CN113128127A - Multi-region virtual power plant coordinated optimization scheduling method - Google Patents

Multi-region virtual power plant coordinated optimization scheduling method Download PDF

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CN113128127A
CN113128127A CN202110459669.7A CN202110459669A CN113128127A CN 113128127 A CN113128127 A CN 113128127A CN 202110459669 A CN202110459669 A CN 202110459669A CN 113128127 A CN113128127 A CN 113128127A
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郭明星
高赐威
吕冉
郭昆健
王素
丁建勇
陈涛
王晓晖
宋梦
王海群
明昊
张铭
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State Grid Shanghai Electric Power Co Ltd
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Abstract

The invention discloses a coordinated optimization scheduling method for a multi-region virtual power plant, which belongs to the technical field of power scheduling and specifically comprises the following steps: (1) analyzing the upper limit and the lower limit of the generated power of the virtual power plant in each area; (2) establishing a multi-region virtual power plant coordination optimization scheduling model; (3) and solving the optimized scheduling model by adopting a goblet sea squirt group algorithm. The invention provides a multi-region virtual power plant coordinated optimization scheduling method, which comprises the steps of aggregating air conditioners at user sides of all regions and regulation potentials of electric vehicles to form a virtual power plant by analyzing the regulation potentials, predicting the upper limit and the lower limit of the power generation power of the virtual power plant of each region, further establishing a multi-region virtual power plant coordinated optimization scheduling model with the minimum network loss as a target, solving the optimization scheduling model by adopting a goblet sea squirt group algorithm, and providing technical support for realizing the minimization of power grid loss through the combined scheduling of the multi-region virtual power plant.

Description

Multi-region virtual power plant coordinated optimization scheduling method
Technical Field
The invention belongs to the technical field of power dispatching, and particularly relates to a coordinated optimization dispatching method for a multi-region virtual power plant.
Background
The virtual power plant is an effective means for constructing large-scale, normalized and accurate distributed resource adjustability, can effectively realize friendly interaction of distributed resources and a power system, realizes integration and distribution of various resources, and has great application value. At present, virtual power plants are practiced many times in the world, the development of the virtual power plants also brings new challenges to power system scheduling, and unreasonable scheduling often increases power network loss and reduces the economy of power grid operation. The existing research mainly focuses on the aspects of coordination control and transaction bidding inside a virtual power plant and optimal scheduling of a single virtual power plant, and related research for performing coordination optimal scheduling on virtual power plants in multiple areas is lacked.
Disclosure of Invention
The invention aims to overcome the defects of the background technology, and provides a coordinated optimization scheduling method for a multi-region virtual power plant.
The purpose of the invention can be realized by the following technical scheme: a multi-region virtual power plant coordinated optimization scheduling method comprises the following steps:
(1) analyzing the upper limit and the lower limit of the generated power of the virtual power plant in each area;
(2) establishing a multi-region virtual power plant coordination optimization scheduling model;
(3) and solving the optimized scheduling model by adopting a goblet sea squirt group algorithm.
The specific step (1) comprises the following steps:
first, a day is divided into 24 × 60/Δ t control periods by a time slot length Δ t (unit is min). And then, analyzing the upper and lower limit power of the variable frequency air conditioner cluster and the virtual power generation of the electric automobile cluster in the area i corresponding to the bus i in the power distribution network with the N buses.
(1-1) analysis of upper and lower limits of generated power of variable frequency air conditioner cluster
Firstly, modeling is carried out on a single variable frequency air conditioner. The thermodynamic process of the temperature change of the air-conditioning room under the action of the indoor and outdoor cold and heat sources can be represented by a first-order equivalent thermal parameter model, and the first-order differential equation of the thermodynamic process is as follows:
Figure BDA0003041925890000021
in the formula: t isin(t) is the indoor gas temperature at time t, ° c; r is equivalent thermal resistance, namely the reciprocal of the air heat loss coefficient, DEG C/kW; c is the heat capacity of the indoor gas, kJ/DEG C; t isout(t) is the ambient temperature at time t, DEG C; qacThe refrigerating capacity of the variable frequency air conditioner is kW.
The relationship between the electric power and the refrigerating capacity of the variable frequency air conditioner and the frequency obtained by further simplifying the experimental result is respectively as follows:
Pac＝k1f+l1 (2)
Qac＝k2f+l2 (3)
in the formula: pacIs the electric power of the variable frequency air conditioner, kW; k is a radical of1,l1,k2,l2Is a constant coefficient; and f is the frequency of the variable frequency air conditioner compressor, Hz.
P can be obtained from the formulae (2) and (3)acAnd QacThe relationship of (1) is:
Figure BDA0003041925890000022
when the indoor temperature is kept at TiIn time, the required refrigerating capacity is shown as the following formula (1):
Figure BDA0003041925890000023
the electric power of the inverter air conditioner can be obtained by the formulas (4) and (5):
Figure BDA0003041925890000031
when the air conditioner is operated under the load of the air conditioner and TsetAnd (t) the electric power is the power base value. From equation (6), the electric power base value is:
Figure BDA0003041925890000032
the virtual power generation power of a single variable frequency air conditioner is as follows:
Pv,ac＝Pac-Pbase (8)
it should be noted that the electric power of the inverter air conditioner load does not increase or decrease without limit, and the inverter air conditioner electric power is [ P ] due to the user comfort constraint and the limitation of the frequency fac,min,Pac,max]An internal variation. Thus, in any time interval, Pv,acAll satisfy:
Pv,ac,min≤Pv,ac≤Pv,ac,max (9)
Pv,ac,min＝-(Pac,max-Pbase) (10)
Pv,ac,max＝-(Pac,min-Pbase) (11)
through the analysis, the controllable power of a single air conditioner at every moment can be changed, but the environmental temperature, the set temperature and the like are almost unchanged in a period with a small control duration. Therefore, to reduce the difficulty of control, P within Δ t can be approximatedv,acIs a constant value.
In summary, if the region i has N in the time period mACThe variable frequency air conditioner is controllable, and the virtual power generation power of the air conditioner cluster in the area i is as follows:
Figure BDA0003041925890000033
the virtual generated power upper and lower limits of the air conditioner cluster in the area i in the time period m are constrained as follows:
Pi,m,V,AC,min≤Pi,m,V,AC≤Pi,m,V,AC,max (13)
Figure BDA0003041925890000034
Figure BDA0003041925890000035
in the formula: pj,m,v,ac，Pj,m,v,ac,min，Pj,m,v,ac,maxAnd respectively limiting the virtual power generation power of the jth air conditioner in the area ith in the time period m and the upper limit and the lower limit of the virtual power generation power.
(1-2) analysis of upper and lower limits of generated power of electric vehicle
A single electric vehicle is also modeled first. In order to meet the travel requirement of a user, according to the initial state of charge SOC of the electric vehicle k0,iThe required charging time of the available electric vehicle i is as follows:
Figure BDA0003041925890000041
in the formula: SOCE,kIs the expected state of charge of the electric vehicle i; wkIs the battery capacity; p is a radical ofkThe charge and discharge power thereof.
And during the period of connecting the electric automobile into the charging pile, determining whether the electric automobile participates in ordered scheduling according to the charging requirement and the residence time of the electric automobile. User k expects a dwell period as follows:
Tstay,k＝(tleave,k-tstart,k)Δt (17)
if m is 1, the remaining stay time of the electric vehicle k in the period m is as follows:
Tstay,k,m＝Tstay,i-(m-tstart,k)Δt (18)
in the formula: t is tleave,iA departure period set for the user; t is tstart,iReach to fill electric pile period for electric automobile.
According to the state of charge SOC of the electric vehicle k in the time period mn,kThe required charging time of the available electric vehicle k in the time period m is as follows:
Figure BDA0003041925890000042
when T isstay,k,m≤Tneed,k,mIn time, the electric vehicles must be in a charging state in the network time period to meet the charging requirement of the electric vehicles (or the time is too short to meet the requirement of vehicle owners), and at this time, the electric vehicles do not have the scheduling capability.
When T isstay,k,m>Tneed,k,mIn time, the virtual machine set control center has enough time to charge the electric vehicle k in the time period m and meet the electric quantity requirement of a user, and a certain time margin is reserved. Under the condition, the virtual machine set can achieve the purpose of power grid dispatching by changing the charging load of the electric automobile.
According to the conditions, whether the electric automobile k has a schedulable space can be judged. For the electric automobiles which can be subjected to ordered scheduling, the charging pile can divide the electric automobile cluster into a charging group and a discharging group according to the charge state of the electric automobiles in the time period m. According to the state of charge SOC of the current momentm,kExpected electric quantity SOC preset by vehicle ownerE,kAnd grouping is performed.
If SOCm,k≤SOCE,kThe electric vehicle k needs to be charged. If whether the electric vehicle k is charged or not in the time period m can be guaranteed to meet the expected charge amount when the electric vehicle k goes out, the electric vehicle k is considered to have charging controllability in the time period. Will satisfy equation (14) and SOCm,k≤SOCE,kThe electric vehicle of (2) is arranged in a charging group.
Figure BDA0003041925890000051
If SOCm,k>SOCE,kAt the moment, the electric automobile has a discharge margin SOCm,k－SOCE,kWhen evaluating the discharging capability of the vehicle group, it is required to ensure that the electric vehicle i meets the expected charge amount when traveling. Therefore, equation (15) will be satisfied and SOCm,k>SOCE,kThe electric vehicle is arranged in a discharge group and is responsible for discharging.
Figure BDA0003041925890000052
Assuming that a grid charging electric vehicle set phi and a discharging electric vehicle set psi in a region are as follows:
Φ＝{CHc|c∈NCH} (22)
Ψ＝{DHd|d∈NDH} (23)
in the formula: CH (CH)cIndicating a c-th charging electric automobile in the area; n is a radical ofCHThe number of electric vehicles in a charging group; DHdIndicating the d-th discharging electric automobile in the area; n is a radical ofDHThe number of the electric automobiles in the discharge group.
The maximum chargeable load of the charging group vehicle group in the time period m is as follows:
Figure BDA0003041925890000053
pc＝ps (25)
the maximum dischargeable load of the discharging group of cars in the period m is as follows:
Figure BDA0003041925890000054
pd＝-ps (27)
in sum, the electric automobile cluster generating power P in the area i in the time period mi,m,V,EVHas a lower limit of Pi,m,V,EV,min＝-PCH,i,mUpper limit of Pi,m,V,EV,max＝-PDH,i,m。
(1-3) upper and lower limits of generated power of virtual power plants in each region
In the area i corresponding to each bus i, the variable frequency air conditioner cluster and the electric vehicle cluster are aggregated to form a virtual power plant of the area i, and in sum, the overall output of the virtual power plant of the area i in the time period m is as follows:
Pi,m,VPP＝Pi,m,V,AC+Pi,m,V,EV (28)
the virtual generated power upper and lower limits of the air conditioner cluster in the area i in the time period m are constrained as follows:
Pi,m,VPP,min≤Pi,m,VPP≤Pi,m,VPP,max (29)
Pi,m,VPP,min＝Pi,m,V,AC,min+Pi,m,V,EV,min (30)
Pi,m,VPP,max＝Pi,m,V,AC,max+Pi,m,V,EV,max (31)
specifically, the step (2) includes the following steps:
in the period of m, the total active network loss of the power distribution network with N buses is as follows:
Figure BDA0003041925890000061
Figure BDA0003041925890000062
Figure BDA0003041925890000063
in the formula: u shapei,mAnd Uj,mThe voltage of the ith bus and the j bus in the m time period respectively; r isijThe resistance of the feeder line between the ith node and the j node; pi,mAnd Pj,mThe injection active power of the ith bus and the j bus in the m time period is respectively; qi,mAnd Qj,mAnd injecting reactive power of the ith bus and the j bus in the n time period respectively.
And after the virtual power plant is accessed, performing multi-region virtual power plant joint optimization scheduling in the m period by taking the minimum network loss as a target. The scheduling goals are as follows:
Figure BDA0003041925890000064
the constraint is shown in equation (29).
Specifically, the step (3) includes the following steps:
(3-1) setting the lower bound of the search space to Pm,VPP,max＝{P1,m,VPP,max,P2,m,VPP,max,…,PN,m,VPP,max}, lower bound is: pm,VPP,min＝{P1,m,VPP,min,P2,m,VPP,min,…,PN,m,VPP,minRandomly initializing an L multiplied by N virtual power plant power generation power matrix according to upper and lower bounds
Figure BDA0003041925890000071
Wherein L is the total number of goblet sea squirt groups, and the generating power vector of the virtual power plant of the first goblet sea squirt is:
Figure BDA0003041925890000072
(3-2) obtaining a network loss vector corresponding to each goblet and sea squirt based on the formula (35):
Figure BDA0003041925890000073
and (3-3) sorting the network loss values of the goblet sea squirt groups from small to large, and selecting the virtual power plant power generation power vector corresponding to the goblet sea squirt with the smallest network loss value as a target position.
(3-4) sorting the net loss values corresponding to the remaining L-1 goblet ascidians from small to large, and selecting the front half goblet ascidians as a leader and the rear half goblet ascidians as a follower.
And (3-5) updating the virtual power plant power generation amount corresponding to each area of the leader based on the formula (36), and updating the virtual power plant power generation amount corresponding to each area of the follower based on the formula (38).
Figure BDA0003041925890000074
In the formula:
Figure BDA0003041925890000075
representing the power generated by the virtual power plant in the ith area in the ith individual in the goblet sea squirt population; fiRepresenting the power generation power of the ith area virtual power plant corresponding to the goblet ascidian with the minimum network loss value in the goblet ascidian group; c1, c2 and c3 are all control parameters, wherein c2 and c3 are [0, 1 ]]Random numbers in the range, c1 is the main control parameter, when the random numbers are greater than 1, the algorithm performs global exploration, when the random numbers are less than 1, the algorithm starts local exploration, so that the algorithm performs full search in the first half of iteration, and performs local precise search in the second half of iteration, and the expression of c1 is selected as follows:
Figure BDA0003041925890000076
in the formula: r represents the current iteration number; r represents the maximum number of iterations.
The follower is in a chain shape and follows the motion in sequence, the motion mode accords with the Newton's law of motion, and the update equation is as follows:
Figure BDA0003041925890000077
in the formula:
Figure BDA0003041925890000078
is the power generation power of the ith area virtual power plant of the updated ith follower,
Figure BDA0003041925890000079
and the generated power of the ith regional virtual power plant before updating.
And (3-6) calculating and updating the network loss value of the goblet ascidian group, comparing the updated network loss value of each goblet ascidian with the network loss value corresponding to the target position, and if the network loss value is higher than the network loss value of the target position, selecting the virtual power plant vector of each region corresponding to the goblet ascidian as the new target position.
And (5) repeating the steps (3-4) to (3-6) according to certain iteration times to obtain the optimal power generation power of the virtual power plant in each area.
The invention has the beneficial effects that: the invention provides a coordination optimization scheduling method of a multi-region virtual power plant aiming at the current situation of vigorous development of the virtual power plant, the method disclosed by the invention is used for aggregating the regulation potentials of air conditioners and electric automobiles at the user sides of all regions to form the virtual power plant, the upper and lower limits of the generated power of the virtual power plant of each region are predicted, a multi-region coordination optimization scheduling model with the minimum network loss as a target is established, the optimized scheduling model is further solved by adopting a zun sea squirt group algorithm, a technical support is provided for realizing the minimum power grid loss through the combined scheduling of the multi-region virtual power plant, and the method has great significance for realizing the economic operation of a power system when the multi-region virtual power plant is connected into a power grid.
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FIG. 1 is a general flow diagram of the process of the present invention.
Detailed Description
The present invention will be further described with reference to the accompanying drawings.
Fig. 1 shows a coordinated optimization scheduling method for a multi-region virtual power plant, and the following steps are specifically described.
Step 1: and analyzing the upper limit and the lower limit of the generated power of the virtual power plant in each region.
First, a day is divided into 24 × 60/Δ t control periods by a time slot length Δ t (unit is min). And then, analyzing the upper and lower limit power of the variable frequency air conditioner cluster and the virtual power generation of the electric automobile cluster in the area i corresponding to the bus i in the power distribution network with the N buses.
(1-1) analysis of upper and lower limits of generated power of variable frequency air conditioner cluster
Firstly, modeling is carried out on a single variable frequency air conditioner. The thermodynamic process of the temperature change of the air-conditioning room under the action of the indoor and outdoor cold and heat sources can be represented by a first-order equivalent thermal parameter model, and the first-order differential equation of the thermodynamic process is as follows:
Figure BDA0003041925890000091
in the formula: tin (t) is the indoor gas temperature at time t, ° c; r is equivalent thermal resistance, namely the reciprocal of the air heat loss coefficient, DEG C/kW; c is the heat capacity of the indoor gas, kJ/DEG C; tout (t) is the ambient temperature at time t, ° C; qacThe refrigerating capacity of the variable frequency air conditioner is kW.
The relationship between the electric power and the refrigerating capacity of the variable frequency air conditioner and the frequency obtained by further simplifying the experimental result is respectively as follows:
Pac＝k1f+l1 (2)
Qac＝k2f+l2 (3)
in the formula: pacIs the electric power of the variable frequency air conditioner, kW; k is a radical of1,l1,k2,l2Is a constant coefficient; and f is the frequency of the variable frequency air conditioner compressor, Hz.
P can be obtained from the formulae (2) and (3)acAnd QacThe relationship of (1) is:
Figure BDA0003041925890000092
when the indoor temperature is kept at TiIn time, the required refrigerating capacity is shown as the following formula (1):
Figure BDA0003041925890000093
the electric power of the inverter air conditioner can be obtained by the formulas (4) and (5):
Figure BDA0003041925890000094
when the air conditioner is operated under the load of the air conditioner and TsetAnd (t) the electric power is the power base value. From equation (6), the electric power base value is:
Figure BDA0003041925890000095
the virtual power generation power of a single variable frequency air conditioner is as follows:
Pv,ac＝Pac-Pbase (8)
it should be noted that the electric power of the inverter air conditioner load does not increase or decrease without limit, and the inverter air conditioner electric power is [ P ] due to the user comfort constraint and the limitation of the frequency fac,min,Pac,max]An internal variation. Thus, in any time interval, Pv,acAll satisfy:
Pv,ac,min≤Pv,ac≤Pv,ac,max (9)
Pv,ac,min＝-(Pac,max-Pbase) (10)
Pv,ac,max＝-(Pac,min-Pbase) (11)
through the analysis, the controllable power of a single air conditioner at every moment can be changed, but the environmental temperature, the set temperature and the like are almost unchanged in a period with a small control duration. Therefore, to reduce the difficulty of control, P within Δ t can be approximatedv,acIs a constant value.
In summary, if the region i has N in the time period mACThe variable frequency air conditioner is controllable, and the virtual power generation power of the air conditioner cluster in the area i is as follows:
Figure BDA0003041925890000101
the virtual generated power upper and lower limits of the air conditioner cluster in the area i in the time period m are constrained as follows:
Pi,m,V,AC,min≤Pi,m,V,AC≤Pi,m,V,AC,max (13)
Figure BDA0003041925890000102
Figure BDA0003041925890000103
in the formula: pj,m,v,ac，Pj,m,v,ac,min，Pj,m,v,ac,maxAnd respectively limiting the virtual power generation power of the jth air conditioner in the area ith in the time period m and the upper limit and the lower limit of the virtual power generation power.
(1-2) analysis of upper and lower limits of generated power of electric vehicle
A single electric vehicle is also modeled first. In order to meet the travel requirement of a user, according to the initial state of charge SOC of the electric vehicle k0,iThe required charging time of the available electric vehicle i is as follows:
Figure BDA0003041925890000104
in the formula: SOCE,kIs the expected state of charge of the electric vehicle i; wkIs the battery capacity; p is a radical ofkThe charge and discharge power thereof.
And during the period of connecting the electric automobile into the charging pile, determining whether the electric automobile participates in ordered scheduling according to the charging requirement and the residence time of the electric automobile. User k expects a dwell period as follows:
Tstay,k＝(tleave,k-tstart,k)Δt (17)
if m is 1, the remaining stay time of the electric vehicle k in the period m is as follows:
Tstay,k,m＝Tstay,i-(m-tstart,k)Δt (18)
in the formula: t is tleave,iA departure period set for the user; t is tstart,iReach to fill electric pile period for electric automobile.
According to the state of charge SOC of the electric vehicle k in the time period mn,kThe required charging time of the available electric vehicle k in the time period m is as follows:
Figure BDA0003041925890000111
when T isstay,k,m≤Tneed,k,mIn time, the electric vehicles must be in a charging state in the network time period to meet the charging requirement of the electric vehicles (or the time is too short to meet the requirement of vehicle owners), and at this time, the electric vehicles do not have the scheduling capability.
When T isstay,k,m>Tneed,k,mIn time, the virtual machine set control center has enough time to charge the electric vehicle k in the time period m and meet the electric quantity requirement of a user, and a certain time margin is reserved. Under the condition, the virtual machine set can achieve the purpose of power grid dispatching by changing the charging load of the electric automobile.
According to the conditions, whether the electric automobile k has a schedulable space can be judged. For the electric automobiles which can be subjected to ordered scheduling, the charging pile can divide the electric automobile cluster into a charging group and a discharging group according to the charge state of the electric automobiles in the time period m. According to the state of charge SOC of the current momentm,kExpected electric quantity SOC preset by vehicle ownerE,kAnd grouping is performed.
If SOCm,k≤SOCE,kThe electric vehicle k needs to be charged. If whether the electric vehicle k is charged or not in the time period m can be guaranteed to meet the expected charge amount when the electric vehicle k goes out, the electric vehicle k is considered to have charging controllability in the time period. Will satisfy equation (14) and SOCm,k≤SOCE,kThe electric vehicle of (2) is arranged in a charging group.
Figure BDA0003041925890000112
If SOCm,k>SOCE,kAt the moment, the electric automobile has a discharge margin SOCm,k－SOCE,kWhen evaluating the discharging capability of the vehicle group, it is required to ensure that the electric vehicle i meets the expected charge amount when traveling. Therefore, equation (15) will be satisfied and SOCm,k>SOCE,kThe electric vehicle is arranged in a discharge group and is responsible for discharging.
Figure BDA0003041925890000121
Assuming that a grid charging electric vehicle set phi and a discharging electric vehicle set psi in a region are as follows:
Φ＝{CHc|c∈NCH} (22)
Ψ＝{DHd|d∈NDH} (23)
in the formula: CH (CH)cIndicating a c-th charging electric automobile in the area; n is a radical ofCHThe number of electric vehicles in a charging group; DHdIndicating the d-th discharging electric automobile in the area; n is a radical ofDHThe number of the electric automobiles in the discharge group.
The maximum chargeable load of the charging group vehicle group in the time period m is as follows:
Figure BDA0003041925890000122
pc＝ps (25)
the maximum dischargeable load of the discharging group of cars in the period m is as follows:
Figure BDA0003041925890000123
pd＝-ps (27)
in sum, the electric automobile cluster generating power P in the area i in the time period mi,m,V,EVHas a lower limit of Pi,m,V,EV,min＝-PCH,i,mUpper limit of Pi,m,V,EV,max＝-PDH,i,m。
(1-3) upper and lower limits of generated power of virtual power plants in each region
In the area i corresponding to each bus i, the variable frequency air conditioner cluster and the electric vehicle cluster are aggregated to form a virtual power plant of the area i, and in sum, the overall output of the virtual power plant of the area i in the time period m is as follows:
Pi,m,VPP＝Pi,m,V,AC+Pi,m,V,EV (28)
the virtual generated power upper and lower limits of the air conditioner cluster in the area i in the time period m are constrained as follows:
Pi,m,VPP,min≤Pi,m,VPP≤Pi,m,VPP,max (29)
Pi,m,VPP,min＝Pi,m,V,AC,min+Pi,m,V,EV,min (30)
Pi,m,VPP,max＝Pi,m,V,AC,max+Pi,m,V,EV,max (31)
step two: and establishing a multi-region virtual power plant coordination optimization scheduling model.
In the period of m, the total active network loss of the power distribution network with N buses is as follows:
Figure BDA0003041925890000131
Figure BDA0003041925890000132
Figure BDA0003041925890000133
in the formula: u shapei,mAnd Uj,mThe voltage of the ith bus and the j bus in the m time period respectively; r isijThe resistance of the feeder line between the ith node and the j node; pi,mAnd Pj,mThe injection active power of the ith bus and the j bus in the m time period is respectively; qi,mAnd Qj,mAnd injecting reactive power of the ith bus and the j bus in the n time period respectively.
And after the virtual power plant is accessed, performing multi-region virtual power plant joint optimization scheduling in the m period by taking the minimum network loss as a target. The scheduling goals are as follows:
Figure BDA0003041925890000134
the constraint is shown in equation (29).
Step three: and solving the m-period multi-region virtual power plant optimization scheduling model by adopting a goblet sea squirt group algorithm.
(3-1) setting the lower bound of the search space to Pm,VPP,max＝{P1,m,VPP,max,P2,m,VPP,max,…,PN,m,VPP,max}, lower bound is: pm,VPP,min＝{P1,m,VPP,min,P2,m,VPP,min,…,PN,m,VPP,minRandomly initializing an L multiplied by N virtual power plant power generation power matrix according to upper and lower bounds
Figure BDA0003041925890000135
Wherein L is the total number of goblet sea squirt groups, and the generating power vector of the virtual power plant of the first goblet sea squirt is:
Figure BDA0003041925890000136
(3-2) obtaining a network loss vector corresponding to each goblet and sea squirt based on the formula (35):
Figure BDA0003041925890000137
and (3-3) sorting the network loss values of the goblet sea squirt groups from small to large, and selecting the virtual power plant power generation power vector corresponding to the goblet sea squirt with the smallest network loss value as a target position.
(3-4) sorting the net loss values corresponding to the remaining L-1 goblet ascidians from small to large, and selecting the front half goblet ascidians as a leader and the rear half goblet ascidians as a follower.
And (3-5) updating the virtual power plant power generation amount corresponding to each area of the leader based on the formula (36), and updating the virtual power plant power generation amount corresponding to each area of the follower based on the formula (38).
Figure BDA0003041925890000141
In the formula:
Figure BDA0003041925890000142
representing the power generated by the virtual power plant in the ith area in the ith individual in the goblet sea squirt population; fiRepresenting the power generation power of the ith area virtual power plant corresponding to the goblet ascidian with the minimum network loss value in the goblet ascidian group; c1, c2 and c3 are all control parameters, wherein c2 and c3 are [0, 1 ]]Random numbers in the range, c1 is the main control parameter, when the random numbers are greater than 1, the algorithm performs global exploration, when the random numbers are less than 1, the algorithm starts local exploration, so that the algorithm performs full search in the first half of iteration, and performs local precise search in the second half of iteration, and the expression of c1 is selected as follows:
Figure BDA0003041925890000143
in the formula: r represents the current iteration number; r represents the maximum number of iterations.
The follower is in a chain shape and follows the motion in sequence, the motion mode accords with the Newton's law of motion, and the update equation is as follows:
Figure BDA0003041925890000144
in the formula:
Figure BDA0003041925890000145
is the power generation power of the ith area virtual power plant of the updated ith follower,
Figure BDA0003041925890000146
and the generated power of the ith regional virtual power plant before updating.
And (3-6) calculating and updating the network loss value of the goblet ascidian group, comparing the updated network loss value of each goblet ascidian with the network loss value corresponding to the target position, and if the network loss value is higher than the network loss value of the target position, selecting the virtual power plant vector of each region corresponding to the goblet ascidian as the new target position.
And (5) repeating the steps (3-4) to (3-6) according to certain iteration times to obtain the optimal power generation power of the virtual power plant in each area.
It will be appreciated by those skilled in the art that various changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the spirit and scope of the invention, and any equivalents thereto, such as those skilled in the art, are intended to be embraced therein.

Claims (7)

1. A coordinated optimization scheduling method for a multi-region virtual power plant is characterized by comprising the following steps: the method comprises the following steps:
(1) analyzing the upper limit and the lower limit of the generated power of the virtual power plant in each area;
(2) establishing a multi-region virtual power plant coordination optimization scheduling model;
(3) and solving the optimized scheduling model by adopting a goblet sea squirt group algorithm.
2. The coordinated optimization scheduling method for the multi-region virtual power plant according to claim 1, wherein the step (1) of analyzing the upper and lower limits of the generated power of each region virtual power plant is performed according to the following steps:
dividing one day into 24 x 60/delta t control periods according to the time slot length delta t; in a power distribution network with N buses, analyzing upper and lower limit powers of virtual power generation of a variable frequency air conditioner cluster and an electric vehicle cluster in an area i corresponding to a bus i, aggregating the variable frequency air conditioner cluster and the electric vehicle cluster to form a virtual power plant of the area i in the area i corresponding to each bus i, and obtaining an analysis model of the power generation power of the virtual power plant of each area.
3. The multi-zone virtual power plant coordination optimization scheduling method according to claim 2, wherein the method for analyzing the upper and lower limits of the power generation power of the variable frequency air conditioner cluster is as follows:
the single variable frequency air conditioner is modeled, the thermodynamic process of the temperature change of an air conditioning room under the action of indoor and outdoor cold and heat sources can be represented by a first-order equivalent thermal parameter model, and the first-order differential equation of the thermodynamic process is as follows:
Figure FDA0003041925880000011
in the formula: t isin(t) is the indoor gas temperature at time t, ° c; r is equivalent thermal resistance, namely the reciprocal of the air heat loss coefficient, DEG C/kW; c is the heat capacity of the indoor gas, kJ/DEG C; t isout(t) is the ambient temperature at time t, DEG C; qacThe refrigerating capacity of the variable frequency air conditioner is kW;
the relationship between the electric power and the refrigerating capacity of the variable frequency air conditioner and the frequency obtained by further simplifying the experimental result is respectively as follows:
Pac＝k1f+l1 (2)
Qac＝k2f+l2 (3)
in the formula: pacIs the electric power of the variable frequency air conditioner, kW; k is a radical of1,l1,k2,l2Is a constant coefficient; f is the frequency of the inverter air conditioner compressor,
p can be obtained from the formulae (2) and (3)acAnd QacThe relationship of (1) is:
Figure FDA0003041925880000021
when the indoor temperature is kept at TiIn time, the required refrigerating capacity is shown as the following formula (1):
Figure FDA0003041925880000022
the electric power of the inverter air conditioner can be obtained by the formulas (4) and (5):
Figure FDA0003041925880000023
when the air conditioner is operated under the load of the air conditioner and Tset(t), the electric power is the power base value, which can be obtained from the formula (6), and the electric power base value is:
Figure FDA0003041925880000024
the virtual power generation power of a single variable frequency air conditioner is as follows:
Pv,ac＝Pac-Pbase (8)
it should be noted that the electric power of the inverter air conditioner load does not increase or decrease without limit, and the inverter air conditioner electric power is [ P ] due to the user comfort constraint and the limitation of the frequency fac,min,Pac,max]Internal change, therefore, in any time interval, Pv,acAll satisfy:
Pv,ac,min≤Pv,ac≤Pv,ac,max (9)
Pv,ac,min＝-(Pac,max-Pbase) (10)
Pv,ac,max＝-(Pac,min-Pbase) (11)
as can be seen from the above analysis, the controllable power of a single air conditioner may change at every moment, but considering that the ambient temperature, the set temperature, and the like are almost unchanged in a period of small control duration, in order to reduce the control difficulty, it can be approximately considered that P within Δ t is approximately equal to P within Δ tv,acIn order to be a constant value,
if the region i has N in total in the time period mACThe variable frequency air conditioner is controllable, and the virtual power generation power of the air conditioner cluster in the area i is as follows:
Figure FDA0003041925880000031
the virtual generated power upper and lower limits of the air conditioner cluster in the area i in the time period m are constrained as follows:
Pi,m,V,AC,min≤Pi,m,V,AC≤Pi,m,V,AC,max (13)
Figure FDA0003041925880000032
Figure FDA0003041925880000033
in the formula: pj,m,v,ac，Pj,m,v,ac,min，Pj,m,v,ac,maxAnd respectively limiting the virtual power generation power of the jth air conditioner in the area ith in the time period m and the upper limit and the lower limit of the virtual power generation power.
4. The multi-region virtual power plant coordination optimization scheduling method according to claim 3, wherein the method for analyzing the upper and lower limits of the generated power of the electric vehicle is as follows:
similarly, firstly, modeling is carried out on a single electric vehicle, and in order to meet the travel requirement of a user, the initial state of charge SOC of the electric vehicle k is used0,iThe required charging time of the available electric vehicle i is as follows:
Figure FDA0003041925880000034
in the formula: SOCE,kIs the expected state of charge of the electric vehicle i; wkIs the battery capacity; p is a radical ofkCharge and discharge power for the same;
during the period that the electric automobile is connected into the charging pile, whether the electric automobile participates in ordered scheduling is determined according to the charging requirement and the residence time of the electric automobile, and the expected residence time of a user k is as follows:
Tstay,k＝(tleave,k-tstart,k)Δt (17)
if m is 1, the remaining stay time of the electric vehicle k in the period m is as follows:
Tstay,k,m＝Tstay,i-(m-tstart,k)Δt (18)
in the formula: t is tleave,iA departure period set for the user; t is tstart,iThe time period for the electric automobile to reach the charging pile is shortened;
according to the state of charge SOC of the electric vehicle k in the time period mn,kThe required charging time of the available electric vehicle k in the time period m is as follows:
Figure FDA0003041925880000041
when T isstay,k,m≤Tneed,k,mWhen the electric automobile does not have the scheduled capacity;
when T isstay,k,m>Tneed,k,mThe electric vehicle has the capability of being dispatched;
whether the electric automobile k has a schedulable space can be judged according to the conditions, for the electric automobile which can be subjected to ordered scheduling, the charging pile can divide the electric automobile cluster into a charging group and a discharging group according to the charge state of the electric automobile in the time period m,
according to the state of charge SOC of the current momentm,kExpected electric quantity SOC preset by vehicle ownerE,kThe values are compared and grouped;
if SOCm,k≤SOCE,kWill satisfy equation (14) and SOCm,k≤SOCE,kThe electric vehicle is arranged in a charging group;
Figure FDA0003041925880000042
if SOCm,k>SOCE,kWill satisfy equation (15) and SOCm,k>SOCE,kThe electric automobile is arranged in a discharge group and is responsible for discharging,
Figure FDA0003041925880000043
assuming that a grid charging electric vehicle set phi and a discharging electric vehicle set psi in a region are as follows:
Φ＝{CHc|c∈NCH} (22)
Ψ＝{DHd|d∈NDH} (23)
in the formula: CH (CH)cIndicating a c-th charging electric automobile in the area; n is a radical ofCHThe number of electric vehicles in a charging group; DHdIndicating the d-th discharging electric automobile in the area; n is a radical ofDHThe number of electric vehicles in a discharge group;
the maximum chargeable load of the charging group vehicle group in the time period m is as follows:
Figure FDA0003041925880000044
pc＝ps (25)
the maximum dischargeable load of the discharging group of cars in the period m is as follows:
Figure FDA0003041925880000051
pd＝-ps (27)
in sum, the electric automobile cluster generating power P in the area i in the time period mi,m,V,EVHas a lower limit of Pi,m,V,EV,min＝-PCH,i,mUpper limit of Pi,m,V,EV,max＝-PDH,i,m。
5. The multi-region virtual power plant coordination optimization scheduling method according to claim 4, characterized in that the analysis method of the upper and lower limits of the generated power of each region virtual power plant is as follows:
in the area i corresponding to each bus i, the variable frequency air conditioner cluster and the electric vehicle cluster are aggregated to form a virtual power plant of the area i, and in sum, the overall output of the virtual power plant of the area i in the time period m is as follows:
Pi,m,VPP＝Pi,m,V,AC+Pi,m,V,EV (28)
the virtual generated power upper and lower limits of the air conditioner cluster in the area i in the time period m are constrained as follows:
Pi,m,VPP,min≤Pi,m,VPP≤Pi,m,VPP,max (29)
Pi,m,VPP,min＝Pi,m,V,AC,min+Pi,m,V,EV,min (30)
Pi,m,VPP,max＝Pi,m,V,AC,max+Pi,m,V,EV,max (31)
6. the multi-region virtual power plant coordination optimization scheduling method according to claim 5, wherein the step (2) of establishing the multi-region virtual power plant coordination optimization scheduling model is performed according to the following steps:
in the period of m, the total active network loss of the power distribution network with N buses is as follows:
Figure FDA0003041925880000052
Figure FDA0003041925880000053
Figure FDA0003041925880000054
in the formula: u shapei,mAnd Uj,mThe voltage of the ith bus and the j bus in the m time period respectively; r isijThe resistance of the feeder line between the ith node and the j node; pi,mAnd Pj,mThe injection active power of the ith bus and the j bus in the m time period is respectively; qi,mAnd Qj,mInjection of i and j buses in n time periodsWork power;
after the virtual power plant is accessed, performing multi-region virtual power plant joint optimization scheduling in the m period by taking the minimum network loss as a target, wherein the scheduling target is as follows:
Figure FDA0003041925880000061
the constraint is shown in equation (29).
7. The multi-zone virtual power plant coordinated optimization scheduling method according to claim 6, wherein the step (3) of solving the m-period multi-zone virtual power plant optimization scheduling model by using the kazun sea squirt group algorithm is performed according to the following steps:
(3-1) setting the lower bound of the search space to Pm,VPP,max＝{P1,m,VPP,max,P2,m,VPP,max,…,PN,m,VPP,max}, lower bound is: pm,VPP,min＝{P1,m,VPP,min,P2,m,VPP,min,…,PN,m,VPP,minRandomly initializing an L multiplied by N virtual power plant power generation power matrix according to upper and lower bounds
Figure FDA0003041925880000062
Wherein L is the total number of goblet sea squirt groups, and the generating power vector of the virtual power plant of the first goblet sea squirt is:
Figure FDA0003041925880000063
(3-2) obtaining a network loss vector corresponding to each goblet and sea squirt based on the formula (35):
Figure FDA0003041925880000064
(3-3) sorting the network loss values of the goblet sea squirt groups from small to large, and selecting the virtual power plant power generation power vector corresponding to the goblet sea squirt with the smallest network loss value as a target position;
(3-4) sorting the network loss values corresponding to the remaining L-1 goblet ascidians from small to large, and selecting the front half goblet ascidians as a leader and the rear half goblet ascidians as a follower;
(3-5) updating the power generation amount of the virtual power plant corresponding to each area of the leader based on an equation (36), and updating the power generation power of the virtual power plant corresponding to each area of the follower based on an equation (38);
Figure FDA0003041925880000071
in the formula:
Figure FDA0003041925880000072
representing the power generated by the virtual power plant in the ith area in the ith individual in the goblet sea squirt population; fiRepresenting the power generation power of the ith area virtual power plant corresponding to the goblet ascidian with the minimum network loss value in the goblet ascidian group; c. C1、c2And c3Are all control parameters, wherein c2And c3Is [0, 1 ]]Random number in the range, c1Is a main control parameter, when the control parameter is more than 1, the algorithm carries out global exploration, when the control parameter is less than 1, the algorithm starts local exploration, so that the algorithm carries out full search in the first half of iteration and local accurate search in the second half of iteration, and c is selected1The expression of (a) is as follows:
Figure FDA0003041925880000073
in the formula: r represents the current iteration number; r represents the maximum number of iterations,
the follower is in a chain shape and follows the motion in sequence, the motion mode accords with the Newton's law of motion, and the update equation is as follows:
Figure FDA0003041925880000074
in the formula:
Figure FDA0003041925880000075
is the power generation power of the ith area virtual power plant of the updated ith follower,
Figure FDA0003041925880000076
generating power of a virtual power plant in the ith area before updating;
(3-6) calculating and updating the network loss value of the goblet ascidian group, comparing the updated network loss value of each goblet ascidian with the network loss value corresponding to the target position, and if the network loss value is higher than the network loss value of the target position, selecting the virtual power plant vector of each area corresponding to the goblet ascidian as the new target position;
Figure FDA0003041925880000077
and (5) repeating the steps (3-4) to (3-6) according to certain iteration times to obtain the optimal power generation power of the virtual power plant in each area.
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Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114050585A (en) * 2021-11-22 2022-02-15 国网上海市电力公司 Coordination control method for forming virtual power plant by utilizing air conditioner load in communication base station
CN114626765A (en) * 2022-05-07 2022-06-14 河南科技学院 Intelligent scheduling method for formation of power lithium battery
CN115203980A (en) * 2022-09-13 2022-10-18 国网湖北省电力有限公司营销服务中心(计量中心) Demand side supply and demand balance control method of localized micro power system
CN117498468A (en) * 2024-01-03 2024-02-02 国网浙江省电力有限公司宁波供电公司 Collaborative optimization operation method for multi-region virtual power plant

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114050585A (en) * 2021-11-22 2022-02-15 国网上海市电力公司 Coordination control method for forming virtual power plant by utilizing air conditioner load in communication base station
CN114626765A (en) * 2022-05-07 2022-06-14 河南科技学院 Intelligent scheduling method for formation of power lithium battery
CN115203980A (en) * 2022-09-13 2022-10-18 国网湖北省电力有限公司营销服务中心(计量中心) Demand side supply and demand balance control method of localized micro power system
CN115203980B (en) * 2022-09-13 2023-01-24 国网湖北省电力有限公司营销服务中心(计量中心) Demand side supply and demand balance control method of localized micro power system
CN117498468A (en) * 2024-01-03 2024-02-02 国网浙江省电力有限公司宁波供电公司 Collaborative optimization operation method for multi-region virtual power plant
CN117498468B (en) * 2024-01-03 2024-05-03 国网浙江省电力有限公司宁波供电公司 Collaborative optimization operation method for multi-region virtual power plant

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