CN115189415A - Multi-objective optimization scheduling method for active power distribution network containing electric automobile aggregator - Google Patents
Multi-objective optimization scheduling method for active power distribution network containing electric automobile aggregator Download PDFInfo
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- H—ELECTRICITY
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- 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
- H02J3/003—Load forecast, e.g. methods or systems for forecasting future load demand
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- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
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- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
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- H—ELECTRICITY
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- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/004—Generation forecast, e.g. methods or systems for forecasting future energy generation
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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
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
- H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
- H02J3/322—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means the battery being on-board an electric or hybrid vehicle, e.g. vehicle to grid arrangements [V2G], power aggregation, use of the battery for network load balancing, coordinated or cooperative battery charging
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
- H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
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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
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
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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
- H02J2300/24—The renewable source being solar energy of photovoltaic origin
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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
- H02J2300/28—The renewable source being wind 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
- H02J2300/40—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation wherein a plurality of decentralised, dispersed or local energy generation technologies are operated simultaneously
Abstract
The invention discloses a multi-objective optimization scheduling method for an active power distribution network with an electric vehicle aggregator, which is characterized by carrying out day-ahead prediction on charging and discharging periods of each electric vehicle based on historical data of charging and discharging periods of the electric vehicle, establishing a target function for minimizing the power generation cost of a thermal power generating unit and the charging cost of the electric vehicle aggregator, establishing a constrained multi-objective nonlinear programming model with 0-1 programming in a high-dimensional space aiming at the problem of multi-objective optimization scheduling of the active power distribution network with the electric vehicle aggregator, providing that only cross operation is applied to a non-dominated solution set individual in a multi-objective optimization scheduling algorithm, and improving the accuracy of a solved multi-objective optimization solution set. The final compromise solution solved by the invention can improve the new energy output consumption capability and maximize the V2G benefit of the electric vehicle users.
Description
Technical Field
The invention relates to the field of active power distribution network optimization scheduling, in particular to an active power distribution network multi-objective optimization scheduling method with an electric automobile aggregator.
Background
With the continuous increase of the holding capacity of electric automobiles at home and abroad in recent years, in order to further improve the consumption capacity of the active power distribution network on new energy, the V2G technology enables the electric automobiles to become potential high-capacity energy storage equipment, and has an important role in relieving the output fluctuation of the new energy and improving the stability of the power distribution network. However, the power level of a single electric automobile is low, and the requirement of high-capacity energy storage is difficult to meet, so that the concept of the electric automobile aggregator comes up, a large number of electric automobiles are aggregated through the charge and discharge prices of considerable electric automobiles to be connected in time according to the volume, the energy storage with larger capacity can be formed, the frequency modulation and peak shaving of a power grid are realized, and the profit of electric automobile users can be increased on the basis of reducing the economic cost of power generation of a traditional unit by performing multi-objective optimization scheduling on an active power distribution network containing the electric automobile aggregator.
At present, the multi-objective optimization scheduling problem of an active power distribution network containing an electric automobile aggregator belongs to a constrained multi-objective nonlinear programming problem of mixed 0-1 programming in a high-dimensional space, in the existing literature and patent published technology, weights are usually set artificially for objective functions, the multi-objective optimization problem is simplified into a single-objective optimization problem to be processed, a compromise optimal solution which can simultaneously meet multiple optimization objectives is difficult to obtain, and maximization of benefits of power grid enterprises and electric automobile users is not facilitated to be achieved simultaneously.
Disclosure of Invention
The invention aims to: aiming at the defects in the prior art, the invention aims to improve the solving precision of the multi-objective optimization problem of the active power distribution network with the electric automobile aggregator, and discloses a multi-objective optimization scheduling method for the active power distribution network with the electric automobile aggregator.
The technical scheme is as follows: the invention discloses a multi-objective optimization scheduling method for an active power distribution network with an electric automobile aggregator, which comprises the following steps:
a) Predicting the day-ahead hour level of the generated power of the wind power plant based on historical wind power generation data;
b) Setting the charging starting time of the electric vehicles to meet Weibull distribution, estimating the charging and discharging time periods of each electric vehicle according to the historical data of the state of charge (SOC) of the electric vehicles, and predicting the schedulable discharging power of the regional electric vehicle aggregator and the schedulable charging load power range at the current small level;
c) Predicting the generation power of a photovoltaic power station day ahead based on historical photovoltaic power generation data; initializing the maximum iteration times, population quantity, inertia weight and learning rate of multi-objective optimization scheduling, initializing individual positions, and estimating the output P of the corresponding thermal power generating unit at each moment Gi t Electric automobile aggregator output upper limit P EVc，max t、P EVd，max t Upper limit of wind power and photovoltaic output P wmax t And P pmax t ，t∈[1，24]；
d) Calculating a power generation cost objective function of a minimum thermal power generating unit, a charging cost objective function of a minimum electric automobile aggregator, the thermal power generating unit, wind power, photovoltaic power generation output upper limit constraint, a thermal power generating unit starting time constraint, a power generation and load power balance constraint estimation, a scheduling discharge power and charging power upper limit constraint of the electric automobile aggregator, counting the number of each individual in a population violating the constraints, and determining a population leader individual based on a wheel disc algorithm;
e) Updating the individual positions, and estimating the power generation power of the thermal power generating unit, the electric automobile aggregator and the new energy source to be scheduled;
f) Calculating two objective function values according to the step d), calculating the number of violations of the constraint values of each individual, and determining a Pareto domination relationship of each individual;
g) Storing a non-dominated solution set, and applying cross operation to individuals in the non-dominated solution set;
h) Randomly selecting 70% of individuals in the whole population to perform mutation operation;
i) Determining cross operation and mutation operation to obtain individual Pareto domination relation, storing a non-domination solution set, and determining group leader individuals based on a roulette algorithm;
j) And f), judging whether the maximum iteration times is reached, if so, ending the multi-target optimization scheduling calculation process, and if not, returning to the step f).
Further, the step b) that the electric vehicle charging starting time satisfies Weibull distribution is specifically:
estimating a coefficient k based on historical charging and discharging period data of the regional electric vehicle t 、c t 。
Further, the step d) specifically comprises:
calculating an objective function for minimizing the power generation cost of the thermal power generating unit:
wherein, C i t Generating cost for the ith thermal power generating unit at the t moment and generating power P for the ith thermal power generating unit Gi t Function of (A), I i t For the ith thermal power generating unit in the operation state at the t moment S i t For the start-stop cost of thermal power generating units, P wa t And P pa t Respectively the wind abandoning power and the light abandoning power k at the t moment w And k p Punishment coefficients of wind abandonment and light abandonment;
calculating a charge cost objective function of the electric automobile aggregator:
P c t and P d t Charging and discharging power mu of the electric vehicle in the electric vehicle aggregator at the t moment c And mu d Charging and discharging electricity price, k, for electric vehicles de The cost is lost for the service life of the battery of the electric automobile;
calculating the upper limit constraints of the output of the thermal power generating unit, the wind power generation and the photovoltaic power generation:
wherein, P Gimin And P Gimax Respectively the minimum output and the maximum output of the thermal power generating unit;
calculating the starting time length constraint of the thermal power generating unit:
T i，on，max ≥T i，on
T i，on，max the maximum starting time length T of the ith thermal power generating unit i，on The actual starting time length of the ith thermal power generating unit is obtained;
estimating power generation and load power balance constraint:
P EVd t and P EVc t Discharging and charging power P of electric automobile aggregator at t moment load t Load power at the t-th moment;
estimating the upper limit constraints of the scheduled discharge power and the scheduled charge power of the electric automobile aggregator:
and counting the number of the violations of the constraints of each individual in the population, and determining the group leader individual based on a roulette algorithm.
Further, the crossing operation in the step g) specifically comprises:
wherein, pos 1 And Pos 2 Respectively parental individual position, pos 1，cross And Pos 2，cross Respectively are the positions of filial generation individuals after the cross operation, and alpha is [0, 1')]To a random number.
Further, the mutation operation in the step h) specifically comprises:
Pos i，mutate ＝Pos i +σ m RN i
wherein, pos i As the ith individual position, pos, in the population i，mutate Is the individual position, σ, after mutation of the individual m For mutation step size, RN i Is a standard normally distributed random number.
Further, determining Pareto dominance relations of the individuals in the step f), wherein minimizing Pareto dominance is defined as, for the individual position vectors x and y, if for any objective function f i Satisfies the following conditions:
the individual position vector x dominates y, i.e.:
F(x)＜F(x)
f (x), F (y) are the corresponding target vectors.
Further, the method for updating the individual location in step e) comprises:
wherein, vel i (k) And Pos i (k) Respectively the velocity and position of the ith particle in the kth iteration, w is the particle inertial weight, cl 1 And cl 2 Learning coefficients for individual and group, rd 1 (k) And rd 2 (k) For uniformly distributing random numbers, pos, between 0 and 1 in the k-th iteration i，pbest For the optimum position, pos, of the ith particle individual leader And the optimal position of the population is obtained.
Has the beneficial effects that:
the multi-objective optimization scheduling method for the active power distribution network with the electric automobile aggregator, disclosed by the invention, can simultaneously meet the compromise optimal solution of a plurality of optimization objectives, realize multi-objective optimization and improve the solving precision of the multi-objective optimization problem of the active power distribution network with the electric automobile aggregator. The final compromise solution solved by the invention can improve the new energy output consumption capability and maximize the V2G benefit of the electric vehicle users.
Drawings
FIG. 1 is a flow chart of a multi-objective optimization scheduling method for an active power distribution network with an electric automobile aggregator;
FIG. 2 shows the results of the forecast of day ahead of the estimated active distribution network accessible wind power, photovoltaic, traditional load, and electric vehicle charging load in the method of the present invention;
FIG. 3 shows the multi-objective Pareto frontier and final compromise solutions solved by the method of the present invention.
Detailed Description
In order to make the technical means, the creation characteristics, the achievement purposes and the effects of the invention easy to understand, the invention is further explained by combining the specific embodiments.
The invention discloses a multi-objective optimization scheduling method for an active power distribution network with an electric vehicle aggregator, which takes an area-level power distribution network example with 10 thousands of electric vehicles as an example and comprises the following steps:
a) And predicting the day-ahead hour level of the generated power of the wind power plant based on historical wind power generation data.
b) The charging starting time of the electric automobile is set to meet Weibull distribution:
estimating a coefficient k based on historical charging and discharging period data of the regional electric vehicle t 、c t (ii) a And estimating the charging and discharging time interval of each electric vehicle according to the historical data of the state of charge (SOC) of the electric vehicle, and predicting the schedulable discharging power of the regional electric vehicle aggregator and the schedulable charging load power range in the day-ahead and hour-level manner.
c) Predicting the day-ahead power generation power of the photovoltaic power station based on historical photovoltaic power generation data;
initializing the maximum iteration times, population quantity, inertia weight and learning rate of multi-objective optimization scheduling, initializing individual positions, and estimating the output P of the corresponding thermal power generating unit at each moment Gi t The upper limit of the output of the electric automobile aggregator P EVc，max t、P EVd，max t Upper limit of wind power and photovoltaic output P wmax t And P pmax t ，t∈[1，24]。
For the present embodiment, the results of the forecast of the active power distribution network estimated according to the above steps, which can be accessed to wind power, photovoltaic, traditional load, and electric vehicle charging load, are shown in fig. 2.
d) Calculating an objective function for minimizing the power generation cost of the thermal power generating unit:
wherein, C i t Generating cost for the ith thermal power generating unit at the t moment and generating power P for the ith thermal power generating unit Gi t A function of (a), I i t For the ith thermal power generating unit in the operation state at the t moment S i t For the start-stop cost of thermal power generating units, P wa t And P pa t Respectively the abandoned wind power and abandoned light power at the t moment, k w And k is p Penalty coefficients for abandoning wind and abandoning light.
Calculating a charge cost objective function of the electric automobile aggregator:
P c t and P d t Charging and discharging power mu of the electric automobile in the electric automobile aggregator at the t moment c And mu d Charging and discharging electricity price, k, for electric vehicles de The cost is lost for the service life of the battery of the electric automobile.
Calculating the upper limit constraints of the output of the thermal power generating unit, the wind power generation and the photovoltaic power generation:
wherein, P Gimin And P Gimax The minimum output and the maximum output of the thermal power generating unit are respectively.
Calculating the starting time constraint of the thermal power generating unit:
T i，on，max ≥T i，on
T i，on，max the maximum starting time length T of the ith thermal power generating unit i，on And the actual starting time of the ith thermal power generating unit is the actual starting time of the ith thermal power generating unit.
Estimating power generation and load power balance constraint:
P EVd t and P Evc t Discharging and charging power P of electric automobile aggregator at t moment load t The load power at the t-th moment.
Estimating the upper limit constraints of the scheduled discharge power and the scheduled charge power of the electric automobile aggregator:
and counting the number of each individual in the population violating the constraint, and determining the group leader individual based on a roulette algorithm.
e) Updating the individual position, estimating the power generation power of the thermal power generating unit, the electric automobile aggregator and the new energy source to be scheduled:
the method for updating the individual position comprises the following steps:
wherein Vel i (k) And Pos i (k) Respectively the velocity and position of the ith particle in the kth iteration, w is the particle inertial weight, cl 1 And cl 2 Individual and group learning coefficients, rd 1 (k) And rd 2 (k) Is the kth wheelUniformly distributing random numbers, pos, between 0 and 1 in an iteration i，pbest For the optimum position, pos, of the ith particle individual leader And the optimal position of the population is obtained.
f) Calculating two objective function values according to the formula in the step d), calculating the number of violations of the constraint values of each individual according to the formula in the step d), and determining a Pareto domination relationship of each individual:
determining the Pareto domination relation of each individual, wherein the minimization Pareto domination is defined as that for the individual position vectors x and y, if for any objective function f i Satisfies the following conditions:
the individual position vector x dominates y, i.e.:
F(x)＜F(x)
f (x), F (y) are the corresponding target vectors.
g) Storing a non-dominated solution set, and applying a cross operation to individuals in the non-dominated solution set:
Pos 1 and Pos 2 Respectively parental individual position, pos 1，cross And Pos 2，cross Respectively are the positions of filial generation individuals after the cross operation, and alpha is [0, 1')]Or a random number.
h) Randomly selecting 70% of individuals in all populations to perform mutation operation:
Pos i，mutate ＝Pos i +σ m RN i
Pos i as the ith individual position, pos, in the population i，mutate Is the individual position, σ, after mutation of the individual m For mutation step size, RN i Is a standard normally distributed random number.
i) Determining cross operation and variation operation to obtain individual Pareto domination relation, storing a non-domination solution set, and determining group leader individuals based on a roulette algorithm.
j) And (f) judging whether the maximum iteration number is reached, if so, ending the multi-objective optimization scheduling calculation process, and if not, returning to the step f).
Fig. 3 shows a multi-objective Pareto frontier and a final compromise solution solved by the disclosed multi-objective optimization scheduling method for the active power distribution network with the electric vehicle aggregator. Defining an objective function F 1 、F 2 Normalizing the values, selecting the minimum value of the two values to form a multi-target ideal solution, estimating Euclidean distances from each point of a solved Pareto solution set in the normalized target function space to the ideal solution, and taking the minimum distance solution as a final compromise solution.
The foregoing shows and describes the general principles and features of the present invention, together with the advantages thereof. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (7)
1. A multi-objective optimization scheduling method for an active power distribution network with an electric automobile aggregator is characterized by comprising the following steps:
a) Predicting the day-ahead hour level of the generated power of the wind power plant based on historical wind power generation data;
b) Setting the charging starting time of the electric automobile to meet Weibull distribution, estimating the charging and discharging time period of each electric automobile according to the historical data of the state of charge (SOC) of the electric automobile, and predicting the schedulable discharging power and the schedulable charging load power range of the regional electric automobile aggregator at the day-ahead and hour-level;
c) Predicting the generation power of a photovoltaic power station day ahead based on historical photovoltaic power generation data; initializing the maximum iteration times, population quantity, inertia weight and learning rate of multi-objective optimization scheduling, initializing individual positions, and estimating the output P of the corresponding thermal power generating unit at each moment Gi t Electric automobileUpper limit of polymerization reactor output P Evc，max t、P EVd，max t Upper limit of wind power and photovoltaic output P wmax t And P pmax t ，t∈[1，24]；
d) Calculating a power generation cost objective function of a minimum thermal power generating unit, a charging cost objective function of a minimum electric automobile aggregator, the thermal power generating unit, wind power, photovoltaic power generation output upper limit constraint, a thermal power generating unit starting time constraint, a power generation and load power balance constraint estimation, a scheduling discharge power and charging power upper limit constraint of the electric automobile aggregator, counting the number of each individual in a population violating the constraints, and determining a population leader individual based on a wheel disc algorithm;
e) Updating the individual positions, and estimating the power generation power of the thermal power generating unit, the electric automobile aggregator and the new energy source to be scheduled;
f) Calculating two objective function values according to the step d), calculating the number of violations of each individual constraint value, and determining a Pareto domination relation of each individual;
g) Storing a non-dominated solution set, and applying cross operation to individuals in the non-dominated solution set;
h) Randomly selecting 70% of individuals in all populations to carry out mutation operation;
i) Determining cross operation and mutation operation to obtain individual Pareto domination relation, storing a non-domination solution set, and determining group leader individuals based on a roulette algorithm;
j) And (f) judging whether the maximum iteration number is reached, if so, ending the multi-target optimization scheduling calculation process, and if not, returning to the step f).
2. The multi-objective optimization scheduling method for the active power distribution network comprising the electric vehicle aggregator according to claim 1, wherein the step b) that the electric vehicle charging start time satisfies Weibu11 distribution is specifically:
based on historical charging and discharging period data of regional electric vehicle, estimation systemNumber k t 、 ct 。
3. The multi-objective optimization scheduling method for the active power distribution network with the electric vehicle aggregator as claimed in claim 1, wherein the step d) specifically comprises:
calculating an objective function for minimizing the power generation cost of the thermal power generating unit:
wherein, C i t Generating cost for the ith thermal power generating unit at the t moment and generating power P for the ith thermal power generating unit Gi t Is a function of (a) a function of (b),
for the operation state of the ith thermal power generating unit at the t moment,
for the start-stop cost of thermal power generating units, P wa t And P pa t Respectively the wind abandoning power and the light abandoning power k at the t moment w And k is p Punishment coefficients of wind abandonment and light abandonment;
calculating a charge cost objective function of the electric automobile aggregator:
P c t and P d t Charging and discharging power mu of the electric automobile in the electric automobile aggregator at the t moment c And mu d Charging and discharging electricity price, k, for electric vehicles de The cost is lost for the service life of the battery of the electric automobile;
calculating the output upper limit constraints of the thermal power generating unit, the wind power generation unit and the photovoltaic power generation unit:
wherein, P Gimin And P Gimax Respectively the minimum output and the maximum output of the thermal power generating unit;
calculating the starting time constraint of the thermal power generating unit:
T i，on，max ≥T i，on
T i，on，max the maximum starting time length T of the ith thermal power generating unit i，on The actual starting time length of the ith thermal power generating unit is obtained;
estimating power generation and load power balance constraint:
P EVd t and P EVc t Discharging and charging power, P, of the electric vehicle aggregator at the t moment load t Load power at the t moment;
estimating the upper limit constraints of the scheduled discharge power and the scheduled charge power of the electric automobile aggregator:
and counting the number of the violations of the constraints of each individual in the population, and determining the group leader individual based on a roulette algorithm.
4. The multi-objective optimization scheduling method for the active power distribution network with the electric vehicle aggregator as claimed in claim 1, wherein the step g) of performing the cross operation specifically comprises the following steps:
wherein, pos 1 And Pos 2 Respectively parental individual position, pos 1，cross And Pos 2，cross Respectively are the positions of filial generation individuals after the cross operation, and alpha is [0, 1')]To a random number.
5. The multi-objective optimization scheduling method for the active power distribution network comprising the electric vehicle aggregator according to claim 1, wherein the mutation operation in the step h) is specifically:
Pos i，mutate ＝Pos i +σ m RN i
wherein, pos i Is the ith individual position, pos, in the population i，mutate Is the individual position, σ, after mutation of the individual m For mutation step size, RN i Is a standard normally distributed random number.
6. The multi-objective optimization scheduling method for the active power distribution network with the electric vehicle aggregator as claimed in claim 1, wherein the Pareto domination relationship of each individual is determined in step f), wherein the minimized Pareto domination is defined as for the individual position vectors x and y, if for any objective function f i Satisfies the following conditions:
then the individual position vector x dominates y, i.e.:
F(x)＜F(x)
f (x), F (y) are the corresponding target vectors.
7. The multi-objective optimization scheduling method for the active power distribution network with the electric vehicle aggregator as claimed in claim 1, wherein the method for updating individual positions in step e) comprises:
wherein, vel i (k) And Pos i (k) Respectively the velocity and position of the ith particle in the kth iteration, w is the particle inertial weight, cl 1 And cl 2 Learning coefficients for individual and group, rd 1 (k) And rd 2 (k) For uniformly distributing random numbers, pos, between 0 and 1 in the k-th iteration i，pbest For the optimal position, pos, of the ith particle individual leader Is the group optimal position.
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