CN111244988B - Electric automobile considering distributed power supply and energy storage optimization scheduling method - Google Patents

Electric automobile considering distributed power supply and energy storage optimization scheduling method Download PDF

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CN111244988B
CN111244988B CN202010012120.9A CN202010012120A CN111244988B CN 111244988 B CN111244988 B CN 111244988B CN 202010012120 A CN202010012120 A CN 202010012120A CN 111244988 B CN111244988 B CN 111244988B
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charging
formula
power
electric automobile
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CN111244988A (en
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刘自发
刘云阳
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North China Electric Power University
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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
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
    • H02J3/14—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
    • 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
    • H02J3/48—Controlling the sharing of the in-phase component
    • 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
    • Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B70/00—Technologies for an efficient end-user side electric power management and consumption
    • Y02B70/30—Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
    • Y02B70/3225—Demand response systems, e.g. load shedding, peak shaving
    • 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
    • Y02T10/10—Internal combustion engine [ICE] based vehicles
    • Y02T10/40—Engine management systems
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02T90/10—Technologies relating to charging of electric vehicles
    • Y02T90/16—Information or communication technologies improving the operation of electric vehicles
    • Y02T90/167—Systems integrating technologies related to power network operation and communication or information technologies for supporting the interoperability of electric or hybrid vehicles, i.e. smartgrids as interface for battery charging of electric vehicles [EV] or hybrid vehicles [HEV]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
    • Y04S20/20—End-user application control systems
    • Y04S20/222—Demand response systems, e.g. load shedding, peak shaving
    • 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
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S30/00—Systems supporting specific end-user applications in the sector of transportation
    • Y04S30/10—Systems supporting the interoperability of electric or hybrid vehicles
    • Y04S30/12—Remote or cooperative charging
    • 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
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S30/00—Systems supporting specific end-user applications in the sector of transportation
    • Y04S30/10—Systems supporting the interoperability of electric or hybrid vehicles
    • Y04S30/14—Details associated with the interoperability, e.g. vehicle recognition, authentication, identification or billing

Abstract

An electric vehicle and energy storage device optimal scheduling method considering that a distributed power supply is connected into a distribution network comprises the following steps: predicting the output of various distributed power supplies in one day according to the probability distribution curves of the wind speed and the illumination environment variables; constructing a probability distribution model of the daily driving mileage and the charging time interval of the electric automobile, and sampling the daily driving mileage and the charging time interval of the electric automobile to obtain the load condition of the electric automobile in one day; constructing a schedulable and non-schedulable type conversion model of the electric automobile, and acquiring the number of schedulable electric automobiles; and constructing a charge-discharge model and an objective function of the energy storage and schedulable electric automobile, establishing an optimized scheduling model comprising constraint conditions, and performing collaborative optimized scheduling on the schedulable electric automobile and the energy storage device. The power distribution network operated according to the method has higher operation efficiency, is more economic, safe and reliable as a whole, and has important practical significance for the sustainable development of the power grid.

Description

Electric automobile considering distributed power supply and energy storage optimization scheduling method
Technical Field
The invention relates to a scheduling method of an electric automobile and an energy storage device, in particular to an all-day optimal scheduling method of the electric automobile and the energy storage device after a distributed power supply is considered to be connected into a power distribution network.
Background
With the rapid development of renewable energy power generation, due to the considerable environmental benefits, more and more renewable energy-based distributed power sources are connected to a power distribution network. On the other hand, due to the environmental protection of electric vehicles, more and more electric vehicles are connected to the power distribution network in order to replace the original fuel vehicles. However, due to the randomness of renewable energy and electric vehicle loads, an energy storage device is needed to regulate power, so that the system operates more smoothly. The electric automobile and energy storage device optimal scheduling method considering the fact that the distributed power supply is connected into the distribution network is researched, and the method has important significance for safe and stable operation of a power system.
When a large number of distributed power sources and electric vehicles are connected to a power distribution network, power fluctuation is large, randomness and instability exist, from the viewpoint of electric quantity balance, the connection of the distributed power sources and the electric vehicles is a great challenge for a power system, and a common method for solving the challenge is to add an energy storage device, so that the effect of adjusting smooth power in real time is achieved. However, in the existing research, coordination between the electric vehicle and the energy storage device or coordination between the electric vehicle and the distributed power supply is generally adopted, and an optimal scheduling method when the three are considered comprehensively is not adopted.
Therefore, how to consider a cooperative optimization scheduling method of the electric vehicle and the energy storage device under the condition that the distributed power supply is connected to the distribution network makes the power grid safer, more reliable and more environment-friendly, and how to obtain higher efficiency becomes a technical problem to be solved urgently in the prior art.
Disclosure of Invention
The invention aims to provide an electric vehicle and energy storage optimization scheduling method considering a distributed power supply, which obtains an optimization scheduling scheme of schedulable electric vehicles and energy storage by taking power balance and voltage and current safety stability as constraint conditions and an optimization scheduling model and an optimization scheduling strategy of the electric vehicle and the energy storage device considering the distributed power supply. According to the power distribution network operated by the scheme, the operation efficiency is higher, the operation is safer and more reliable, and the method has important practical significance for the sustainable development of the power distribution network.
An electric vehicle and energy storage device optimal scheduling method considering that a distributed power supply is connected into a distribution network is characterized by comprising the following steps:
distributed power output prediction step S110: predicting the output of various distributed power supplies in one day according to the probability distribution curves of the wind speed and the illumination environment variables;
electric vehicle load prediction step S120: constructing a probability distribution model of the daily driving mileage and the charging time interval of the electric automobile, and sampling the daily driving mileage and the charging time interval of the electric automobile to obtain the load condition of the electric automobile in one day;
an electric vehicle conversion model establishing step S130, wherein a schedulable and non-schedulable type conversion model of the electric vehicle is established according to the real-time electricity price, and the schedulable electric vehicle number is obtained;
a charging and discharging model building step S140, building a charging and discharging model of the energy storage and schedulable electric automobile according to the parameters of the energy storage device and the schedulable electric automobile;
and a cooperative optimization scheduling step S150, constructing an optimization scheduling model taking the minimum total cost including the charge-discharge operation cost, the charge-discharge electric quantity cost, the daily loss cost, the dead zone penalty cost, the network loss cost and the voltage deviation cost of the energy storage and schedulable electric vehicle as a target function and taking the power balance and the voltage and current safety and stability constraint as constraint conditions, and performing cooperative optimization scheduling on the schedulable electric vehicle and the energy storage device by adopting a particle swarm algorithm improved based on an optimization scheduling strategy to obtain an operation scheduling plan in one day.
The optimal scheduling method aims at maximizing efficiency, takes the minimum total cost as an exemplary expression, takes power balance and voltage and current safety stability as constraint conditions, considers the optimal scheduling model and the optimal scheduling strategy of the electric automobile and the energy storage device of the distributed power supply, and obtains the optimal scheduling scheme of the schedulable electric automobile and the energy storage. According to the power distribution network operated by the scheme, the operation efficiency is higher, the whole power distribution network is more economic, safe and reliable, and the power distribution network has important practical significance for the sustainable development of the power grid.
Drawings
Fig. 1 is a flowchart of an electric vehicle and energy storage optimization scheduling method after considering that a distributed power source accesses a distribution network according to an embodiment of the present invention;
FIG. 2 is a schematic illustration of a probability density curve sampling according to an embodiment of the present invention;
FIG. 3 is a probability distribution diagram of charging periods of an electric vehicle according to an embodiment of the present invention;
fig. 4 is a diagram of an IEEE33 node grid structure according to an embodiment of the present invention;
FIG. 5 is a four season load curve according to an embodiment of the present invention;
FIG. 6 is a 24-hour electricity rate change curve according to an embodiment of the present invention;
FIG. 7 is a wind speed profile according to an embodiment of the present invention;
FIG. 8 is a graph of illumination intensity variation according to an embodiment of the present invention;
FIG. 9 is an electric vehicle load curve according to an embodiment of the present invention;
FIGS. 10 (a) - (d) are graphs of SOC variation of various types of electric vehicles and energy storage in four seasons according to an embodiment of the present invention;
fig. 11 (a) - (d) are comparison of total load curves before and after adjustment of the electric vehicle with energy storage and scheduling according to the embodiment of the invention in four seasons.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not to be construed as limiting the invention. It should be further noted that, for the convenience of description, only some structures related to the present invention are shown in the drawings, not all of them.
Referring to fig. 1, a flowchart of an electric vehicle and energy storage device optimized scheduling method after considering that a distributed power source accesses a distribution network according to the present invention is shown, which includes the following steps:
distributed power supply output prediction step S110: and predicting the output of various distributed power supplies in one day according to the probability distribution curves of the wind speed and the illumination environment variables.
Specifically, the distributed power supply comprises a fan and photovoltaic power generation, the wind speed is divided into 4 seasons according to the change of the wind speed and the illumination intensity along with the time, 96 groups of probability density curves are obtained at 24 moments every season, the illumination intensity is divided into 12 groups of illumination intensity curves, and each group corresponds to a curve of the typical daily illumination intensity changing along with the time within one month. And respectively sampling the wind speed and the illumination intensity, and randomly selecting the wind speed and the illumination intensity so as to further determine the output of the fan and the photovoltaic.
Referring to fig. 2, a sampling method is illustratively shown, comprising:
a. a random number r is randomly generated in the range of 0, 1. The probability density curve corresponding to a total area of 1 is the extracted value starting from the origin and going to the point where the area of the red region is r.
b. Using a momentThe method of area equivalence looks for the boundary point when the area is r. And dividing an area enclosed by the probability density curve and the abscissa into a series of small rectangles, wherein the height of each small rectangle is a probability density function value corresponding to the right boundary abscissa of the rectangle. By successively accumulating the areas of the small rectangles from left to right until the area sum S + R or more, recording the left and right boundaries x of the last rectangle - And x + And the sum S of the areas of all previous rectangles _ 。
c. According to the method using the linear difference, a point x on the abscissa corresponding to the area r is found as shown in the following formula.
Figure BDA0002356972880000051
Further, sampling is carried out on the wind speed and the illumination intensity to obtain the wind speed v and the wind speed R corresponding to the time c . The fan output model adopts a Weibull model, and the model is shown as the following formula.
Figure BDA0002356972880000052
In the formula P WT The active power output of the fan is obtained; p r-WT Outputting rated active power of the fan; v. of ci The cut-in wind speed of the fan; v. of r Is the rated wind speed of the fan; v. of co Is the cut-out wind speed of the fan.
The photovoltaic output model adopts Beta distribution as shown in the following formula.
Figure BDA0002356972880000053
In the formula, P PV Is the active power output of the photovoltaic; n is a radical of an alkyl radical pv Is the number of photovoltaic cells; p r-PV Is the rated active power output of a photovoltaic unit; r r Is the nominal light intensity; k is the power temperature coefficient; t is a unit of c And T r Actual temperature and standard temperature.
Electric vehicle load prediction step S120: and constructing a probability distribution model of the daily driving mileage and the charging time interval of the electric automobile, and sampling the daily driving mileage and the charging time interval of the electric automobile to obtain the load condition of the electric automobile in one day.
Specifically, the non-dispatchable electric vehicle is only used for daily trips, and the consumed electric quantity is related to daily driving mileage. The daily driving mileage obeys normal distribution, and different types of electric automobiles obey different normal distribution.
The types of the electric automobiles are divided into three types, namely buses, taxies and private cars, and the probability distribution of daily driving mileage is shown as the following formula.
Figure BDA0002356972880000061
In the formula D EV Is the daily mileage of the electric vehicle; μ and σ are the mean and standard deviation of the normal distribution;
according to the formula (4), the daily driving mileage of each electric vehicle is sampled to obtain the daily driving mileage of the electric vehicle, the electric quantity consumed by the electric vehicle each day is calculated according to the formula (5), and the energy storage State (State of charge, SOC) of the electric vehicle is further calculated, wherein the calculation formula is shown as (6).
Figure BDA0002356972880000062
Figure BDA0002356972880000063
In the formula, E EV-consumption Is the electric automobile consumes power every day D EVmax Is the maximum number of miles driven, C EV Is the battery capacity, SOC of the electric vehicle EV Is that the electric automobile runs D EV The SOC remaining after the last time is,
according to daily consumed electric quantity E EV-consumption Calculating daily needsTime T of charging EV-charge The calculation formula is shown in the following formula.
Figure BDA0002356972880000064
In the formula, T EV-charge Is the total charging time required by the electric vehicle during the day; p is EV-charging Is the charging power of the electric automobile.
The randomness of the charging period of the electric vehicle is an important characteristic of the electric vehicle load. The charging periods of different kinds of electric vehicles have different distributions. The probability distribution of the charging periods of different types of electric vehicles adopted by the invention is shown in figure 3.
According to the charging period probabilities of different types of electric automobiles, sampling the charging initial time of each electric automobile, and finally obtaining the charging initial time t of each electric automobile EV-start . Assuming that the electric vehicle is continuously charged in the charge distribution period, the calculation method of the charge period may be calculated by the following steps.
1) First time from t EV-start Starting, and starting from the current time later. Judging whether the next moment is in the charging distribution time period, if so, adding 1 to the charged time; if not, the next charging time is determined as the starting time of the next charging distribution period, and the charged time is added by 1. And recording the moment as the charging moment, and updating the current moment to the value of the next moment.
2) Judging whether the charged time reaches the required total charging time T EV-charge If so, ending the searching for the charging time period to obtain the charging time period of the electric automobile; if the total time is not reached, return to 1), continue to look for a charging period.
Through the steps, the charging time interval of each electric automobile can be obtained, and the charging power P of the electric automobile is combined EV-charging The charging load time distribution of the electric vehicle can be obtained.
Step S130 of establishing an electric vehicle conversion model, namely establishing schedulable and non-schedulable electric vehicles according to the real-time electricity priceThe model is converted into a schedulable type to obtain the schedulable number N of the electric vehicles EV-d 。
The owner of the electric automobile can select to access the electric automobile of the owner into a power grid as a schedulable device for flexible scheduling of the system. The important factor influencing this decision is the peak-to-valley valence difference. Typically, the system will allow the energy storage device to discharge during peak load periods and to charge during valley load periods. The magnitude of the peak-to-valley electricity price difference directly determines the profit that the user can obtain.
Therefore, the conversion model of the dispatchable type and the non-dispatchable type of the electric automobile is as formula (8),
Figure BDA0002356972880000081
in the formula, N EV-d Is converted into the number of schedulable electric vehicles, R (x) is an integer function, k ev-trans Is the conversion coefficient, c udd Is the actual peak to valley electricity price difference, c 0 Is the threshold for the transition.
The thresholds and coefficients for conversion should also be different for different types of electric vehicles due to different conversion costs. Specifically, after the electric vehicle of this type is converted into a schedulable device, there is a series of costs caused by the fact that the electric vehicle cannot be used as a tool for traveling.
And a charging and discharging model building step S140, building a charging and discharging model of the energy storage and schedulable electric automobile according to the parameters of the energy storage device and the schedulable electric automobile.
The energy storage device and the schedulable electric automobile are used as schedulable energy management devices and play an important role in improving network trend. The function of the schedulable electric automobile is the same as that of energy storage, and the schedulable electric automobile can be regarded as a battery with larger capacity. The present invention uses the same model to describe them. The mathematical model of which can be represented by the following equation.
The charge energy balance equation, namely the equation (9), and the discharge energy balance equation, namely the equation (10) are constructed by adopting the charge and discharge loss coefficients,
E es (t)＝E es (t-Δt)+ηP es-c Δt (9)
Figure BDA0002356972880000082
in the formula, E es (t) is the energy stored by the energy storage device at time t, η is the charge-discharge loss coefficient of the energy storage device, P es-c Is the charging power, P, of the energy storage device es-f Is the discharge power of the energy storage device.
Since the SOC needs to be in a reasonable range for the energy storage device, the life loss of the battery is reduced. In addition, the charging and discharging power of the stored energy is also limited by an upper limit and a lower limit.
Further establishing constraints respectively aiming at the SOC and the charge and discharge of the stored energy,
SOC min ≤SOC≤SOC max (11)
P es-cmin ≤P es-c ≤P es-cmax (12)
P es-fmin ≤P es-f ≤P es-fmax (13)
in the formula, SOC max And SOC min Is the upper and lower limits of the SOC of the energy storage device; p is es-cmax And P es-cmin Is charging power P of energy storage device es-c The upper and lower limits of (c); p is es-fmax And P es-fmin Is the discharge power P of the energy storage device es-f The upper and lower limits of (2).
And a cooperative optimization scheduling step S150, constructing an optimization scheduling model taking the minimum total cost including the charge and discharge operation cost, the charge and discharge electric quantity cost, the daily loss cost, the dead zone penalty cost, the network loss cost and the voltage deviation cost of the energy storage and schedulable electric vehicle as a target function and taking power balance and voltage and current safety and stability constraints as constraint conditions, and performing cooperative optimization scheduling on the schedulable electric vehicle and the energy storage device by adopting a particle swarm algorithm improved based on an optimization scheduling strategy to obtain an operation scheduling plan within one day.
Specifically, the method comprises the following steps:
an objective function calculation substep: the charging and discharging strategy of the energy storage and schedulable automobile in one day is optimized to achieve the highest efficiency, so that the total cost is minimized.
After the battery enters the dead zone, the battery can not carry out bidirectional electric quantity exchange, so that the effect of regulating the tide by the battery is reduced. The invention also considers the punishment cost after the charging and discharging of the battery enter the dead zone. In order to examine the superiority of the charging and discharging strategy, the total network loss cost and the voltage deviation cost of the whole day are introduced.
In conclusion, the total cost includes the cost of the number of times of charging and discharging operations of the energy storage and schedulable electric vehicle, the cost of the charging and discharging electricity quantity, the loss cost when the electric vehicle does not work, the punishment cost after the battery is charged and discharged into the dead zone, the total network loss cost all day long and the voltage deviation cost.
Figure BDA0002356972880000101
Figure BDA0002356972880000102
Figure BDA0002356972880000103
Figure BDA0002356972880000104
Figure BDA0002356972880000105
Figure BDA0002356972880000106
Figure BDA0002356972880000107
The total objective function is represented by equation (14), and equation (15) represents the charge/discharge operation frequency cost
Figure BDA0002356972880000108
The calculation formula of (2) has an upper limit on the number of battery charging and discharging, so that the cost is generated every charging and discharging. Equation (16) is the charge-discharge electricity cost
Figure BDA0002356972880000109
The calculation formula of (2). Due to the peak-to-valley rate mechanism, the battery is charged and discharged at different periods, which results in unbalanced revenue and expenditure. Therefore, there is a cost in charging and discharging the battery. Equation (17) is the loss cost in the off state
Figure BDA00023569728800001010
The calculation formula of (2). This cost is due to the aging cost of the battery, which is an age-bearing battery that ages over time even when not operating. Equation (18) is the penalty cost after entering the dead zone
Figure BDA00023569728800001011
The formula is calculated. Equation (19) is the total daily loss cost
Figure BDA00023569728800001012
The cost is obtained by the product of the active power loss of the network and the electricity price at the current moment, and the equation (20) is the total day voltage deviation cost
Figure BDA00023569728800001013
According to the calculation formula, the voltage deviation rated value brings stability and safety loss to the load and the power grid, so that a comprehensive cost coefficient is considered to measure the cost generated by the voltage deviation.
In the formula (I), the compound is shown in the specification,
Figure BDA00023569728800001014
is the number of charge and discharge operations of the ith device,
Figure BDA00023569728800001015
is the total number of chargeable and dischargeable times of the ith device,
Figure BDA00023569728800001016
is the cost of the battery for the ith device,
Figure BDA00023569728800001017
is the electricity price at the time of the t,
Figure BDA0002356972880000111
is the charging/discharging power at the time t of the ith device, and the value is positive during charging and negative during discharging,
Figure BDA0002356972880000112
is the length of time the ith device is inactive,
Figure BDA0002356972880000113
is the total lifetime of the ith device, c dp Is the dead zone penalty cost per unit time,
Figure BDA0002356972880000114
is the time at which the ith device enters the dead band,
Figure BDA0002356972880000115
is the active power loss of the network at time t,
Figure BDA0002356972880000116
is the active power of line kj at time t,
Figure BDA0002356972880000117
is the reactive power of the line kj at time t,
Figure BDA0002356972880000118
is the voltage of node j at time t, r kj Is the resistance of the line kj，c dv Is the overall cost factor of the voltage deviation,
Figure BDA0002356972880000119
and the voltage deviation value at the jth node t moment, M is the total number of the energy storage and electric automobile devices, and J is the total number of the network nodes.
Constraint condition calculation substep: when the model solution is performed, the following constraint conditions should be satisfied, including the equation constraints of the network power balance of the equations (21) to (22), the constraint of the node voltage of the equation (23), the line capacity constraint of the equation (24), and the energy storage of the equations (11) to (13) of the step S140 and the battery charging and discharging constraint of the electric vehicle;
Figure BDA00023569728800001110
Figure BDA00023569728800001111
0.95U N ≤U i ≤1.05U N (23)
Figure BDA00023569728800001112
in the formula, P i And Q i Is the injected active and reactive power of node i; p DGi And Q DGi The distributed power supply of the node i outputs active power and reactive power, the active power is obtained by calculating formulas (2) to (3) in the step S110, and the reactive power is obtained by converting the power factor into 0.9; p is ESi The energy storage device of the node i outputs active power which is the quantity to be solved; p is EVi The active power absorbed by the electric automobile of the node i is calculated in step S120 to obtain the charging load time distribution of each type of automobile, and then the active power absorbed by the electric automobile of the node i is calculated according to the number of the unscheduled electric automobiles obtained in step S130; g ij And B ij Is the conductance and susceptance of line ij; theta.theta. ij Is the voltage phase angle difference across line ij; u shape N Is the rated voltage; s. the ijmax Is the maximum capacity that line ij is allowed to flow through.
Optimizing scheduling strategy and improving algorithm:
in order to solve the above mentioned model, the present invention employs a particle swarm algorithm. The particle swarm optimization algorithm is a relatively effective optimization algorithm, has good convergence capacity, is easy to fall into a local optimal state, and has low optimization efficiency. Therefore, the invention provides a plurality of optimization strategies based on the particle swarm optimization algorithm, and improves the particle swarm optimization algorithm by combining the strategies. The optimization strategies comprise a dead time optimization strategy, a charging and discharging times optimization strategy and a charging and discharging power optimization strategy.
The particle swarm optimization algorithm of the basic solution algorithm of the model mainly comprises two iterative formulas, namely a speed updating formula and a position updating formula, which are shown in formulas (25) to (26). The new velocity vector is influenced by the old velocity vector, the global optimal position vector and the individual optimal position vector. The new position vector is related to the new velocity vector. Thus, by continually iterating, the position of the particle can be made to tend towards a globally optimal position.
Figure BDA0002356972880000121
Figure BDA0002356972880000122
In the formula (I), the compound is shown in the specification,
Figure BDA0002356972880000123
and
Figure BDA0002356972880000124
is the velocity vector and position vector of the kth iteration of the ith particle; x gbest Is a global optimal position vector; x pbest，i Is the individual optimal position vector of the ith particle; c. C 1 And c 2 Is a learning factor; r is 1 And r 2 Is [0,1]]The random number of (2).
There are two main ways to control the position of the particles, one is to directly constrain the position vector so that it produces the desired result. Another approach is to constrain the velocity vector to move the particles towards the desired result. The invention is mainly based on the formula (25) and the formula (26) to improve, so that the optimizing efficiency is greatly improved.
And on one hand, the particle swarm algorithm is adopted, so that the position vector is directly constrained to generate a desired result. Another method is to constrain the velocity vector to move the particle toward the desired result, and to increase the optimization speed, which specifically includes:
(1) Dead time optimization strategy
The dead time refers to the time when the battery enters a bidirectional working disabled interval, and the effective SOC interval of the battery is usually 20% -80%. When the SOC of the battery reaches the boundary, the battery enters the dead zone. At this time, the battery can only work in one direction, and the effect of the regulating load of the battery is greatly reduced. Therefore, in order to limit the time for the battery to enter the dead zone, the present invention adopts a certain strategy.
As the battery SOC gets closer to the boundary, the battery should be operated in the reverse direction. To achieve this, the particle position vector may be controlled to improve the velocity update equation, which is (27).
Figure BDA0002356972880000131
The formula comprises a plurality of position variables, wherein the position variables are a vector taking time scales as dimensions, and X is a 1440-dimensional vector in 1440 minutes a day and indicates the time at which the energy storage and schedulable electric automobile is charged and discharged and the charging amount and the discharging amount; SOC i Is the energy storage device of the i particles or the energy storage state vector of the dispatchable electric vehicle, namely the calculation result of the formula (6).
In the formula (I), the compound is shown in the specification,
Figure BDA0002356972880000132
is a prior artThe speed updating formula in the operation is specifically expressed as (25); r is a radical of hydrogen 3 And r 4 Is a random number; soc i Is the battery SOC vector for the ith particle; β is a small constant, illustratively 0.001, in order to prevent the denominator from being zero.
By the equation (27), when the SOC approaches 80%, the second term on the right side of the equation approaches zero, and the third term approaches a negative number having a large absolute value. When the three terms are added up, the speed of the particles tends to be negative, so that the positions of the particles tend to be negative, namely, the device has larger possible discharge, so that the SOC of the device is reduced; otherwise, the same applies.
(2) Charge-discharge frequency optimization strategy
If the charging and discharging times of the battery are not limited, the battery can change the charging and discharging state of the battery due to instantaneous power fluctuation, so that the aging of the battery is accelerated, and unnecessary loss is caused. The invention sets a variable delta t of the shortest duration time of the working state of the battery according to the output time characteristics of the load, the distributed power supply and the electric automobile load min . The battery can not change the working state for many times in a short time through the limitation of the variable. Further, in order to stabilize the output power of the battery, the output power of the battery in the shortest duration time period is averaged within a time period, and the specific calculation formula is (28), namely the output power of the battery is stabilized by using the formula.
Figure BDA0002356972880000141
In the formula (I), the compound is shown in the specification,
Figure BDA0002356972880000142
outputting power for the ith device before using the strategy at the t moment; t is t 0 And t 1 Is the start time and the end time of the shortest duration. Through the calculation of the formula (28), the charging and discharging times and the stable output power can be reduced on the basis that the total energy output by the battery in the shortest duration time is not changed.
(3) Charge and discharge power optimization strategy
The energy storage and electric automobile are used for adjusting load, so that the power change of a power grid is smoother. Therefore, the charging/discharging power is also determined in accordance with the load change. In order to more effectively find the optimal charging and discharging power, the invention improves the speed updating formula, so that the particles move towards the expected direction. The new and improved velocity update formula is shown in the following formula.
Figure BDA0002356972880000143
P load-ave ＝P load +P EV -P DG -P avel (30)
The formula (30) is for the variable P in the formula (29) load-ave The supplementary notes of (1) explain how the variable is calculated.
In the formula (I), the compound is shown in the specification,
Figure BDA0002356972880000151
is the improved velocity update formula in formula (27); c. C 3 Is a learning factor; r is 5 Is [0,1]]The random number of (2); p load-ave Is the average total load P of the whole day avel And the difference value of the total load, wherein the total load comprises a daily load, a distributed power supply and an electric automobile load, and the expression is shown as a formula (30).
According to the formula (30), when the total load is larger than the average value, P load-ave Is a positive number, at which point the cell should be discharged, i.e. the position of the particles should tend towards-P load-ave (ii) a When the total load is less than the average value, P is present load-ave Is a negative number, the position of the particles tends to be a positive number, i.e. the battery tends to charge, is satisfactory.
The optimized scheduling strategy mainly comprises three aspects, namely a dead time optimization strategy, a charging and discharging times optimization strategy and a charging and discharging power optimization strategy. The three are improved by the particle swarm algorithm, so that the optimization purpose is achieved. The time for the energy storage and the schedulable electric automobile to enter the energy storage dead zone is limited, so that the function of regulating the power is fully ensured. By limiting the charging and discharging times, the service life of the device can be well protected, and the scheduling scheme is more economical. By optimizing the charge and discharge power, the power regulation and peak clipping and valley filling functions can be well realized, so that the system is more economical and safer to operate.
Through the optimization, the energy storage and schedulable electric automobile can be cooperatively and optimally scheduled, the total load of a power grid becomes more stable, the working efficiency of the power grid is improved, and the total cost is reduced.
The optimal scheduling method aims at maximizing efficiency, takes the minimum total cost as an exemplary expression, takes power balance and voltage and current safety stability as constraint conditions, considers the optimal scheduling model and the optimal scheduling strategy of the electric automobile and the energy storage device of the distributed power supply, and obtains the optimal scheduling scheme of the schedulable electric automobile and the energy storage. According to the power distribution network operated by the scheme, the operation efficiency is higher, the whole power distribution network is more economic, safe and reliable, and the power distribution network has important practical significance for the sustainable development of the power grid.
The embodiment is as follows:
the invention adopts an IEEE33 node system for example analysis, and the grid structure is shown in figure 4.
The distributed power parameters and access points are shown in table 1.
TABLE 1 distributed Power parameters
Figure BDA0002356972880000161
The relevant parameters and access points of the electric vehicle are shown in table 2.
TABLE 2 parameters of electric vehicles
Figure BDA0002356972880000162
Figure BDA0002356972880000171
The energy storage parameters are shown in table 3.
TABLE 3 parameters of stored energy
Figure BDA0002356972880000172
The four season curve of the load is shown in fig. 5. The units in the graph are per unit values.
The algorithm parameters are shown in the following table.
TABLE 4 Algorithm parameters
Figure BDA0002356972880000173
The 24-hour electricity rate change curve is shown in fig. 6.
The wind speed and the light intensity are sampled according to the sampling method of S110, and the result is shown in fig. 7.
According to the model and method of S120, the charging load curve of the electric vehicle is shown in fig. 9 after sampling.
In order to verify the correctness of the model provided by the invention, the whole year is divided into four scenes, and each season corresponds to one scene. Then, the solutions were performed, and the results are shown in table 5.
TABLE 5 Multi-Scenario results
Figure BDA0002356972880000181
The SOC variation curves of the energy storage and various types of electric vehicles throughout the day in four seasons of spring, summer, autumn and winter are shown in fig. 10 (a), (b), (c) and (d). Fig. 11 (a), (b), (c), and (d) are respectively a comparison of the total load curves before and after the energy storage and schedulable electric vehicle adjustment in four seasons of spring, summer, autumn, and winter.
As can be seen from fig. 10, fig. 11 and the tables, the total load of the power grid becomes more stable and the total cost is improved due to the cooperative optimal scheduling of the energy storage and schedulable electric vehicles.
While the invention has been described in further detail with reference to specific preferred embodiments thereof, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.

Claims (4)

1. An electric automobile and energy storage device optimal scheduling method considering that a distributed power supply is connected into a distribution network is characterized by comprising the following steps:
distributed power supply output prediction step S110: predicting the output of various distributed power supplies in one day according to the probability distribution curves of the wind speed and the illumination environment variables;
electric vehicle load prediction step S120: constructing a probability distribution model of the daily driving mileage and the charging time period of the electric automobile, and sampling the daily driving mileage and the charging time period of the electric automobile to obtain the load condition of the electric automobile in one day;
step S130 of establishing electric vehicle conversion models, wherein a schedulable and non-schedulable type conversion model of the electric vehicle is established according to the real-time electricity price, and the schedulable electric vehicle number N is obtained EV-d ；
A charging and discharging model building step S140, building a charging and discharging model of the energy storage and schedulable electric automobile according to the parameters of the energy storage device and the schedulable electric automobile;
a cooperative optimization scheduling step S150, which is to construct an optimization scheduling model taking the minimum total cost including the charge-discharge operation cost, the charge-discharge electric quantity cost, the daily loss cost, the dead zone penalty cost, the network loss cost and the voltage deviation cost of the energy storage and schedulable electric vehicle as a target function and taking the power balance and the voltage and current safety and stability constraint as constraint conditions, and perform cooperative optimization scheduling on the schedulable electric vehicle and the energy storage device by adopting a particle swarm algorithm improved based on an optimization scheduling strategy to obtain an operation scheduling plan within one day;
in the distributed power output prediction step S110, the distributed power includes a fan and photovoltaic power generation, the wind speed is divided into 4 seasons according to the change of the wind speed and the illumination intensity with time, 24 moments each season, a total of 96 groups of probability density curves, the illumination intensity is divided into 12 groups of illumination intensity curves, each group corresponds to a typical daily illumination intensity curve changing with time within one month, the wind speed and the illumination intensity are respectively sampled, and the wind speed and the illumination intensity are randomly selected, so as to further determine the output of the fan and the photovoltaic power generation;
in the distributed power output prediction step S110, the wind speed and the illumination intensity are sampled to obtain the wind speed v and R at corresponding time c The fan output model adopts a Weibull model, which is shown as the following formula,
Figure FDA0003811300080000021
in the formula P WT Outputting active power of the fan; p r-WT Outputting rated active power of the fan; v. of ci The cut-in wind speed of the fan; v. of r Is the rated wind speed of the fan; v. of co The cut-out wind speed of the fan;
the photovoltaic output model adopts Beta distribution, which is shown as the following formula:
Figure FDA0003811300080000022
in the formula, P PV Is the active power output of the photovoltaic; n is a radical of an alkyl radical pv Is the number of photovoltaic cells; p is r-PV Is the rated active power output of a photovoltaic unit; r r Is the nominal light intensity; k is the power temperature coefficient; t is a unit of c And T r Actual temperature and standard temperature;
in the electric vehicle load prediction step S120, the types of electric vehicles are classified into three types, i.e., buses, taxis, and private cars, and the probability distribution of daily mileage is shown as follows,
Figure FDA0003811300080000031
in the formula, D EV The daily driving mileage of the electric automobile follows normal distribution; μ and σ are the mean and standard deviation of the normal distribution;
according to the formula (4), the daily driving mileage of each electric automobile is sampled to obtain the daily driving mileage of the electric automobile, the electric quantity consumed by the electric automobile every day is calculated according to the formula (5), the energy storage state SOC of the electric automobile is further calculated, and the calculation formula is shown as (6),
Figure FDA0003811300080000032
Figure FDA0003811300080000033
in the formula, E EV-consumption Is the electric automobile consumes power every day D EVmax Is the maximum number of miles driven, C EV Is the battery capacity, SOC of the electric vehicle EV Is that the electric automobile runs D EV The SOC of the remaining part of the battery,
according to daily consumption electric quantity E EV-consumption Calculating the time T required to be charged every day EV-charge The calculation formula is shown as the following formula,
Figure FDA0003811300080000034
in the formula, T EV-charge Is the total charging time required by the electric vehicle during the day; p EV-charging Is the charging power of the electric vehicle;
according to the charging period probabilities of different types of electric automobiles, sampling the charging initial time of each electric automobile, and finally obtaining the charging initial time t of each electric automobile EV-start Assuming that the electric vehicle is continuously charged in the charging distribution time period, the calculation method of the charging period may be calculated by the following steps:
1) First time from t EV-start Starting, starting from the current moment, judging whether the next moment is in the charging distribution time period, and if so, adding 1 to the charged time; if the position of the mobile phone is not in the same place,
setting the next charging time as the starting time of the next charging distribution period, adding 1 to the charged time, recording the time as the charging time, and updating the current time to the value of the next time;
2) Judging whether the charged time reaches the required total charging time T EV-charge If so, ending the searching for the charging time period to obtain the charging time period of the electric automobile; if the total time is not reached, returning to 1), continuing to search for the charging period,
through the steps, the charging time interval of each electric automobile can be obtained, and the charging power P of the electric automobile is combined EV-charging The charging load time distribution of the electric vehicle can be obtained.
2. The optimal scheduling method of claim 1,
in the step S130 of building the electric vehicle conversion model, the electric vehicle dispatchable and non-dispatchable type conversion model is as formula (8),
Figure FDA0003811300080000041
in the formula, N EV-d Is converted into the number of schedulable electric vehicles, R (x) is an integer function, k ev-trans Is the conversion coefficient, c udd Is the actual peak to valley electricity price difference, c 0 Is the threshold for the transition.
3. The optimized scheduling method of claim 2,
in the charge and discharge model construction step S140, a charge energy balance equation, i.e., equation (9), and a discharge energy balance equation, i.e., equation (10), are constructed using the charge and discharge loss coefficients,
E es (t)＝E es (t-Δt)+ηP es-c Δt (9)
Figure FDA0003811300080000042
in the formula, E es (t) is the energy stored by the energy storage device at time t, η is the charge-discharge loss coefficient of the energy storage device, P es-c Is the charging power of the energy storage device, P es-f Is the discharge power of the energy storage device;
respectively establishing constraints aiming at the SOC and the charge and discharge of the stored energy,
SOC min ≤SOC≤SOC max (11)
P es-cmin ≤P es-c ≤P es-cmax (12)
P es-fmin ≤P es-f ≤P es-fmax (13)
in the formula, SOC max And SOC min Is the upper and lower limits of the SOC of the energy storage device; p es-cmax And P es-cmin Is charging power P of energy storage device es-c The upper and lower limits of (d); p is es-fmax And P es-fmin Is the discharge power P of the energy storage device es-f The upper and lower limits of (2).
4. The optimized scheduling method of claim 3,
the cooperative optimal scheduling step S150 includes:
an objective function calculation substep: the total cost comprises the cost of the number of times of charging and discharging operations of the energy storage and schedulable electric automobile, the cost of charging and discharging electricity quantity, the loss cost when not working, the punishment cost after the charging and discharging of the battery enters a dead zone, the total network loss cost and the voltage deviation cost all day long,
Figure FDA0003811300080000051
Figure FDA0003811300080000052
Figure FDA0003811300080000053
Figure FDA0003811300080000054
Figure FDA0003811300080000055
Figure FDA0003811300080000061
Figure FDA0003811300080000062
the total objective function is expressed by the equation (14), and the equation (15) represents the cost of the number of charge and discharge operations
Figure FDA0003811300080000063
Equation (16) is the charge/discharge electricity cost
Figure FDA0003811300080000064
Equation (17) is the loss cost in the off state
Figure FDA0003811300080000065
Equation (18) is the penalty cost after entering the dead zone
Figure FDA0003811300080000066
The formula (19) is the total loss cost of the whole day
Figure FDA0003811300080000067
Equation (20) is the total voltage deviation cost
Figure FDA0003811300080000068
The formula for the calculation of (a) is,
in the formula (I), the compound is shown in the specification,
Figure FDA0003811300080000069
is the number of charge and discharge operations of the ith device,
Figure FDA00038113000800000610
is the total number of chargeable and dischargeable times of the ith device,
Figure FDA00038113000800000611
is the battery cost of the ith device,
Figure FDA00038113000800000612
is the electricity price at the time of the t,
Figure FDA00038113000800000613
is the charging/discharging power at the time t of the ith device, and the value is positive during charging and negative during discharging,
Figure FDA00038113000800000614
is the length of time the ith device is not operating,
Figure FDA00038113000800000615
is the total lifetime of the ith device, c dp Is the dead zone penalty cost per unit time,
Figure FDA00038113000800000616
is the time the ith device enters the dead band,
Figure FDA00038113000800000617
is the active power loss of the network at time t,
Figure FDA00038113000800000618
is the active power of line kj at time t,
Figure FDA00038113000800000619
is the reactive power of the line kj at time t,
Figure FDA00038113000800000620
is the voltage of node j at time t, r kj Is the resistance of the line kj, c dv Is the overall cost factor of the voltage deviation,
Figure FDA00038113000800000621
the voltage deviation value at the jth node t moment is shown, M is the total number of energy storage and electric automobile devices, and J is the total number of network nodes;
constraint condition calculation substep: when the model solution is performed, the following constraint conditions should be satisfied, including equation constraints of network power balance of equations (21) - (22), constraint of node voltage of equation (23), and line capacity constraint of equation (24), and further including equations (11) - (13) of step S140 for energy storage and the electric vehicle satisfying battery charging and discharging constraints;
Figure FDA0003811300080000071
Figure FDA0003811300080000072
0.95U N ≤U i ≤1.05U N (23)
Figure FDA0003811300080000073
in the formula, P i And Q i Is the injected active and reactive power of node i; p DGi And Q DGi The distributed power supply of the node i outputs active power and reactive power, the active power is obtained by calculating formulas (2) to (3) in the step S110, and the reactive power is obtained by converting the power factor into 0.9; p is ESi The energy storage device of the node i outputs active power which is a quantity to be solved; p EVi The active power absorbed by the electric automobile of the node i is calculated in step S120 to obtain the charging load time distribution of each type of automobile, and then the active power absorbed by the electric automobile of the node i is calculated according to the number of the unscheduled electric automobiles obtained in step S130; g ij And B ij Is the conductance and susceptance of line ij; theta ij Is the voltage phase angle difference across line ij; u shape N Is the rated voltage; s ijmax Is the maximum capacity that line ij is allowed to flow through;
the sub-steps of optimizing the scheduling strategy and improving the algorithm specifically comprise:
(1) Dead time optimization strategy
The dead time is the time when the battery enters the bidirectional working disabled interval, the effective SOC interval of the battery is 20% -80%, when the SOC of the battery is closer to the boundary, the battery should run in the reverse direction, the particle position vector is controlled, the speed updating formula is improved, the improved formula is (27),
Figure FDA0003811300080000074
in the formula (I), the compound is shown in the specification,
Figure FDA0003811300080000081
is a velocity update formula in the prior art, r 3 And r 4 Is a random number; soc i Is the battery SOC vector for the ith particle; β is a constant;
(2) Charge and discharge frequency optimization strategy
In order to make the output power of the battery smooth, the output power of the battery in the shortest duration time period is taken as an average value in a time period, and the specific calculation formula is (28),
Figure FDA0003811300080000082
in the formula (I), the compound is shown in the specification,
Figure FDA0003811300080000083
outputting power for the ith device before using the strategy at the t moment; t is t 0 And t 1 Is the starting time and the ending time of the shortest duration;
(3) Charge and discharge power optimization strategy
The energy storage and electric automobile are used for adjusting load, so that the power change of a power grid is more gradual, a speed updating formula is improved, particles move towards an expected direction, and the new improved speed updating formula is shown as the following formula:
Figure FDA0003811300080000084
P load-ave ＝P load +P EV -P DG -P avel (30)
in the formula (I), the compound is shown in the specification,
Figure FDA0003811300080000085
is formula (27); c. C 3 Is a learning factor; r is a radical of hydrogen 5 Is [0,1]]The random number of (2); p load-ave Is the average total load P of the whole day avel And (4) the difference value of the total load, wherein the total load comprises a daily load, a distributed power supply and an electric vehicle load, and the expression is shown as a formula (30).
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