and reactive power output of a photovoltaic power supply connected to a node i; pLi、QLiThe active power and the reactive power of the conventional load on the node i are consumed; pEVi、QEViThe active power and the reactive power of the electric automobile connected to the node i are calculated; u shapei、UjThe voltage amplitudes of the node i and the node j are respectively; gij、Bij、θijThe real part and the imaginary part of the admittance matrix between the node i and the node j, and the voltage phase angle difference are respectively.
(2) Photovoltaic power supply reactive power output constraint:
QPVi_min≤QPVi,t≤QPVi_max
in the formula, QPVi_min、QPVi_maxThe upper limit and the lower limit of the photovoltaic reactive power regulation capacity are respectively.
(3) Electric vehicle charging and discharging power constraint:
Figure BDA0002577921860000065
in the formula, PEVci,t、PEVdi,tEV adjustable charging and discharging power on a node i respectively; pEVci_min、PEVci_max、PEVdi_min、PEVdi_maxThe maximum charging and discharging power and the minimum active power of EV on the node i can be regulated and controlled respectively.
(4) Voltage constraint:
Uimin≤Ui≤Uimax
in the formula of UiFor each node voltage, U, in the systemi_min、Ui_maxThe upper and lower limit values of the node voltage are (0.93-1.07) UN；UNIs the voltage rating.
(5) Whether a scheduling constraint is received:
whether each distributed energy storage configuration accepts cooperative scheduling flag ucp,j(t)，ucp,j(t) ═ 0 means that the jth distributed energy storage does not accept coordinated scheduling, and the distributed energy storage is always in a charging state no matter what level the surrounding load is; u. ofcp,jAnd (t) 1 represents that the jth distributed energy storage receives cooperative scheduling. Charging probability threshold p for next time periodev,j(t+1)：
Figure BDA0002577921860000071
Wherein the total capacity p1l(t) equivalent load pavg(t), whether the charging pile accepts the cooperative scheduling mark ucp,j(t)。
In step S3, the specific optimization control method for solving the optimization objective function includes, as shown in fig. 2:
the dispatching center determines adjustable reactive power capacity and active power capacity according to historical photovoltaic power generation grid-connected system active power processing prediction;
estimating the voltage level of all photovoltaic inverters after the reactive adjustable capacity is put into operation through load flow calculation, and judging whether the voltage of the power distribution network is recovered;
if so, solving by taking the minimized photovoltaic reactive power control cost as a target, determining the actual input quantity of each control variable, and completing the voltage regulation of the power distribution network;
otherwise, the dispatching center performs load flow calculation of all the photovoltaic reactive adjustable capacity and pre-estimates the voltage level of the system;
continuously judging whether the voltage of the power distribution network is recovered, if so, solving by taking the minimum photovoltaic reactive power control cost and the penalty cost of insufficient node voltage qualification rate as targets, determining the actual input quantity of each control variable, and finishing the voltage regulation of the power distribution network;
otherwise, adding a target function of the minimum electric vehicle compensation cost to solve, determining the actual input quantity of each control variable, and completing the voltage regulation of the power distribution network.
Example 2
The embodiment provides a park energy differentiation dispatch system based on monte carlo simulation, includes:
the target function building module is used for building a distribution and control target function by taking the minimum photovoltaic station reactive power output cost, the electric automobile compensation cost and the node voltage qualification rate shortage penalty cost as targets according to the obtained electric power operation data of the photovoltaic power supply and the electric automobile nodes accessed in the power distribution network;
the constraint condition module is used for constructing constraint conditions according to the load flow results, battery charging and discharging characteristics and states which meet the balance of active power and reactive power of the power distribution network, and randomly setting the uncertainty of the photovoltaic and the uncertainty of charging of the electric automobile through Monte Carlo simulation;
and the regulating and controlling module is used for solving a power distribution network voltage regulating and controlling objective function according to the constraint condition to obtain the optimal power generation power and charge-discharge power of the photovoltaic power supply and the electric automobile which are connected to the power distribution network under different working conditions, so that the voltage of the power distribution network access node is coordinately controlled.
It should be noted that the above modules correspond to steps S1 to S3 in embodiment 1, and the above modules are the same as the examples and application scenarios realized by the corresponding steps, but are not limited to the disclosure in embodiment 1. It should be noted that the modules described above as part of a system may be implemented in a computer system such as a set of computer-executable instructions.
In further embodiments, there is also provided:
an electronic device comprising a memory and a processor and computer instructions stored on the memory and executed on the processor, the computer instructions when executed by the processor performing the method of embodiment 1. For brevity, no further description is provided herein.
It should be understood that in this embodiment, the processor may be a central processing unit CPU, and the processor may also be other general purpose processors, digital signal processors DSP, application specific integrated circuits ASIC, off-the-shelf programmable gate arrays FPGA or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, and so on. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory may include both read-only memory and random access memory, and may provide instructions and data to the processor, and a portion of the memory may also include non-volatile random access memory. For example, the memory may also store device type information.
A computer readable storage medium storing computer instructions which, when executed by a processor, perform the method described in embodiment 1.
The method in embodiment 1 may be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules in the processor. The software modules may be located in ram, flash, rom, prom, or eprom, registers, among other storage media as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware of the processor. To avoid repetition, it is not described in detail here.
Those of ordinary skill in the art will appreciate that the various illustrative elements, i.e., algorithm steps, described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
The above is only a preferred embodiment of the present invention, and is not intended to limit the present invention, and various modifications and changes will occur to those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, it is not intended to limit the scope of the present invention, and it should be understood by those skilled in the art that various modifications and variations can be made without inventive efforts by those skilled in the art based on the technical solution of the present invention.

Claims (10)

1. A park energy differentiation scheduling method based on Monte Carlo simulation is characterized by comprising the following steps:
according to the obtained power operation data of the nodes of the photovoltaic power supply and the electric automobile connected to the power distribution network, a distribution and regulation objective function is constructed by taking the minimum reactive power output cost of the photovoltaic station, the compensation cost of the electric automobile and the penalty cost for insufficient node voltage qualified rate as targets;
constraint conditions are established according to the load flow results, battery charging and discharging characteristics and states which meet the balance of active power and reactive power of the power distribution network, and the uncertainty of photovoltaic and the uncertainty of electric vehicle charging are set randomly through Monte Carlo simulation;
and solving a power distribution network voltage regulation target function according to the constraint conditions to obtain the optimal power generation power and charge-discharge power of the photovoltaic power supply and the electric automobile connected to the power distribution network under different working conditions, so as to perform coordination control on the voltage of the power distribution network access node.
2. The park energy differential scheduling method based on Monte Carlo simulation of claim 1, wherein the distribution network voltage regulation objective function is:
Figure FDA0002577921850000011
wherein n is the number of photovoltaic power supply access nodes in the power distribution network; n is the number of electric automobile grid-connected nodes; omega1、ω2、ω3And the weight coefficient of each controllable resource participating in voltage regulation.
3. The park energy differential scheduling method based on Monte Carlo simulation of claim 1, wherein in the distribution network voltage regulation objective function, when
Figure FDA0002577921850000012
And when k is 3, the control model is used for assisting the electric automobile to cooperate with the photovoltaic linkage control distribution network voltage.
4. The park energy differential scheduling method based on monte carlo simulation as claimed in claim 1, wherein the constraint condition constructed by the tidal current result satisfying the balance of the active power and the reactive power of the distribution network is:
Figure FDA0002577921850000021
Figure FDA0002577921850000022
wherein, PPVi、QPViActive and reactive power output of a photovoltaic power supply connected to a node i; pLi、QLiThe active power and the reactive power of the conventional load on the node i are consumed; pEVi、QEViThe active power and the reactive power of the electric automobile connected to the node i are calculated; u shapei、UjThe voltage amplitudes of the node i and the node j are respectively; gij、Bij、θijRespectively the real part of the admittance matrix between node i and node j,Imaginary part and voltage phase angle difference.
5. The method of claim 1, wherein the constraints further include a photovoltaic power reactive output constraint:
QPVi_min≤QPVi,t≤QPVi_max
wherein Q isPVi_min、QPVi_maxThe upper limit and the lower limit of the photovoltaic reactive power regulation capacity are respectively.
6. The park energy differential scheduling method based on monte carlo simulation of claim 1, wherein the constraint condition further comprises a charge and discharge power constraint of the electric vehicle:
Figure FDA0002577921850000023
wherein, PEVci,t、PEVdi,tEV adjustable charging and discharging power on a node i respectively; pEVci_min、PEVci_max、PEVdi_min、PEVdi_maxThe maximum charging and discharging power and the minimum active power of EV on the node i can be regulated and controlled respectively.
7. The method of claim 1, wherein the constraints further include voltage constraints of each access node of the distribution network:
Uimin≤Ui≤Uimax
wherein, UiFor each node voltage, U, in the systemi_min、Ui_maxRespectively, the upper and lower limit values of the node voltage, UNIs the voltage rating.
8. A park energy differentiation scheduling system based on Monte Carlo simulation, comprising:
the target function building module is used for building a distribution and control target function by taking the minimum photovoltaic station reactive power output cost, the electric automobile compensation cost and the node voltage qualification rate shortage penalty cost as targets according to the obtained electric power operation data of the photovoltaic power supply and the electric automobile nodes accessed in the power distribution network;
the constraint condition module is used for constructing constraint conditions according to the load flow results, battery charging and discharging characteristics and states which meet the balance of active power and reactive power of the power distribution network, and randomly setting the uncertainty of the photovoltaic and the uncertainty of charging of the electric automobile through Monte Carlo simulation;
and the regulating and controlling module is used for solving a power distribution network voltage regulating and controlling objective function according to the constraint condition to obtain the optimal power generation power and charge-discharge power of the photovoltaic power supply and the electric automobile which are connected to the power distribution network under different working conditions, so that the voltage of the power distribution network access node is coordinately controlled.
9. An electronic device comprising a memory and a processor and computer instructions stored on the memory and executed on the processor, the computer instructions when executed by the processor performing the method of any of claims 1-7.
10. A computer-readable storage medium storing computer instructions which, when executed by a processor, perform the method of any one of claims 1 to 7.
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