CN110854928A - Large-scale power distribution network risk control optimization method facing distributed power supply and electric automobile - Google Patents

Large-scale power distribution network risk control optimization method facing distributed power supply and electric automobile Download PDF

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CN110854928A
CN110854928A CN201911038505.6A CN201911038505A CN110854928A CN 110854928 A CN110854928 A CN 110854928A CN 201911038505 A CN201911038505 A CN 201911038505A CN 110854928 A CN110854928 A CN 110854928A
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output
risk
power supply
distribution network
optimization method
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CN110854928B (en
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吴杰康
邬智江
毛晓明
吴帆
黄业广
陈灵敏
唐惠玲
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Guangdong University of Technology
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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
    • 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
    • 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
    • Y02E10/70—Wind energy
    • Y02E10/76—Power conversion electric or electronic aspects

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

Abstract

The invention discloses a risk control optimization method for a large-scale power distribution network facing a distributed power supply and an electric automobile, wherein DG has the characteristic of uncertain output due to energy sources, such as wind power, grid-connected power generation is carried out when wind exists, no output exists when no wind exists, solar energy is high in output when the illumination intensity is high, and basically no output exists at night. This DG output uncertainty may cause the node voltage to be too high or too low, line overload, etc., which will bring risks to the grid. According to the invention, the energy storage device is configured in the DG, so that the optimal control on the DG is realized. The energy storage device stores the residual electric quantity on the premise that the DG output meets the power grid requirement, and supplements the insufficient part when the DG output is insufficient. The DG output can therefore be considered constant over a period of time, reducing the uncertainty in the DG output.

Description

Large-scale power distribution network risk control optimization method facing distributed power supply and electric automobile
Technical Field
The invention relates to the technical field of power grids, in particular to a risk control optimization method for a large-scale power distribution network facing distributed power supplies and electric automobiles.
Background
According to the definition of the risk, the risk size is determined by the probability of occurrence of the risk and the severity of the consequences, so that the risk size can be controlled by the probability of the accident and the severity of the consequences for the risk of a large power distribution network comprising DGs (distributed power supplies) and EVs (electric vehicles).
The possibility of risk occurrence in large power distribution networks with DG and EV includes two aspects, the first being system failure due to system component failure and the second being risk due to changes in operating conditions. For the first possibility, the main influencing factor is the failure rate of the system element, and the reliability of the system power supply can be improved by replacing the element with low failure rate, so that the possibility of failure occurrence is reduced, and the risk is reduced. The second possibility is that the operating conditions change, wherein the conditions are very complex, including uncertainty of DG output, uncertainty of electric vehicle charging, uncertainty of load, failure due to external force (typhoon, human misoperation, etc.), and the like, and the conditions are difficult to analyze and control, and the effect of reducing risk by reducing risk probability is not obvious.
The risk assessment of the large-scale power distribution network comprising the DGs and the EVs is the development of the traditional power distribution network risk assessment, except for the assessment of the probability and the consequences of system faults, the risk assessment has a new characteristic, and the impact of the uncertainty of the DGs and the EVs on the power grid is mainly reflected. Because the uncertainty of the DG and the uncertainty of the EV cause the fluctuation of the operation condition of the power grid, the fluctuation can cause the power grid to operate under quite unfavorable conditions, such as the condition that the DG output is reduced to the minimum, the EV charging power reaches the maximum, the load level reaches the maximum, the power grid is subjected to huge operation pressure, if the fault occurs again, the operation condition of the system is further worsened, and the immeasurable loss is caused, therefore, the network containing the DG and the EV needs to analyze and research the uncertainty of the DG and the EV, and carry out risk control and optimization through certain measures.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a risk control optimization method for a large-scale power distribution network facing a distributed power supply and an electric automobile.
In order to achieve the purpose, the technical scheme provided by the invention is as follows:
the large-scale power distribution network risk control optimization method for the distributed power supply and the electric automobile adds energy storage equipment at each DG, controls the DG to output power, minimizes the network loss risk, and controls the voltage risk and the line overload risk.
Further, the optimal configuration of DG output and the voltage, line and network loss results are obtained through the following steps:
s1, simulating the wave point conditions of the electric vehicle and the load in the ith time period by monte carlo, i being 1, 2.., 24;
s2, searching the optimal DG configuration in the ith time interval by taking the minimum network loss risk as an objective function, if the number of iterations is exceeded, recording unsatisfied node and line data, wherein i is i +1, and returning to the step S1, wherein the time interval has no optimal DG configuration; otherwise, go to step S3;
s3, simulating a DG dialing condition in the ith time period through Monte Carlo;
s4, judging whether the grid voltage meets the 95% CVar constraint, if so, entering the step S5, otherwise, returning to the step S2;
s5, judging whether the power grid branch meets the 95% CVar constraint of the line power, if so, entering the step S6, otherwise, returning to the step S2;
s6, recording the optimal DG processing configuration in the ith time period;
s7, judging whether the time period i is greater than 24, if so, outputting 24-hour optimal DG configuration and voltage, line and network loss results; otherwise, i is i +1, and returns to step S1
Further, in step S4, the allowable offset range of the grid voltage is obtained through a cone processing of the risk constraint function, which is specifically as follows:
Figure BDA0002252209520000031
Figure BDA0002252209520000032
wherein, ViAs a primary variable, XiY is a new variable, α and β are both coefficients.
Further, in step S5, the grid branch overload is obtained through a cone processing of the risk constraint function, which is specifically as follows:
Figure BDA0002252209520000033
wherein, Vi、VjAs a primary variable, Gij、Bij、Yij、Xi、XjY is a new variable, both α and β are coefficients;
the relationship between the new variables and the original variables is as follows:
Figure BDA0002252209520000041
Yij＝ViVjcosθij。
further, in step S2, the constraint of the network loss risk is as follows:
Figure BDA0002252209520000042
Figure BDA0002252209520000043
Figure BDA0002252209520000045
wherein, PG totalFor all power supply output power sums, including system power supply and all DG output, Gij、Vi、VjIs the original variable, y is the new variable, and α and β are both coefficients.
Compared with the prior art, the principle and the advantages of the scheme are as follows:
the DG has the characteristic of uncertain output due to energy sources, such as wind power, grid-connected power generation is carried out when wind exists, no output exists when no wind exists, the output of solar energy is high when the illumination intensity is high, and no output exists basically at night. This DG output uncertainty may cause the node voltage to be too high or too low, line overload, etc., which will bring risks to the grid. According to the scheme, the energy storage device is configured in the DG, so that the optimal control of the DG is realized. The energy storage device stores the residual electric quantity on the premise that the DG output meets the power grid requirement, and supplements the insufficient part when the DG output is insufficient. The DG output can therefore be considered constant over a period of time, reducing the uncertainty in the DG output.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the services required for the embodiments or the technical solutions in the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a schematic structural diagram of a risk control optimization method for a large-scale power distribution network facing distributed power supplies and electric vehicles according to the present invention.
Detailed Description
The invention will be further illustrated with reference to specific examples:
as shown in fig. 1, according to the method for controlling and optimizing the risk of the large distribution network facing the distributed power supply and the electric vehicle, energy storage devices are added at each DG, and the DG output is controlled, so that the risk of network loss is minimized, and the risk of voltage and line overload is controlled.
The optimal configuration of DG output and the results of voltage, line and network loss are obtained through the following steps:
s1, simulating the wave point conditions of the electric vehicle and the load in the ith time period by monte carlo, i being 1, 2.., 24;
s2, searching the optimal DG configuration in the ith time interval by taking the minimum network loss risk as an objective function, if the number of iterations is exceeded, recording unsatisfied node and line data, wherein i is i +1, and returning to the step S1, wherein the time interval has no optimal DG configuration; otherwise, go to step S3;
s3, simulating a DG dialing condition in the ith time period through Monte Carlo;
s4, judging whether the grid voltage meets the 95% CVar constraint, if so, entering the step S5, otherwise, returning to the step S2;
in this step, the allowable offset range of the grid voltage is obtained by cone processing of a risk constraint function, which is specifically as follows:
Figure BDA0002252209520000051
Figure BDA0002252209520000052
Figure BDA0002252209520000061
wherein, ViAs a primary variable, XiY is a new variable, α and β are both coefficients.
S5, judging whether the power grid branch meets the 95% CVar constraint of the line power, if so, entering the step S6, otherwise, returning to the step S2;
in this step, the grid branch overload is obtained by cone processing of a risk constraint function, which is specifically as follows:
Figure BDA0002252209520000062
wherein, Vi、VjAs a primary variable, Gij、Bij、Yij、Xi、XjY is a new variable, both α and β are coefficients;
the relationship between the new variables and the original variables is as follows:
Figure BDA0002252209520000063
Yij＝ViVjcosθij。
s6, recording the optimal DG processing configuration in the ith time period;
s7, judging whether the time period i is greater than 24, if so, outputting 24-hour optimal DG configuration and voltage, line and network loss results; otherwise, i is i +1, and returns to step S1.
In step S2, the constraint of the network loss risk is as follows:
Figure BDA0002252209520000071
Figure BDA0002252209520000072
Figure BDA0002252209520000073
Figure BDA0002252209520000074
wherein, PG totalFor all power supply output power sums, including system power supply and all DG output, Gij、Vi、VjIs the original variable, y is the new variable, and α and β are both coefficients.
The above-mentioned embodiments are merely preferred embodiments of the present invention, and the scope of the present invention is not limited thereto, so that variations based on the shape and principle of the present invention should be covered within the scope of the present invention.

Claims (5)

1. The large-scale power distribution network risk control optimization method for the distributed power supply and the electric automobile is characterized in that energy storage equipment is added at each DG, the DG output is controlled, the network loss risk is minimized, and the voltage risk and the line overload risk are controlled at the same time.
2. The distributed power supply and electric vehicle oriented large power distribution network risk control optimization method according to claim 1, wherein the optimal DG output configuration and voltage, line and network loss results are obtained through the following steps:
s1, simulating the wave point conditions of the electric vehicle and the load in the ith time period by monte carlo, i being 1, 2.., 24;
s2, searching the optimal DG configuration in the ith time interval by taking the minimum network loss risk as an objective function, if the number of iterations is exceeded, recording unsatisfied node and line data, wherein i is i +1, and returning to the step S1, wherein the time interval has no optimal DG configuration; otherwise, go to step S3;
s3, simulating a DG dialing condition in the ith time period through Monte Carlo;
s4, judging whether the grid voltage meets the 95% CVar constraint, if so, entering the step S5, otherwise, returning to the step S2;
s5, judging whether the power grid branch meets the 95% CVar constraint of the line power, if so, entering the step S6, otherwise, returning to the step S2;
s6, recording the optimal DG processing configuration in the ith time period;
s7, judging whether the time period i is greater than 24, if so, outputting 24-hour optimal DG configuration and voltage, line and network loss results; otherwise, i is i +1, and returns to step S1.
3. The distributed power supply and electric vehicle oriented large power distribution network risk control optimization method according to claim 2, wherein in the step S4, the allowable deviation range of the grid voltage is obtained through a cone processing of a risk constraint function, specifically as follows:
Figure FDA0002252209510000011
Figure FDA0002252209510000022
wherein, ViAs a primary variable, XiY is a new variable, α and β are both coefficients.
4. The distributed power supply and electric vehicle oriented large power distribution network risk control optimization method according to claim 2, wherein in the step S5, the grid branch overload is obtained through a cone processing of a risk constraint function, specifically as follows:
wherein, Vi、VjAs a primary variable, Gij、Bij、Yij、Xi、XjY is a new variable, both α and β are coefficients;
the relationship between the new variables and the original variables is as follows:
Figure FDA0002252209510000024
Yij＝ViVjcosθij。
5. the distributed power and electric vehicle oriented large power distribution network risk control optimization method according to claim 2, wherein in step S2, the constraints of the network loss risk are as follows:
Figure FDA0002252209510000032
Figure FDA0002252209510000033
Figure FDA0002252209510000034
wherein, PG totalFor all power supply output power sums, including system power supply and all DG output, Gij、Vi、VjIs the original variable, y is the new variable, and α and β are both coefficients.
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Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106549378A (en) * 2016-12-09 2017-03-29 国网江苏省电力公司金湖县供电公司 It is a kind of to exert oneself probabilistic distribution coordinated dispatching method for distributed power source
CN109560574A (en) * 2018-12-14 2019-04-02 国网江苏省电力有限公司苏州供电分公司 A kind of intelligent distribution network space truss project method considering uncertain factor

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106549378A (en) * 2016-12-09 2017-03-29 国网江苏省电力公司金湖县供电公司 It is a kind of to exert oneself probabilistic distribution coordinated dispatching method for distributed power source
CN109560574A (en) * 2018-12-14 2019-04-02 国网江苏省电力有限公司苏州供电分公司 A kind of intelligent distribution network space truss project method considering uncertain factor

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
邬智江: "面向分布式电源和电动汽车的配电网运行风险评估" *

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