CN112131733B - Distributed power supply planning method considering influence of charging load of electric automobile - Google Patents

Distributed power supply planning method considering influence of charging load of electric automobile Download PDF

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CN112131733B
CN112131733B CN202010969039.XA CN202010969039A CN112131733B CN 112131733 B CN112131733 B CN 112131733B CN 202010969039 A CN202010969039 A CN 202010969039A CN 112131733 B CN112131733 B CN 112131733B
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distributed power
electric automobile
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CN112131733A (en
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马丽叶
王海锋
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Yanshan University
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • 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
    • B60L8/00—Electric propulsion with power supply from forces of nature, e.g. sun or wind
    • B60L8/003—Converting light into electric energy, e.g. by using photo-voltaic systems
    • 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
    • B60L8/00—Electric propulsion with power supply from forces of nature, e.g. sun or wind
    • B60L8/006—Converting flow of air into electric energy, e.g. by using wind turbines
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
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    • G06F—ELECTRIC DIGITAL DATA PROCESSING
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
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    • G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
    • 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
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    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
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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
    • 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
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    • G06F2111/00—Details relating to CAD techniques
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    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2113/00—Details relating to the application field
    • G06F2113/04—Power grid distribution 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
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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
    • 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
    • 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
    • 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
    • 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

Abstract

The invention discloses a distributed power supply planning method considering the influence of charging load of an electric automobile, which comprises the following steps: modeling wind power, a photovoltaic generator set, a user load and an electric automobile charging load; sampling wind speed, illumination intensity and demand load; carrying out scene clustering and reduction on wind power, photovoltaic, user load and electric vehicle charging load; weighting four indexes of network loss, reactive loss, line load index and voltage deviation to obtain a system comprehensive performance index serving as an index for evaluating the system performance; constructing a multi-target distributed power supply planning model, constructing a fuzzy membership model for a target function, and characterizing an optimization result by an overall satisfaction degree; and solving by using a hybrid search strategy combining an improved particle swarm algorithm and an interior point method. The invention reduces the reduction of comprehensive performance caused by accessing distributed energy and electric vehicles into the power grid, and the power grid company sets the network performance indexes according to the preference to determine the positions and the capacities of the distributed energy and the electric vehicles, thereby achieving the optimal economic benefit.

Description

Distributed power supply planning method considering influence of charging load of electric automobile
Technical Field
The invention relates to the technical field of distributed power supply planning, in particular to a distributed power supply planning method for considering the influence of charging load of an electric automobile.
Background
According to the display of '2020 plus 2026 years China electric automobile charging infrastructure industry development status survey and development trend analysis report': by the end of 2019, the quantity of electric vehicles in China reaches 381 thousands of vehicles, which accounts for 1.4609% of the total quantity of the vehicles, and compared with the end of 2018, the quantity of the electric vehicles is increased by 120 thousands of vehicles and increased by 46.05%. Wherein, the pure electric vehicles hold 310 thousands of vehicles, accounting for 81.19% of the total amount of the new energy vehicles. The increment of the new energy automobile is more than 100 thousands of automobiles in two consecutive years and is in a rapid growth trend. From the growth trend, electric automobiles in China enter a rapid development period, and the development of the electric automobiles inevitably affects the development and operation of a power grid.
The distributed power supply output is influenced by climate and environment, so that the distributed power supply output has strong time sequence and fluctuation, and the electric automobile also has strong randomness in the aspects of initial charging time, battery charging characteristics, different charging power and the like. In the aspect of power supply planning, such a special charging load of an electric vehicle is not usually considered, most of the charging facility planning of the electric vehicle at present mainly meets the charging requirement according to the behavior of a user, and the economic impact on a power grid is not considered much. The distributed power supply permeability has an important influence on the economy of a power grid, the distributed power supply permeability has the functions of raising voltage and reducing grid loss on the power grid within a certain range, the power grid mainly acquires electric energy in a mode of purchasing electricity from a distributed power plant at present, the investment cost of the power grid is increased due to the excessively high distributed power supply permeability, and even the voltage is out of limit, so that the distributed power supply permeability is required to be maximized within a reasonable interval as much as possible.
In summary, it is necessary to invent a power distribution network power supply planning method considering the electric vehicle load influence and the distributed power supply permeability so as to ensure the rationality of the distributed power supply planning.
Disclosure of Invention
The invention aims to provide a distributed power supply planning method considering the influence of the charging load of an electric vehicle so as to ensure the rationality of the distributed power supply planning.
In order to achieve the above purpose, the following technical scheme is adopted, and the specific scheme is as follows:
a distributed power supply planning method considering influence of charging load of an electric automobile comprises the following steps:
step 1, establishing a mathematical model of a distributed power supply and a load; the mathematical model of the distributed power supply considers wind power and photovoltaic randomness and comprises a wind power output random model and a photovoltaic processing random model; the load mathematical model comprises a common load random model and an electric vehicle charging load random model;
step 2, sampling the wind speed, the illumination intensity and the required load by using a state duration sampling technology according to the characteristics of the distributed power supply and the load;
step 3, carrying out scene clustering and reduction on wind power output, photovoltaic output, common load required power and electric vehicle charging load value by using a hierarchical clustering method;
step 4, determining and calculating the power supply permeability IEVP of the electric automobile according to the definition of the permeability of the distributed power supply, and regarding the electric automobile load with uncertainty as a power supply capable of absorbing power from a power grid;
step 5, taking the network loss, the reactive loss, the line load index and the voltage deviation as indexes for evaluating the network performance of the system, and weighting and adding the four indexes to obtain a comprehensive performance index of the system network;
step 6, constructing a multi-target distributed power supply planning model according to the minimum comprehensive performance index of the system network and the maximum IEVP;
step 7, constructing fuzzy membership functions for the two objective functions, converting the fuzzy membership functions into a single objective problem, and characterizing an optimization result by a total satisfaction degree;
and 8, solving the single-target problem by adopting a hybrid search strategy combining a self-adaptive weight particle swarm algorithm and an interior point method to obtain an optimal solution of the power distribution network planning.
Further, the wind power output stochastic model includes:
by adopting a Weber distribution model, the functional relation between the output of the fan and the wind speed is as follows:
Figure GDA0003493097890000021
wherein, PWTFor fan output, PSRated power of the fan, v is wind speed, vin、vs、voutRespectively the cut-in wind speed, the rated wind speed and the cut-out wind speed;
the wind speed probability density function is:
Figure GDA0003493097890000022
wherein k and c are Weber shape parameters and dimensions;
the photovoltaic output stochastic model comprises:
the illumination distribution adopts a Beta distribution model, and the relationship between the output power and the illumination intensity is as follows:
Figure GDA0003493097890000031
wherein, PPVOutput power, G is actual illumination intensity, PqRated power for photovoltaic, GqIs the rated illumination intensity;
the illumination intensity probability density function is:
Figure GDA0003493097890000032
wherein Γ (—) represents a gamma function; gmaxMaximum illumination intensity; alpha and Beta are shape parameters of Beta distribution;
the common load stochastic model comprises:
the normal load satisfies normal distribution, and the probability density function is:
Figure GDA0003493097890000033
wherein, PlAnd QlRespectively representing active load and reactive load; mu.sPAnd σPRespectively an expected value and a standard deviation of the active load; mu.sQAnd σQRespectively an expected value and a standard deviation of the reactive load;
the electric vehicle charging load stochastic model comprises:
the daily driving mileage d of the electric automobile user approximately follows lognormal distribution, and the probability density function and the driving expected value are as follows:
Figure GDA0003493097890000034
Figure GDA0003493097890000035
wherein, f (d) is a probability density function of the daily driving mileage d of the electric vehicle user, and E (D) is a driving expected value; mu.sdAnd σdThe expected value and the standard deviation of the daily mileage;
the moment of starting charging follows normal distribution, and the probability density function is as follows:
Figure GDA0003493097890000041
wherein x represents the time when the electric automobile starts to charge; (x) is a probability density function of the moment of starting charging; mu.sdAnd σdIs a dayThe expected value and standard deviation of the driving mileage;
the charging duration of the electric automobile follows normal distribution, and the probability density function is as follows:
Figure GDA0003493097890000042
wherein t represents the charging time of the electric automobile; f (t) is a probability density function of the charging time of the electric automobile; mu.stAnd σtThe expected value and the standard deviation of the charging time are obtained;
further, the state duration sampling technique includes: according to the time sequence characteristic, a cycle period is appointed, each state is calculated, and required index data are obtained through a statistical rule, wherein the index data are as follows:
Figure GDA0003493097890000043
wherein n is the cycle number; m is the value of the sample in a single period, xtIs a state variable at the time t; f (x)t) As a function of the index versus the state variable,
Figure GDA0003493097890000044
is index data;
for the mathematical model in step 1, the state variables are:
Figure GDA0003493097890000045
wherein R isbeta(alpha, Beta) is a Beta random number with parameters of alpha, Beta; rWeibull(k, c) are weber random numbers with the parameters k, c; rnormal(mu, sigma) is a normal distribution random number with the parameters of mu and sigma; PV is photovoltaic output, and WG is wind power output.
Further, the scene clustering includes:
clustering the generated scenes by using a hierarchical clustering method, wherein the probability of the scenes contained in a clustering center is typical scene data, an evaluation index is the distance between a class and a new class, an elbow rule is used for determining a polymerization coefficient, and the optimal clustering number is found, wherein the formula is as follows:
Figure GDA0003493097890000051
wherein J is the aggregation coefficient, K is the number of clusters, CkDenotes the kth class, ukRepresenting the position of the center of gravity, x, of the classiIs the ith sample position.
Further, the IEVP is:
Figure GDA0003493097890000052
wherein eta issIs IEVP; eta is the permeability of the distributed power supply; etaEVThe electric vehicle load permeability; alpha is alphai、βiThe node is a 0-1 decision variable, a distributed power supply or an electric automobile exists in the node and is 1, otherwise, the node is 0; pDGiInstalled capacity, P, of the ith distributed power supplyEViCharging power for the node electric vehicle; pLmaxAnd n is the number of system nodes for the maximum load of the system.
Further, it is characterized in that,
the network loss index is as follows:
Figure GDA0003493097890000053
wherein R isPlossIs a loss index; pDGlossCalculating the loss power on the line for the distributed power supply and the electric automobile load flow; plossCalculating the loss power on the line for the load flow without the distributed power supply and the electric automobile;
the reactive loss index is:
Figure GDA0003493097890000054
wherein R isQlossIs a reactive loss index; qDGlossCalculating the reactive power lost on the line for the distributed power supply and the electric automobile load flow; qlossCalculating the reactive power lost on the line for the power flow without the distributed power supply and the electric automobile;
the line load index is:
Figure GDA0003493097890000055
wherein R isLIs a line load index; n is the number of lines; pLiLoading the line; pSiThe capacity of the line;
the voltage deviation index is:
Figure GDA0003493097890000061
wherein R isVIs a voltage deviation index; v1Is the substation node voltage; viIs the ith node voltage deviation;
the comprehensive performance indexes of the system network are as follows: r ═ xi1RPloss+ξ2RQloss+ξ3RL+ξ4RV；
Wherein ξ1，ξ2，ξ3，ξ4And the weight of each index is determined by the preference of the power grid company.
Further, the constraints in the multi-objective distributed power supply planning model include: the method comprises the following steps of (1) system operation power flow constraint, voltage deviation constraint, line load constraint, distributed power supply permeability constraint and electric vehicle charging load permeability constraint;
wherein the system operation power flow constraint is as follows:
Figure GDA0003493097890000062
wherein k represents a scene; i represents a node, Ui，k、Uj，kRespectively the k < th > sceneThe voltage amplitude, P, of nodes i and ji，k、Qi，kRespectively the active power and the reactive power of the ith node of the kth scene; n is the total number of nodes; gij，k、Bij，k、δij，kAdmittance and phase angle difference between the kth scene nodes i and j respectively;
the voltage deviation constraint is:
|V1-Vj|≤ΔVmax；
wherein, VjIs the voltage deviation of the j-th node, Δ VmaxIs the maximum voltage deviation allowed;
the line load constraints are:
PLi≤PSi；
the distributed power supply permeability constraint is as follows:
η≤ηmax；
wherein eta ismaxThe maximum permeability of the distributed power supply is obtained;
the electric automobile charging load permeability constraint is as follows:
ηEV≤ηEVmax；
wherein eta isEVmaxAnd (4) the maximum permeability of the charging load of the electric automobile.
Further, the constructing a fuzzy membership function for the two objective functions and converting the fuzzy membership function into a single-objective problem comprises:
constructing corresponding fuzzy membership functions for the two objective functions, and expressing the optimal degree of each objective function, wherein the value of the membership is closer to 1, which means that the objective function is closer to the optimal, and the fuzzy membership functions are as follows:
Figure GDA0003493097890000071
and solving the minimum value of the fuzzy membership function of the two objective functions to maximize the minimum value of the two objective functions so as to obtain the total satisfaction degree: max ρ min { ρ ═1，ρ2}；
Wherein i is 1, 2; rhoiThe fuzzy membership degree corresponding to the ith objective function; f. ofiIs the value of the ith sub-target, fimaxIs the ith target function upper limit value; f. ofibestThe optimal value optimized for the ith objective function alone.
Further, the solving of the single-target problem by adopting a hybrid search strategy combining an adaptive weight particle swarm algorithm and an interior point method to obtain an optimal solution of the power distribution network planning includes:
performing iteration by using a self-adaptive weight particle swarm algorithm, and recording an individual value and an optimal value of each iteration;
solving by taking the individual value as an initial value of an interior point method to obtain an accurate optimal solution corresponding to the initial value;
and recording all iterative solutions, and taking out the minimum value and the corresponding individual value so as to obtain the optimal solution of the power distribution network planning.
Compared with the prior art, the invention has the following advantages:
1. the comprehensive performance indexes of the system are set, the power grid can set index weights according to own preference, and the model can work out the optimal plan corresponding to the preference.
2. Compared with other distributed power supply planning models considering electric vehicles, the distributed power supply permeability (IEVP) considering the electric vehicle load is provided, the IEVP can represent the social cost, and a suitable planning scheme is found out through optimizing the permeability.
3. The multi-objective programming is simplified by utilizing the fuzzy idea and converted into a single objective function, the algorithm is simple and easy to operate after the conversion, and the simplification process is more perfect compared with other method theories.
4. A hybrid search strategy combining the self-adaptive weight particle swarm algorithm and the interior point method is provided, the iteration times can be shortened through the alternate operation of the two algorithms, and the obtained result has better convergence compared with the common heuristic algorithm.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are 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 block diagram of a system architecture for power distribution network power supply planning in an embodiment of the present invention;
FIG. 2 is a flow chart of a distributed power supply planning method in accordance with an embodiment of the present invention to account for the charging load impact of an electric vehicle;
FIG. 3 is a flowchart of a hybrid search strategy solution model using a combination of an adaptive weight particle swarm algorithm and an interior point method according to an embodiment of the present invention.
Detailed Description
The invention provides a distributed power supply planning method considering the influence of the charging load of an electric vehicle under the condition that a large-scale distributed power supply and the charging load of the electric vehicle are connected to an active power distribution network, so that the rationality of the distributed power supply planning is ensured.
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations 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.
As shown in fig. 1 and fig. 2, a distributed power supply planning method for considering the influence of the charging load of the electric vehicle in the embodiment of the present invention includes the following specific steps:
(1) establishing a mathematical model of the distributed power supply and the load, wherein the mathematical model of the distributed power supply considers wind power and photovoltaic randomness and comprises a wind power output random model and a photovoltaic processing random model; the mathematical model of the load is divided into a common load random model and an electric vehicle charging load model.
(1-1) wind power output stochastic model
By adopting a Weber distribution model, the functional relation between the output of the fan and the wind speed is as follows:
Figure GDA0003493097890000091
wherein, PWTFor fan output, PSRated power of the fan, v is wind speed, vin、vs、voutRespectively the cut-in wind speed, the rated wind speed and the cut-out wind speed;
the wind speed probability density function is:
Figure GDA0003493097890000092
wherein k and c are Weber shape parameters and dimensions.
(1-2) photovoltaic output stochastic model
The illumination distribution adopts a Beta distribution model, and the relationship between the output power and the illumination intensity is as follows:
Figure GDA0003493097890000093
wherein, PPVOutput power, G is actual illumination intensity, PqRated power for photovoltaic, GqIs the rated illumination intensity;
the illumination intensity probability density function is:
Figure GDA0003493097890000101
wherein Γ (—) represents a gamma function; gmaxMaximum illumination intensity; and alpha and Beta are shape parameters of Beta distribution.
(1-3) ordinary load stochastic model
The normal load satisfies normal distribution, and the probability density function is:
Figure GDA0003493097890000102
Figure GDA0003493097890000103
wherein, PlAnd QlRespectively representing active load and reactive load; mu.sPAnd σPRespectively an expected value and a standard deviation of the active load; mu.sQAnd σQRespectively an expected value and a standard deviation of the reactive load;
(1-4) electric vehicle charging load stochastic model
The daily driving mileage d of the electric automobile user approximately follows lognormal distribution, and the probability density function and the driving expected value are as follows:
Figure GDA0003493097890000104
Figure GDA0003493097890000105
wherein the content of the first and second substances,f (d) is a probability density function of the daily driving mileage d of the electric vehicle user, and E (D) is a driving expected value; mu.sdAnd σdThe expected value and standard deviation of the daily driving mileage d;
the moment of starting charging follows normal distribution, and the probability density function is as follows:
Figure GDA0003493097890000106
wherein x represents the time when the electric automobile starts to charge; (x) is a probability density function of the moment of starting charging; mu.saAnd σaThe expected value and the standard deviation of the charging starting time are obtained;
the charging duration of the electric automobile follows normal distribution, and the probability density function is as follows:
Figure GDA0003493097890000107
wherein t represents the charging time of the electric automobile; f (t) is a probability density function of the charging time of the electric automobile; mu.stAnd σtThe expected value and standard deviation of the charging time period.
(2) Sampling wind speed, illumination intensity and required load by using a state duration sampling technology according to the characteristics of different distributed power supplies and loads;
the state duration sampling technique is that a cycle period is specified according to a time sequence characteristic, each state is calculated, and required index data is obtained through a statistical rule, and the following formula is provided:
Figure GDA0003493097890000111
wherein n is the cycle number; m is the value of the sample in a single period, xtIs a state variable at the time t; f (x)t) As a function of the index versus the state variable,
Figure GDA0003493097890000112
is index data;
for the mathematical model in step 1, the state variables are:
Figure GDA0003493097890000113
wherein R isbeta(alpha, Beta) is a Beta random number with parameters of alpha, Beta; rWeibull(k, c) are weber random numbers with the parameters k, c; rnormal(mu, sigma) is a normal distribution random number with the parameters of mu and sigma; PV is photovoltaic output, and WG is wind power output.
(3) Performing scene clustering and reduction on wind power, photovoltaic output, required power of a common load and a charging load value of the electric automobile by using a hierarchical clustering method;
clustering the generated scenes by using a hierarchical clustering method, wherein the probability of the scenes contained in a clustering center is typical scene data, an evaluation index is the distance between a class and a new class, an elbow rule is used for determining a polymerization coefficient, and finally, the optimal clustering number is found, wherein the formula is as follows:
Figure GDA0003493097890000114
wherein J is the aggregation coefficient, K is the number of clusters, CkDenotes the kth class, ukRepresenting the position of the center of gravity, x, of the classiIs the ith sample position.
Compared with the K-means clustering method, the hierarchical clustering method has the greatest advantage that the hierarchical clustering method does not need to initially specify the number of clusters. The initial clustering number selection of the K-means clustering method is not suitable for directly influencing the final clustering result, the hierarchical clustering method can obtain all clustering conditions, then the aggregation coefficient is calculated according to the elbow rule, and the optimal classification result is found.
(4) Determining power supply permeability (IEVP) of the electric vehicle according to the definition of the distributed power supply permeability, and regarding the electric vehicle load with uncertainty as a power supply capable of absorbing power from a power grid;
the permeability of the traditional distributed power supply is the ratio of the installed total capacity of the distributed power supply to the maximum load of the system, and is as follows:
Figure GDA0003493097890000121
wherein eta is the permeability of the distributed power supply; etaWGPermeability for wind power generation; etaPVThe photovoltaic power generation permeability; pDGiThe installed capacity of the ith distributed power supply; pLmaxIs the maximum load of the system.
When considering the electric vehicle load, the invention provides a distributed power supply permeability (IEVP) considering the electric vehicle load, which is as follows:
Figure GDA0003493097890000122
wherein eta issIs IEVP; eta is the permeability of the distributed power supply; etaEVThe electric vehicle load permeability; alpha is alphai、βiThe node is a 0-1 decision variable, a distributed power supply or an electric automobile exists in the node and is 1, otherwise, the node is 0; pDGiInstalled capacity, P, of the ith distributed power supplyEViCharging power for the node electric vehicle; pLmaxAnd n is the number of system nodes for the maximum load of the system.
ηsThe significance of (1) is that eta, for the cost of the whole society, considers the state of the electric vehicle during charging as an uncertain distributed power supply with negative input power to the systemsThe larger the size, the smaller the cost of the whole society, and therefore, η should be increased as much as possible while satisfying certain constraintss。
(5) Taking the network loss, the reactive loss, the line load index and the voltage deviation as indexes for evaluating the network performance of the system, and weighting and adding the four indexes to obtain a comprehensive performance index of the system network;
the grid loss index, reactive loss index, line load index and voltage deviation of the system are defined as follows:
net loss index:
Figure GDA0003493097890000131
wherein R isPlossIs a loss index; pDGlossCalculating the loss power on the line for the distributed power supply and the electric automobile load flow; plossCalculating the loss power on the line for the load flow without the distributed power supply and the electric automobile;
the exponent shows the effect of the penetration of distributed power and electric vehicles on the system active network loss, RPlossThe smaller the system performance the better.
Reactive loss exponent:
Figure GDA0003493097890000132
wherein R isQlossIs a reactive loss index; qDGlossCalculating the reactive power lost on the line for the distributed power supply and the electric automobile load flow; qlossAnd calculating reactive power lost on the line for the power flow without the distributed power supply and the electric automobile.
The exponent shows the effect of the penetration of the distributed power supply and the electric vehicle on the reactive network loss of the system, RQlossThe smaller the system performance the better.
③ line load index:
Figure GDA0003493097890000133
wherein R isLIs a line load index; n is the number of lines; pLiLoading the line; pSiIs the line capacity.
The index indicates the maximum value of the line load to capacity ratio in all lines, and the smaller the index, the larger the line available capacity, and the better the system performance.
Voltage deviation index:
Figure GDA0003493097890000134
wherein R isVIs a voltage deviation index; v1Is the substation node voltage; viIs the ith node voltage deviation.
The index indicates the maximum value of the voltage deviation ratio of the node to the substation, and the smaller the index, the smaller the voltage deviation, and the better the system performance.
Obtaining the comprehensive performance index of the system network after weighting:
R＝ξ1RPloss+ξ2RQloss+ξ3RL+ξ4RV (19)
wherein ξ1，ξ2，ξ3，ξ4And the weight of each index is determined by the preference of the power grid company.
(6) Constructing a multi-target distributed power supply (DG) planning model according to the minimum comprehensive performance index and the maximum IEVP of the system network as target functions;
the objective functions and constraint conditions of the multi-objective distributed power supply planning model are as follows:
(6-1) objective function:
aiming at the minimum comprehensive performance index and the maximum IEVP of the system network:
Figure GDA0003493097890000141
(6-2) constraint conditions:
firstly, system operation flow constraint:
Figure GDA0003493097890000142
wherein k represents a scene; i represents a node, Ui，k、Uj，kThe voltage magnitudes of nodes i and j respectively,Pi，k、Qi，krespectively the active power and the reactive power of the ith node of the kth scene; n is the total number of nodes; gij，k、Bij，k、δij，kRespectively, the admittance and phase angle difference between the kth scene nodes i and j.
Voltage deviation constraint:
|V1-Vj|≤ΔVmax (22)
wherein, VjIs the voltage deviation of the j-th node, Δ VmaxThe maximum voltage deviation allowed.
Third, line load constraint:
PLi≤PSi (23)
and fourthly, restricting the permeability of the distributed power supply:
η≤ηmax (24)
wherein eta ismaxThe maximum permeability of the distributed power supply.
Constraint of charging load permeability of the electric automobile:
ηEV≤ηEVmax (25)
wherein eta isEVmaxAnd (4) the maximum permeability of the charging load of the electric automobile.
(7) Constructing a fuzzy membership function for the multi-objective function, converting the fuzzy membership function into a single-objective problem, and characterizing an optimization result by an overall satisfaction degree;
constructing corresponding fuzzy membership functions for the two objective functions, and expressing the optimal degree of each objective function, wherein the value of the membership is more approximate to 1, which means that the objective function is more approximate to the optimal degree, and the fuzzy membership functions are as follows:
Figure GDA0003493097890000151
and solving the minimum value of the fuzzy membership function of the two objective functions to maximize the minimum value of the two objective functions so as to obtain the total satisfaction degree:
maxρ＝min{ρ1，ρ2} (27)
wherein i is 1, 2; rhoiThe fuzzy membership degree corresponding to the ith objective function; f. ofiIs the value of the ith sub-target, fimaxIs the ith target function upper limit value; f. ofibestThe optimal value optimized for the ith objective function alone.
(8) In the aspect of model solving, the method belongs to mixed integer nonlinear programming (MINLP), and adopts a mixed search strategy of combining an adaptive weight particle swarm algorithm and an interior point method to solve.
The solving algorithm adopts a mixed search strategy combining an adaptive weight particle swarm algorithm and an interior point method to solve, the adaptive weight particle swarm algorithm has strong capability of searching for global optimum, but the algorithm is unstable, the optimum solution cannot be found every time, the interior point method can accurately find the optimum solution, but because the selection of an initial value is easy to fall into local optimum, and the interior point method is difficult to find global optimum for a non-convex function, the mixed search strategy combining the adaptive weight particle swarm algorithm and the interior point method is provided, the adaptive weight particle swarm algorithm is used for iteration first, the individual value and the optimum value of each iteration are recorded, the individual value is used as the initial value of the interior point method to solve, the accurate optimum solution corresponding to the initial value is obtained, finally, all the iterated solutions are recorded, and the minimum value and the corresponding individual value are taken out. Thereby solving the optimal solution of the power distribution network planning.
The specific solving algorithm is shown in fig. 3, and includes the following steps:
s1, inputting wind speed, illumination intensity, load and electric vehicle charging load, and generating a typical operation scene through scene clustering;
s2, initializing self-adaptive weight particle swarm parameters, and generating an initial random population;
s3, carrying out load flow calculation and judging whether constraint conditions are met;
s4, calculating and storing an objective function value f1 and a corresponding fitness value;
s5, performing speed updating, position updating and weight updating by using a particle swarm algorithm;
s6, calculating and storing a target function value f2 by taking the new position of the particle swarm algorithm as an interior point method output value;
s7, judging whether the maximum iteration number is reached;
s8, if the maximum iteration number is reached, finding the maximum objective function value max (f1, f2) and the corresponding decision variable; if the maximum number of iterations has not been reached, the process returns to step S3.
Compared with the prior art, the invention has the following advantages:
1. the comprehensive performance indexes of the system are set, the power grid can set index weights according to own preference, and the model can work out the optimal plan corresponding to the preference.
2. Compared with other distributed power supply planning models considering electric vehicles, the distributed power supply permeability (IEVP) considering the electric vehicle load is provided, the IEVP can represent the social cost, and a suitable planning scheme is found out through optimizing the permeability.
3. The multi-objective programming is simplified by utilizing the fuzzy idea and converted into a single objective function, the algorithm is simple and easy to operate after the conversion, and the simplification process is more perfect compared with other method theories.
4. A hybrid search strategy combining the self-adaptive weight particle swarm algorithm and the interior point method is provided, the iteration times can be shortened through the alternate operation of the two algorithms, and the obtained result has better convergence compared with the common heuristic algorithm.
In the embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units may be a logical division, and in actual implementation, there may be another division, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.

Claims (3)

1. A distributed power supply planning method considering influence of charging load of an electric automobile is characterized by comprising the following steps:
step 1, establishing a mathematical model of a distributed power supply and a load; the mathematical model of the distributed power supply considers the randomness of wind power and photovoltaic power, and comprises a wind power output random model and a photovoltaic output random model; the load mathematical model comprises a common load random model and an electric vehicle charging load random model;
step 2, sampling the wind speed, the illumination intensity and the required load by using a state duration sampling technology according to the characteristics of the distributed power supply and the load;
step 3, carrying out scene clustering and reduction on wind power output, photovoltaic output, common load required power and electric vehicle charging load value by using a hierarchical clustering method;
step 4, determining and calculating the power supply permeability IEVP of the electric automobile according to the definition of the permeability of the distributed power supply, and regarding the electric automobile load with uncertainty as a power supply capable of absorbing power from a power grid;
step 5, taking the network loss, the reactive loss, the line load index and the voltage deviation as indexes for evaluating the network performance of the system, and weighting and adding the four indexes to obtain a comprehensive performance index of the system network;
step 6, constructing a multi-target distributed power supply planning model according to the minimum comprehensive performance index of the system network and the maximum IEVP;
step 7, constructing fuzzy membership functions for the two objective functions, converting the fuzzy membership functions into a single objective problem, and characterizing an optimization result by a total satisfaction degree;
step 8, solving the single-target problem by adopting a hybrid search strategy combining a self-adaptive weight particle swarm algorithm and an interior point method to obtain an optimal solution of the power distribution network planning;
wherein, the wind power output stochastic model comprises:
by adopting a Weber distribution model, the functional relation between the output of the fan and the wind speed is as follows:
Figure FDA0003493097880000011
wherein, PWTFor fan output, PSRated power of the fan, v is wind speed, vin、vs、voutRespectively the cut-in wind speed, the rated wind speed and the cut-out wind speed;
the wind speed probability density function is:
Figure FDA0003493097880000012
wherein k and c are Weber shape parameters and dimensions;
the photovoltaic output stochastic model comprises:
the illumination distribution adopts a Beta distribution model, and the relationship between the output power and the illumination intensity is as follows:
Figure FDA0003493097880000021
wherein, PPVFor output power, G is actual light intensity, PqRated power for photovoltaic, GqIs the rated illumination intensity;
the illumination intensity probability density function is:
Figure FDA0003493097880000022
wherein Γ (—) represents a gamma function; gmaxMaximum illumination intensity; alpha and Beta are shape parameters of Beta distribution;
the common load stochastic model comprises:
the normal load satisfies normal distribution, and the probability density function is:
Figure FDA0003493097880000023
Figure FDA0003493097880000024
wherein, PlAnd QlRespectively representing active load and reactive load; mu.sPAnd σPRespectively an expected value and a standard deviation of the active load; mu.sQAnd σQRespectively an expected value and a standard deviation of the reactive load;
the electric vehicle charging load stochastic model comprises:
the daily driving mileage d of the electric automobile user approximately follows lognormal distribution, and the probability density function and the driving expected value are as follows:
Figure FDA0003493097880000025
Figure FDA0003493097880000026
wherein, f (d) is a probability density function of the daily driving mileage d of the electric vehicle user, and E (D) is a driving expected value; mu.sdAnd σdThe expected value and standard deviation of the daily driving mileage d;
the moment of starting charging follows normal distribution, and the probability density function is as follows:
Figure FDA0003493097880000031
wherein x represents the time when the electric automobile starts to charge; (x) is a probability density function of the moment of starting charging; mu.saAnd σaThe expected value and the standard deviation of the charging starting time are obtained;
the charging duration of the electric automobile follows normal distribution, and the probability density function is as follows:
Figure FDA0003493097880000032
wherein t represents the charging time of the electric automobile; f (t) is a probability density function of the charging time of the electric automobile; mu.stAnd σtThe expected value and the standard deviation of the charging time are obtained;
the state duration sampling technique includes: according to the time sequence characteristic, a cycle period is appointed, each state is calculated, and required index data are obtained through a statistical rule, wherein the index data are as follows:
Figure FDA0003493097880000033
wherein n is the cycle number; m is the value of the sample in a single period, xtIs a state variable at the time t; f (x)t) As a function of the index versus the state variable,
Figure FDA0003493097880000034
is index data;
for the mathematical model in step 1, the state variables are:
Figure FDA0003493097880000035
wherein R isbeta(alpha, Beta) is a Beta random number with parameters of alpha, Beta; rWeibull(k, c) are weber random numbers with the parameters k, c; rnormal(mu, sigma) is a normal distribution random number with the parameters of mu and sigma; PV is photovoltaic output, WG is wind power output;
the scene clustering includes:
clustering the generated scenes by using a hierarchical clustering method, wherein the probability of the scenes contained in a clustering center is typical scene data, an evaluation index is the distance between a class and a new class, an elbow rule is used for determining a polymerization coefficient, and the optimal clustering number is found, wherein the formula is as follows:
Figure FDA0003493097880000041
wherein J is the aggregation coefficient, K is the number of clusters, CkDenotes the kth class, ukRepresenting the position of the center of gravity, x, of the classiIs the ith sample position;
the IEVP is:
Figure FDA0003493097880000042
wherein eta issIs IEVP; eta is the permeability of the distributed power supply; etaEVThe electric vehicle load permeability; alpha is alphai、βiThe node is a 0-1 decision variable, a distributed power supply or an electric automobile exists in the node and is 1, otherwise, the node is 0; pDGiInstalled capacity, P, of the ith distributed power supplyEViCharging power for the node electric vehicle; pLmaxThe system maximum load is defined, and n is the number of system nodes;
the method for constructing the fuzzy membership function for the two objective functions and converting the fuzzy membership function into the single-objective problem comprises the following steps:
constructing corresponding fuzzy membership functions for the two objective functions, and expressing the optimal degree of each objective function, wherein the value of the membership is closer to 1, which means that the objective function is closer to the optimal, and the fuzzy membership functions are as follows:
Figure FDA0003493097880000043
and solving the minimum value of the fuzzy membership function of the two objective functions to maximize the minimum value of the two objective functions so as to obtain the total satisfaction degree: max ρ min { ρ ═1，ρ2}；
Wherein i is 1, 2; rhoiThe fuzzy membership degree corresponding to the ith objective function; f. ofiIs the value of the ith sub-target, fimaxIs the ith target function upper limit value; f. ofibestAn optimum value optimized individually for the ith objective function;
the method for solving the single-target problem by adopting the hybrid search strategy combining the self-adaptive weight particle swarm algorithm and the interior point method to obtain the optimal solution of the power distribution network planning comprises the following steps:
performing iteration by using a self-adaptive weight particle swarm algorithm, and recording an individual value and an optimal value of each iteration;
solving by taking the individual value as an initial value of an interior point method to obtain an accurate optimal solution corresponding to the initial value;
and recording all iterative solutions, and taking out the minimum value and the corresponding individual value so as to obtain the optimal solution of the power distribution network planning.
2. The method of claim 1,
the network loss index is as follows:
Figure FDA0003493097880000051
wherein R isPlossIs a loss index; pDGlossCalculating the loss power on the line for the distributed power supply and the electric automobile load flow; plossCalculating the loss power on the line for the load flow without the distributed power supply and the electric automobile;
the reactive loss index is:
Figure FDA0003493097880000052
wherein R isQlossIs a reactive loss index; qDGlossCalculating the reactive power lost on the line for the distributed power supply and the electric automobile load flow; qlossCalculating the reactive power lost on the line for the power flow without the distributed power supply and the electric automobile;
the line load index is:
Figure FDA0003493097880000053
wherein R isLIs a line load index; n is the number of lines; pLiLoading the line; pSiThe capacity of the line;
the voltage deviation index is:
Figure FDA0003493097880000054
wherein R isVIs a voltage deviation index; v1Is the substation node voltage; viIs the ith node voltage deviation;
the comprehensive performance indexes of the system network are as follows: r ═ xi1RPloss+ξ2RQloss+ξ3RL+ξ4RV；
Wherein ξ1，ξ2，ξ3，ξ4And the weight of each index is determined by the preference of the power grid company.
3. The method of claim 2, wherein the constraints in the multi-objective distributed power supply planning model comprise: the method comprises the following steps of (1) system operation power flow constraint, voltage deviation constraint, line load constraint, distributed power supply permeability constraint and electric vehicle charging load permeability constraint;
wherein the system operation power flow constraint is as follows:
Figure FDA0003493097880000061
wherein k represents a scene; i represents a node, Ui,k、Uj,kThe voltage amplitudes, P, of the kth scene nodes i and j, respectivelyi,k、Qi,kRespectively the active power and the reactive power of the ith node of the kth scene; n is the total number of nodes; gij,k、Bij,k、δij，kAdmittance and phase angle difference between the kth scene nodes i and j respectively;
the voltage deviation constraint is:
|V1-Vj|≤ΔVmax；
wherein, VjIs the voltage deviation of the j-th node, Δ VmaxIs the maximum voltage deviation allowed;
the line load constraints are:
PLi≤PSi；
the distributed power supply permeability constraint is as follows:
η≤ηmax；
wherein eta ismaxThe maximum permeability of the distributed power supply is obtained;
the electric automobile charging load permeability constraint is as follows:
ηEV≤ηEVmax；
wherein eta isEVmaxAnd (4) the maximum permeability of the charging load of the electric automobile.
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