CN110854928B - Large-scale power distribution network risk control optimization method for distributed power supply and electric automobile - Google Patents

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

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CN110854928B
CN110854928B CN201911038505.6A CN201911038505A CN110854928B CN 110854928 B CN110854928 B CN 110854928B CN 201911038505 A CN201911038505 A CN 201911038505A CN 110854928 B CN110854928 B CN 110854928B
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output
risk
power
period
voltage
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CN110854928A (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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  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Supply And Distribution Of Alternating Current (AREA)

Abstract

The invention discloses a large-scale power distribution network risk control optimization method for a distributed power supply and an electric automobile, wherein DG has the characteristic of uncertainty of output due to energy sources, such as wind power, grid-connected power generation when wind exists, no output when no wind exists, high output of solar energy when the illumination intensity is high, and no output basically when the illumination intensity is high. Such uncertainty in DG output may cause node voltages to be too high or too low, line overload, etc., which would present a risk to the grid. The invention realizes the optimal control of DG by configuring the energy storage device in DG. 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. Thus the DG output can be considered constant over a period of time, reducing the uncertainty in DG output.

Description

Large-scale power distribution network risk control optimization method for 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 a distributed power supply and an electric automobile.
Background
According to the definition of risk, the risk size is determined by both the probability of occurrence of the risk and the severity of the consequences, so that for the risk of a large power distribution network comprising DGs (distributed power sources) and EVs (electric vehicles), the risk size can be controlled by both the probability of occurrence and the severity of the consequences.
The possibility of risk occurrence of large-scale distribution networks containing DG and EV includes two aspects, the first being system failure caused by system element failure and the second being risk due to change in operating conditions. For the first possibility, the main influencing factor is the failure rate of the system components, and the reliability of power supply of the system can be improved by replacing components with low failure rate, so that the possibility of failure occurrence is reduced, and the risk is reduced. For the second possibility, i.e. the running condition changes, the situation is very complex, including uncertainty of DG output, uncertainty of electric vehicle charging, uncertainty of load, and the like, and the analysis of these situations is difficult and difficult to control due to faults caused by external forces (typhoons, artificial misoperation, etc.), and the effect of reducing risk probability to reduce risk is not obvious.
The risk assessment of the large-scale power distribution network containing DGs and EVs is the development of the risk assessment of the traditional power distribution network, and besides the probability and the consequences of system faults are assessed, the risk assessment of the large-scale power distribution network containing DGs and EVs has new characteristics, and the impact of uncertainty of the DGs and the EVs on the power grid is mainly reflected. Because the uncertainty of the two causes fluctuation of the running condition of the power grid at any time, the fluctuation can sometimes lead the power grid to run under quite unfavorable conditions, such as the condition that DG output is reduced to the minimum, EV charging power is maximized and load level is maximized, the power grid is about to bear huge running pressure, if faults occur again at the moment, the running condition of the system is further worsened, and immeasurable loss is caused, therefore, the network containing DG and EV needs to analyze and study the uncertainty of DG and EV, and risk control and optimization are carried out 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 above purpose, the technical scheme provided by the invention is as follows:
according to the risk control optimization method of the large-scale power distribution network for the distributed power supply and the electric automobile, energy storage equipment is added at each DG, DG output is controlled, so that the risk of network loss is minimum, and meanwhile, the voltage risk and the overload risk of a line are controlled.
Further, the optimal configuration of DG output and the voltage, line and net loss results are obtained by the following steps:
s1, simulating the wave point conditions of the electric vehicle and the load in the i-th period by monte carlo, i=1, 2, 24;
s2, searching an optimal DG configuration of an ith period by taking the minimum network loss risk as an objective function, if the iteration times are exceeded, the period has no optimal DG configuration, recording unsatisfied nodes and line data, i=i+1, and returning to the step S1; otherwise, entering a step S3;
s3, simulating the stirring condition of the ith period DG through Monte Carlo;
s4, judging whether the power grid voltage meets the 95% CVar constraint of the voltage, if so, entering a 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 a step S6, otherwise, returning to the step S2;
s6, recording DG optimal processing configuration in the ith period;
s7, judging whether the period i is greater than 24, if so, outputting the optimal DG configuration and the voltage, line and network loss results for 24 hours; otherwise, i=i+1, and return to step S1
Further, in the step S4, the allowable deviation range of the grid voltage is obtained by cone processing of the risk constraint function, which is specifically as follows:
wherein V is i As the original variable, X i Y is a new variable, and alpha and beta are both coefficients.
Further, in the step S5, the grid branch overload is obtained through cone processing of the risk constraint function, which specifically includes the following steps:
wherein V is i 、V j As the original variable, G ij 、B ij 、Y ij 、X i 、X j Y is a new variable, and alpha and beta are coefficients;
the relationship between the new variable and the original variable is as follows:
Y ij ＝V i V j cosθ ij 。
further, in step S2, the constraint of the network loss risk is as follows:
wherein P is G Total For the sum of all power sources output power, including the system power source and all DG output forces, G ij 、V i 、V j As the original variable, y is the new variable, and α and β are both coefficients.
Compared with the prior art, the scheme has the following principle and advantages:
the DG has the characteristic of uncertainty of output, such as wind power, grid-connected power generation in case of wind, no output in case of no wind, high output of solar energy in case of high illumination intensity, and no output basically in the evening. Such uncertainty in DG output may cause node voltages to be too high or too low, line overload, etc., which would present a risk to the grid. According to the scheme, the energy storage device is configured in the DG, so that the DG is optimally controlled. 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. Thus the DG output can be considered constant over a period of time, reducing the uncertainty in DG output.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the services required in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the figures in the following description are only some embodiments of the present invention, and that other figures can be obtained according to these figures without inventive effort to a person skilled in the art.
Fig. 1 is a schematic structural diagram of a risk control optimization method for a large-scale power distribution network for a distributed power supply and an electric automobile.
Detailed Description
The invention is further illustrated by the following examples:
as shown in fig. 1, in the method for optimizing risk control of a large-scale power distribution network for a distributed power source and an electric automobile according to the embodiment, energy storage equipment is added at each DG, DG output is controlled, so that the risk of network loss is minimized, and meanwhile, the risk of voltage and the risk of overload of a line are controlled.
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 i-th period by monte carlo, i=1, 2, 24;
s2, searching an optimal DG configuration of an ith period by taking the minimum network loss risk as an objective function, if the iteration times are exceeded, the period has no optimal DG configuration, recording unsatisfied nodes and line data, i=i+1, and returning to the step S1; otherwise, entering a step S3;
s3, simulating the stirring condition of the ith period DG through Monte Carlo;
s4, judging whether the power grid voltage meets the 95% CVar constraint of the voltage, if so, entering a step S5, otherwise, returning to the step S2;
in this step, the allowable deviation range of the grid voltage is obtained by cone processing of the risk constraint function, which is specifically as follows:
wherein V is i As the original variable, X i Y is a new variable, and alpha and beta are both coefficients.
S5, judging whether the power grid branch meets the 95% CVar constraint of the line power, if so, entering a step S6, otherwise, returning to the step S2;
in the step, the overload of the branch circuit of the power grid is obtained through cone processing of a risk constraint function, and the method specifically comprises the following steps:
wherein V is i 、V j As the original variable, G ij 、B ij 、Y ij 、X i 、X j Y is a new variable, and alpha and beta are coefficients;
the relationship between the new variable and the original variable is as follows:
Y ij ＝V i V j cosθ ij 。
s6, recording DG optimal processing configuration in the ith period;
s7, judging whether the period i is greater than 24, if so, outputting the optimal DG configuration and the voltage, line and network loss results for 24 hours; otherwise, i=i+1, and returns to step S1.
In the step S2, the constraint of the network loss risk is as follows:
wherein P is G Total For the sum of all power sources output power, including the system power source and all DG output forces, G ij 、V i 、V j As the original variable, y is the new variable, and α and β are both coefficients.
The above embodiments are only preferred embodiments of the present invention, and are not intended to limit the scope of the present invention, so variations in shape and principles of the present invention should be covered.

Claims (1)

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, DG output is controlled, so that the network loss risk is minimum, and meanwhile, the voltage risk and the line overload risk are 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 i-th period by monte carlo, i=1, 2, 24;
s2, searching an optimal DG configuration of an ith period by taking the minimum network loss risk as an objective function, if the iteration times are exceeded, the period has no optimal DG configuration, recording unsatisfied nodes and line data, i=i+1, and returning to the step S1; otherwise, entering a step S3;
s3, simulating the stirring condition of the ith period DG through Monte Carlo;
s4, judging whether the power grid voltage meets the 95% CVaR constraint of the voltage, if so, entering a 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 a step S6, otherwise, returning to the step S2;
s6, recording DG optimal processing configuration in the ith period;
s7, judging whether the period i is greater than 24, if so, outputting the optimal DG configuration and the voltage, line and network loss results for 24 hours; otherwise, i=i+1, and return to step S1;
in the step S4, the allowable deviation range of the grid voltage is obtained through cone processing of the risk constraint function, which specifically includes the following steps:
wherein V is i As the original variable, X i Y is a new variable, and alpha and beta are coefficients;
in the step S5, the grid branch overload is obtained through cone processing of the risk constraint function, which specifically includes the following steps:
wherein V is i 、V j As the original variable, G ij 、B ij 、Y ij 、X i 、X j Y is a new variable, and alpha and beta are coefficients;
the relationship between the new variable and the original variable is as follows:
Y ij ＝V i V j cosθ ij ；
in step S2, the constraint of the network loss risk is as follows:
wherein P is G Total For the sum of all power sources output power, including the system power source and all DG output forces, G ij 、V i 、V j As 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.中国优秀硕士学位论文全文数据库工程科技Ⅱ辑.2016,(第10期),第C042-163页. *

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