CN112821438A - Optimal scheduling method and system of BSS-containing micro-grid combined system - Google Patents

Optimal scheduling method and system of BSS-containing micro-grid combined system Download PDF

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CN112821438A
CN112821438A CN202110280383.2A CN202110280383A CN112821438A CN 112821438 A CN112821438 A CN 112821438A CN 202110280383 A CN202110280383 A CN 202110280383A CN 112821438 A CN112821438 A CN 112821438A
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battery
constraint
period
bss
power
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赵钰婷
刘柏岩
付小标
崔杨
王铮
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Northeast Electric Power University
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Northeast Dianli 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
    • H02J3/322—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means the battery being on-board an electric or hybrid vehicle, e.g. vehicle to grid arrangements [V2G], power aggregation, use of the battery for network load balancing, coordinated or cooperative battery charging
    • 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
    • H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • 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

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  • Power Engineering (AREA)
  • Electric Propulsion And Braking For Vehicles (AREA)
  • Charge And Discharge Circuits For Batteries Or The Like (AREA)

Abstract

The invention relates to an optimal scheduling method and system of a BSS-containing micro-grid combined system, wherein the method comprises the following steps: constructing a vehicle transfer mechanism model according to the vehicle transfer mode and the vehicle transfer way; determining vehicle transfer compensation cost and the actual number of battery replacement vehicles according to the vehicle transfer mechanism model; constructing an optimized scheduling model of the BSS-containing microgrid combined system by taking the maximum operating yield of the BSS-containing microgrid combined system as an objective function and taking power constraint, total electric quantity constraint, battery state quantity change constraint, charge and discharge machine output limit constraint and battery number constraint as constraint conditions; and performing optimized scheduling according to an optimized scheduling model containing the BSS microgrid combined system to obtain the optimized vehicle charging and battery replacing time and the number of vehicles for charging and battery replacing. The invention can improve the utilization efficiency of the combined system to energy.

Description

Optimal scheduling method and system of BSS-containing micro-grid combined system
Technical Field
The invention relates to the technical field of energy utilization, in particular to an optimal scheduling method and system for a BSS-containing micro-grid combined system.
Background
And the demand response is applied to the micro-grid dispatching, and the load curve can be optimized by changing the working time of the flexible load. However, in the existing research, the existing technologies that take electric vehicles as transferable loads to participate in the micro-grid dispatching of a Base Station System (BSS) are few. The combined system comprises a fan, a Micro Turbine (MT), a BSS and the like and is connected with an external power grid. The energy management center can provide charging energy for the battery rack in a mode of preferentially selecting the optimal cost from fan power generation, MT power generation and power grid power purchase, and the BSS can provide battery replacement service for the electric automobile and can also utilize electricity price type demand response to conduct power transaction with an external power grid so as to obtain additional benefits. In the existing research, the vehicle with changed battery replacement time is not properly compensated, and the optimal adjustment of a battery replacement load curve is not realized, so that the utilization rate of energy in a combined system is low.
Disclosure of Invention
The invention aims to provide an optimal scheduling method and system of a BSS-containing microgrid combined system so as to improve the utilization efficiency of the combined system to energy.
In order to achieve the purpose, the invention provides the following scheme:
an optimal scheduling method of a BSS-containing microgrid combined system comprises the following steps:
constructing a vehicle transfer mechanism model according to the vehicle transfer mode and the vehicle transfer way;
determining vehicle transfer compensation cost and the number of actual battery replacement vehicles according to the vehicle transfer mechanism model;
determining a power constraint based on the first difference and the second difference; the first difference is a battery residual capacity difference; the second difference is the difference between the input power and the output power of the BSS-containing microgrid joint system;
determining total electric quantity constraint according to the total electric quantity of the battery;
determining a battery state quantity change constraint according to the battery state; the battery state includes: charging, full, discharging, and waiting to charge;
determining output limit constraints of the charge and discharge machine according to the power of the charge and discharge machine, the number of the charged batteries and the number of the discharged batteries;
determining battery number constraint according to the actual number of the battery replacement vehicles and the number of fully charged batteries;
constructing an optimized scheduling model of the BSS-containing microgrid combined system by taking the maximum operating yield of the BSS-containing microgrid combined system as an objective function and taking the power constraint, the total electric quantity constraint, the battery state quantity change constraint, the charge and discharge machine output limit constraint and the battery number constraint as constraint conditions; the operation income of the BSS-containing microgrid combined system comprises: the system sells electricity income, trades electricity income, fan cost, miniature gas turbine cost to the electric wire netting, the said vehicle shifts the compensation cost, battery cost, purchases electricity cost and system construction cost from the external electric wire netting;
and performing optimized scheduling according to the optimized scheduling model of the BSS-containing micro-grid combined system to obtain the optimized time for charging and replacing the electric power of the vehicle and the number of the vehicles for charging and replacing the electric power.
Optionally, the determining the vehicle transfer compensation cost and the actual number of battery replacement vehicles according to the vehicle transfer mechanism model specifically includes:
determining the actual number of battery replacement vehicles by using the following formula:
Figure BDA0002978038900000021
wherein, Plnew,tFor the actual number of battery change vehicles, Pl,tThe number of the electric vehicles is changed for the original plan,
Figure BDA0002978038900000022
the number of vehicles is shifted i cycles ahead for cycle t to cycle t-i,
Figure BDA0002978038900000023
advancing the number of vehicles from cycle t + i to cycle t, wherein i > 0,
Figure BDA0002978038900000024
the number of vehicles is transferred for the delay from period t to period t + i,
Figure BDA0002978038900000025
transferring the number of vehicles to a period T after the period T-i is delayed, wherein i is greater than 0, and T is the total number of periods;
determining a vehicle transfer compensation cost using the following equation:
Figure BDA0002978038900000026
wherein P is total transfer compensation; dtq,iPrice compensation is carried out for i period transfer in advance; dyh,iThe price is compensated for the delayed i-cycle transfer.
Optionally, the determining the power constraint according to the first difference and the second difference specifically includes:
the power constraint is determined using the following equation:
Figure BDA0002978038900000031
wherein, BtIs the total electric quantity of the battery pack in the station in the period t, Bt+1Is the total electric quantity of the battery pack in the station in the period of t +1, EbRated capacity, P, for a single cellw,tThe power flowing from the fan to the BSS in the t period;
Figure BDA0002978038900000032
is the power, P, output by the nth MT to the charger during the t periodbuy,tFor the electricity purchasing power, P, of the system in the t periodlcar,tNumber of changed taxis in t period, SmaxThe upper limit of the state of charge of the storage battery; ssocRemaining battery SOC, P when replacing battery for electric vehiclesell,tAnd the power sold to the power grid by the combined system is in the period t.
Optionally, the determining the total power constraint according to the total power of the battery specifically includes:
the total charge constraint is determined using the following equation:
(Nc,t+Nd,t+Nw,t)EbSmin+Nr,tEbSmax≤Bt≤Nw,tEbSmin+(Nc,t+Nd,t+Nr,t)EbSmax
wherein N isc,tNumber of batteries being charged for t period, Nd,tNumber of cells being discharged for t period, Nw,tNumber of batteries waiting to be charged for t period, Nr,tThe number of BSS full-charge batteries in t period, SminThe lower limit of the SOC of the battery is c, the state of charge is w, the state of charge is waiting, d is the state of discharge, and r is the full state.
Optionally, the determining the constraint of the change of the number of battery states according to the battery states specifically includes:
determining a battery state quantity change constraint using the following equation:
Figure BDA0002978038900000033
wherein M is the total number of batteries in BSS, Nc,t-1Number of batteries being charged for t-1 cycle, Nd,t-1Number of cells being discharged for t-1 cycle, Nw,t-1Number of batteries waiting to be charged for t-1 cycle, Ncar,t-1And the total number of the vehicles participating in the battery replacement in the scheduling day of the t-1 period.
Optionally, determining the output limit constraint of the charge and discharge machine according to the charge and discharge machine power, the number of the charged batteries and the number of the discharged batteries specifically includes:
determining a charge-discharge machine output limit constraint using the following equation:
Figure BDA0002978038900000034
wherein k iscIs the total number of chargers, Pc,maxUpper limit of power flowing in charge and discharge machine, Pc,tAnd charging power for the charge and discharge machine.
Optionally, determining a battery number constraint according to the actual number of battery replacement vehicles and the number of full-charge batteries specifically includes:
the number of cells constraint is determined using the following equation:
Plnew,t≤Nr,t
wherein N isr,tThe number of BSS full-charge batteries in t period, Plnew,tThe number of the actual battery replacement vehicles is shown.
An optimized scheduling system containing a BSS microgrid combined system comprises:
the vehicle transfer mechanism model building module is used for building a vehicle transfer mechanism model according to the vehicle transfer mode and the vehicle transfer way;
the vehicle transfer compensation cost and actual battery replacement vehicle number determining module is used for determining the vehicle transfer compensation cost and the actual battery replacement vehicle number according to the vehicle transfer mechanism model;
a power constraint determination module for determining a power constraint based on the first difference and the second difference; the first difference is a battery residual capacity difference; the second difference is the difference between the input power and the output power of the BSS-containing microgrid combined system;
the total electric quantity constraint determining module is used for determining total electric quantity constraint according to the total electric quantity of the battery;
the battery state quantity change constraint determining module is used for determining battery state quantity change constraints according to the battery states; the battery state includes: charging, full, discharging, and waiting to charge;
the charge and discharge machine output limit restriction determining module is used for determining the charge and discharge machine output limit restriction according to the charge and discharge machine power, the number of the charged batteries and the number of the discharged batteries;
the battery number constraint determining module is used for determining battery number constraint according to the actual battery replacement vehicle number and the full-charge battery number;
the optimization scheduling model building module is used for building an optimization scheduling model of the BSS-containing microgrid combined system by taking the maximum operating yield of the BSS-containing microgrid combined system as an objective function and taking the power constraint, the total electric quantity constraint, the battery state quantity change constraint, the charge and discharge machine output limit constraint and the battery number constraint as constraint conditions; the operation income of the BSS-containing microgrid combined system comprises: the system sells electricity income, trades electricity income, fan cost, miniature gas turbine cost to the electric wire netting, the said vehicle shifts the compensation cost, battery cost, purchases electricity cost and system construction cost from the external electric wire netting;
and the optimized scheduling module is used for performing optimized scheduling according to the optimized scheduling model of the BSS-containing micro-grid combined system to obtain the optimized time for charging and exchanging the battery and the number of vehicles for charging and exchanging the battery.
Optionally, the module for determining the vehicle transfer compensation cost and the actual battery replacement vehicle number specifically includes:
an actual battery replacement vehicle number determination unit for determining an actual battery replacement vehicle number using the following formula:
Figure BDA0002978038900000051
wherein, Plnew,tFor the actual number of battery change vehicles, Pl,tThe number of the electric vehicles is changed for the original plan,
Figure BDA0002978038900000052
the number of vehicles is shifted i cycles ahead for cycle t to cycle t-i,
Figure BDA0002978038900000053
advancing the number of vehicles from cycle t + i to cycle t, wherein i > 0,
Figure BDA0002978038900000054
the number of vehicles is transferred for the delay from period t to period t + i,
Figure BDA0002978038900000055
transferring the number of vehicles to a period T after the period T-i is delayed, wherein i is greater than 0, and T is the total number of periods;
a vehicle transfer compensation cost determination unit for determining a vehicle transfer compensation cost using the following formula:
Figure BDA0002978038900000056
wherein P is total transfer compensation; dtq,iPrice compensation is carried out for i period transfer in advance; dyh,iThe price is compensated for the delayed i-cycle transfer.
Optionally, the power constraint determining module specifically includes:
a power constraint determining unit for determining a power constraint using the following formula:
Figure BDA0002978038900000057
wherein, BtIs the total electric quantity of the battery pack in the station in the period t, Bt+1Is the total electric quantity of the battery pack in the station in the period of t +1, EbRated capacity, P, for a single cellw,tThe power flowing from the fan to the BSS in the t period;
Figure BDA0002978038900000058
is the power, P, output by the nth MT to the charger during the t periodbuy,tFor the electricity purchasing power, P, of the system in the t periodlcar,tNumber of changed taxis in t period, SmaxThe upper limit of the state of charge of the storage battery; ssocRemaining battery SOC, P when replacing battery for electric vehiclesell,tAnd the power sold to the power grid by the combined system is in the period t.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the invention provides an optimal scheduling method and system for a BSS-containing micro-grid combined system. And vehicle transfer compensation is carried out on the vehicle with the transfer power time, so that the power transfer load curve of the BSS-containing microgrid combined system is changed, and the energy utilization rate of the BSS-containing microgrid combined system is improved.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
Fig. 1 is a flowchart of an optimal scheduling method of a BSS-included microgrid integrated system according to the present invention;
FIG. 2 is a schematic view of a vehicle transfer mode of operation;
FIG. 3 is TlimitThe vehicle transfer dynamic model schematic diagram is 1;
fig. 4 is a schematic structural diagram of a BSS-containing microgrid integrated system;
FIG. 5 is a schematic diagram of the model-load and the output conditions of various types of units;
FIG. 6 is a schematic diagram of the output conditions of model two loads and various types of units;
FIG. 7 is a schematic diagram of the number of discharged batteries and power sold for model one and model two;
FIG. 8 is a schematic view of a 3-scheme actual load and transfer vehicle;
FIG. 9 is a schematic illustration of transfer vehicle and system benefits at different transfer offsets;
fig. 10 is a schematic diagram of an optimized scheduling system of a BSS-included microgrid integrated system provided by the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention aims to provide an optimal scheduling method and system of a BSS-containing microgrid combined system so as to improve the utilization efficiency of the combined system to energy.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
Example one
As shown in fig. 1, the optimal scheduling method for a BSS-containing piconet integrated system provided in this embodiment includes:
step 101: and constructing a vehicle transfer mechanism model according to the vehicle transfer mode and the vehicle transfer way. The vehicle mode comprises an advancing mode and a delaying mode, and the vehicle transfer mode comprises a transfer-in mode and a transfer-out mode.
Step 102: and determining the vehicle transfer compensation cost and the actual battery replacement vehicle number according to the vehicle transfer mechanism model. In practical application, step 102 specifically includes:
determining the actual number of battery replacement vehicles by using the following formula:
Figure BDA0002978038900000071
wherein, Plnew,tFor the actual number of battery change vehicles, Pl,tThe number of the electric vehicles is changed for the original plan,
Figure BDA0002978038900000072
the number of vehicles is shifted i cycles ahead for cycle t to cycle t-i,
Figure BDA0002978038900000073
advancing the number of vehicles from cycle t + i to cycle t, wherein i > 0,
Figure BDA0002978038900000074
the number of vehicles is transferred for the delay from period t to period t + i,
Figure BDA0002978038900000075
and transferring the number of vehicles for delaying the period T-i to the period T, wherein i is greater than 0, and T is the total number of periods.
Determining a vehicle transfer compensation cost using the following equation:
Figure BDA0002978038900000076
wherein P is total transfer compensation; dtq,iPrice compensation is carried out for i period transfer in advance; dyh,iThe price is compensated for the delayed i-cycle transfer.
Step 103: determining a power constraint based on the first difference and the second difference; the first difference is a battery residual capacity difference; and the second difference is the difference between the input power and the output power of the BSS-contained microgrid joint system. Step 103, specifically comprising:
the power constraint is determined using the following equation:
Figure BDA0002978038900000077
wherein, BtIs the total electric quantity of the battery pack in the station in the period t, Bt+1Is the total electric quantity of the battery pack in the station in the period of t +1, EbRated capacity, P, for a single cellw,tThe power flowing from the fan to the BSS in the t period;
Figure BDA0002978038900000081
is the power, P, output by the nth MT to the charger during the t periodbuy,tFor the electricity purchasing power, P, of the system in the t periodlcar,tNumber of changed taxis in t period, SmaxThe upper limit of the state of charge of the storage battery; ssocRemaining battery SOC, P when replacing battery for electric vehiclesell,tAnd the power sold to the power grid by the combined system is in the period t.
Step 104: and determining a total electric quantity constraint according to the total electric quantity of the battery. Step 104, specifically comprising:
the total charge constraint is determined using the following equation:
(Nc,t+Nd,t+Nw,t)EbSmin+Nr,tEbSmax≤Bt≤Nw,tEbSmin+(Nc,t+Nd,t+Nr,t)EbSmax
wherein N isc,tNumber of batteries being charged for t period, Nd,tNumber of cells being discharged for t period, Nw,tNumber of batteries waiting to be charged for t period, Nr,tThe number of BSS full-charge batteries in t period, SminThe lower limit of the SOC of the battery is c, the state of charge is w, the state of charge is waiting, d is the state of discharge, and r is the full state.
Step 105: determining a battery state quantity change constraint according to the battery state; the battery state includes: charging, full, discharging, and waiting to charge; step 105, specifically comprising:
determining a battery state quantity change constraint using the following equation:
Figure BDA0002978038900000082
wherein M is the total number of batteries in BSS, Nc,t-1Number of batteries being charged for t-1 cycle, Nd,t-1Number of cells being discharged for t-1 cycle, Nw,t-1Number of batteries waiting to be charged for t-1 cycle, Ncar,t-1And the total number of the vehicles participating in the battery replacement in the scheduling day of the t-1 period.
Step 106: and determining the output limit constraint of the charge and discharge machine according to the power of the charge and discharge machine, the number of the charged batteries and the number of the discharged batteries. Step 106, specifically comprising:
determining a charge-discharge machine output limit constraint using the following equation:
Figure BDA0002978038900000083
wherein k iscIs the total number of chargers, Pc,maxUpper limit of power flowing in charge and discharge machine, Pc,tAnd charging power for the charge and discharge machine.
Step 107: and determining battery number constraint according to the actual number of the battery replacement vehicles and the number of the fully charged batteries. Step 107, specifically including:
the number of cells constraint is determined using the following equation:
Plnew,t≤Nr,t
wherein N isr,tThe number of BSS full-charge batteries in t period, Plnew,tThe number of the actual battery replacement vehicles is shown.
Step 108: constructing an optimized scheduling model of the BSS-containing microgrid combined system by taking the maximum operating yield of the BSS-containing microgrid combined system as an objective function and taking the power constraint, the total electric quantity constraint, the battery state quantity change constraint, the charge and discharge machine output limit constraint and the battery number constraint as constraint conditions; the operation income of the BSS-containing microgrid combined system comprises: the system sells electricity income, trades electric income, fan cost, micro-gas turbine cost to the electric wire netting, the vehicle shifts compensation cost, battery cost, purchases electric cost and system construction cost from the external electric wire netting.
Step 109: and performing optimized scheduling according to the optimized scheduling model of the BSS-containing micro-grid combined system to obtain the optimized time for charging and replacing the electric power of the vehicle and the number of the vehicles for charging and replacing the electric power.
Example two
The embodiment provides a specific mode of an optimal scheduling method of a combined system containing a BSS (base station system) microgrid, and the optimal scheduling method is characterized in that a microgrid combined system power generation mechanism, an electric taxi participation scheduling mechanism, a BSS electric energy storage mechanism and a vehicle transfer mechanism are gradually introduced based on two aspects of smooth load curves and improvement of system economy, an optimal scheduling model of the combined system containing the BSS microgrid considering the vehicle transfer mechanism is constructed, a standby loss penalty cost is added into a target function, load completion constraints are added into constraint conditions to serve as quantitative indexes, and effectiveness of the optimal scheduling model provided by the embodiment on the smooth load curves and improvement of system economic operation is analyzed. Through comparing MT, wind and the electricity generation cost who purchases the electricity three from the major network and carrying out preferred energy charging, introduce the vehicle and shift the mechanism, utilize BSS group battery as energy storage system for electric automobile deposit electric power energy, through carrying out appropriate compensation to the vehicle that shifts the electricity conversion time, borrow this to change and trade electric load curve, make BSS formulate more excellent charging and conversion plan again, scientific and reasonable, the suitability is strong, and the effect is good, can improve energy utilization efficiency and operation economy. The method comprises the following steps:
step one, constructing a vehicle transfer mechanism model:
as shown in fig. 2, the vehicle transfer is divided into two ways, advance and retard, and the transfer direction is divided into 2 ways of turning in and turning out. Taking the example of the early transition of cycle t in FIG. 2, the transition is first advanced by 1 cycle to cycle t-1
Figure BDA0002978038900000101
The vehicles are moved to the t-i cycle in advance by i cycles in the same way
Figure BDA0002978038900000102
Vehicle, until t-1 cycle shift ahead of cycle 1
Figure BDA0002978038900000103
And (4) vehicles.
Similarly, in the other advanced transition mode from the period t to the period t, the period t + i (i > 0) is advanced to the period t
Figure BDA0002978038900000104
A vehicle; in the mode of delaying the transfer of the period t to other periods, the period t is delayed to be transferred to the period t + i
Figure BDA0002978038900000105
A vehicle; in other modes of delaying the transition of the period to the period t, the period t-i (i > 0) is delayed to the period t
Figure BDA0002978038900000106
And (4) vehicles.
Load P of period t after completion of all transferslnew,tNamely, the calculation formula of the number of the actual battery replacement vehicles is formula (1):
Figure BDA0002978038900000107
in the formula: t is the total number of periods;
Figure BDA0002978038900000108
number of vehicles transferred 1 cycle ahead for cycle t-1, Pl,tThe number of the electric vehicles is changed for the original plan,
Figure BDA0002978038900000109
the number of vehicles is transferred for the delay from period t-i to period t.
There is a periodic upper limit for the vehicle to transition. T shown in FIG. 3limitWhen the period is T, the vehicle transfers the dynamic model schematic diagramlimitWhile, the period T can only be T before and afterlimitVehicle transfer is performed for each cycle. Meanwhile, the owner who carries out the vehicle transfer is given appropriate compensation, and the compensation amount is based on the transfer cycle number, and the expression is as follows:
Figure BDA00029780389000001010
in the formula: p is total transfer compensation; dtq,iPrice compensation is carried out for i period transfer in advance; dyh,iThe price is compensated for the delayed i-cycle transfer.
Step two, constructing an optimal scheduling model of the BSS-containing microgrid combined system:
an objective function:
the dispatching model takes the maximization of the operation income of the combined system as an objective function, and comprises the income C of the system selling electricity to the power grid1(ii) a Battery replacement income C2(ii) a Cost of the fan C3(ii) a MT cost C4(ii) a Vehicle transfer compensation cost C5(ii) a Cost of battery C6(ii) a Cost of purchasing electricity from external power grid C7(ii) a Cost of system construction C8；
max Psum＝C1+C2-C3-C4-C5-C6-C7-C8 (3)
The combined system utilizes the electricity price type demand response to conduct electric power transaction with the main network to obtain the income:
Figure BDA0002978038900000111
in the formula: dsell,tSelling electricity to the power grid for the system at a time-sharing unit price; psell,tAnd the power sold to the power grid by the combined system is in the period t.
The BSS provides a service for replacing the battery for the electric automobile and charges the battery differential charge and fixed service charge to the BSS:
Figure BDA0002978038900000112
in the formula: n is a radical ofcarThe total number of the vehicles participating in the battery replacement within the dispatching day; dserveCharging a service fee for replacing the battery of a single automobile; dfare,tThe differential electricity price of the battery charged to the vehicle owner for the system; pfare,tAnd the electric quantity is supplemented for the electric automobile by replacing the battery in the t period.
The fan cost comprises operation and maintenance cost and wind abandon punishment cost generated by fan abrasion:
Figure BDA0002978038900000113
in the formula: q. q.swThe cost of wind abandonment is unit; qw,tAbandoning wind power in t period; cwThe unit fan maintenance cost; pw,tThe power flowing from the fan to the BSS in the t period; w is the wait state of charge.
The MT combusts fuels such as natural gas or methane and the like to generate electric energy, and the output power is in direct proportion to the consumed fuel quantity; the start-up operating costs and fuel costs arise during operation:
Figure BDA0002978038900000114
in the formula: n is a radical ofmtIs the total number of MT; u. ofnIs MTnThe boot cost; kn,tThe parameter is a unit state variable, the parameter is a 0-1 variable, 0 is a shutdown state, and 1 is a startup state;
Figure BDA0002978038900000115
is MTnPower output to a charger at a period t; v. ofnAnd wnIs MTnThe consumption coefficient of (c).
When the vehicle is transferred, the proper amount of compensation is given to the owner of the electric automobile, and the calculation formula is shown as the formula (2).
The battery cost includes a battery depreciation cost and a battery backup loss penalty cost. The storage battery will produce a certain loss of life during the charging and discharging process of continuous cycle. The service life of the storage battery is shortened due to over charge and discharge, and the application effect is best when the single charge and discharge depth is controlled to be 50% -70%.
The relationship between the cycle life and depth of discharge of a battery can be expressed as:
Ncir＝f(Ssoc)＝b0+b1(Smax-Ssoc)+b2(Smax (8)
-Ssoc)2+…+bn(Smax-Ssoc)n
in the formula: n is a radical ofcirThe number of times of circulation of the storage battery is; smaxIs the upper limit of the state of charge (SOC) of the battery; ssocThe remaining SOC of the battery when the battery is replaced for the electric automobile; b0、b1…bnIs a cycle characteristic parameter.
The relationship between the number of battery cycles and the loss cost can be represented by equation (9):
Figure BDA0002978038900000121
in the formula: cjjThe cost is lost for the single circulation of the storage battery; cbatThe purchase cost of the single battery.
In order to deal with the deviation of the actual load and the predicted load of the electric automobile, a standby battery is arranged in the system to ensure the stability of the BSS battery replacement service. The backup battery is set to 30% of the predicted load. Insufficient parts will generate spare miss penalty costs:
Nbycf,t＝Plcar,t+ceil,t-Nr,t (10)
Figure BDA0002978038900000122
in the formula: n is a radical ofbycf,tThe number of the spare batteries in the t period is the number of the spare batteries in the t period; plcar,tThe number of the taxi battery replacement in the t period; c. Ceil,tThe number of standby batteries of the power conversion load in the t period; n is a radical ofr,tThe number of BSS full batteries in t period; cbyPenalizing costs for unit spare misses.
When the internal energy of the system is not enough to supply to a charger, electricity can be purchased from an external power grid, and the electricity purchasing price is a time-of-use electricity price:
Figure BDA0002978038900000123
in the formula: cbuy,tThe unit price of time-sharing electricity purchasing; pbuy,tThe power purchasing power of the system in t period is realized.
The system construction cost includes daily average lot lease fees CrjzlDaily wage CrjgzAverage daily equipment cost CrjsbWaiting for a fixed fee;
C8＝Crizl+Crigz+Crjsb (13)
constraint conditions:
the difference value of the residual electric quantity of the front period and the rear period in the storage battery pack is equal to the difference value of the input power and the output power of the combined system;
Figure BDA0002978038900000131
in the formula: b istThe total electric quantity of the battery pack in the station is t period; b ist+1The total electric quantity of the battery pack in the station in the period of t + 1; ebThe rated capacity of the single battery is obtained.
The total electric quantity of the BSS storage battery has upper and lower limits:
(Nc,t+Nd,t+Nw,t)EbSmin+Nr,tEbSmax≤Bt≤Nw,tEbSmin+(Nc,t+Nd,t+Nr,t)EbSmax (15)
in the formula: n is a radical ofc,tThe number of batteries being charged for the t cycle; n is a radical ofr,tThe number of fully charged batteries for the t period; n is a radical ofd,tThe number of cells being discharged for t cycles; n is a radical ofw,tThe number of batteries waiting to be charged for t cycles; sminIs the lower limit of the SOC of the storage battery.
The battery status in the BSS is 4: during charging, full charging, discharging and waiting for charging, the number of various states changes are constrained as follows:
Figure BDA0002978038900000132
wherein M is the total number of batteries in BSS.
A single charging and discharging machine can only charge or discharge a single storage battery at the same time, and each charging and discharging machine has output limit constraint. The charging power of the flow direction storage battery is not more than the product of the power of the charge-discharge machine and the number of the charged batteries, and the power sold by the system to the power grid is not more than the product of the power of the charge-discharge machine and the number of the discharged batteries:
Figure BDA0002978038900000133
in the formula: k is a radical ofcThe total number of the chargers is; pc,maxIs the upper limit of the charge and discharge motor flow power.
In order to enable the BSS to meet the battery replacement requirement, the number of fully charged batteries should be not less than the number of actual battery replacement vehicles in any period:
Plnew,t≤Nr,t (18)
according to the unit coupling relationship shown in fig. 4, a 10-node microgrid system is adopted to construct an embodiment, and the calculation conditions of the embodiment are described as follows: the scheduling period is 24 hours, and the unit scheduling time interval is 1 hour; the system comprises 1 fan with the capacity of 250kW and 2 micro gas turbines, and the rated capacities of the 2 micro gas turbines are respectively 100kW for MT1 and 80kW for MT 2.
In order to verify the influence of adding the electric taxi to cooperate with the private car to participate in dispatching on the economy of the combined system in the battery replacement load, the comparison of two models including only the private car and the electric taxi to assist the private car to participate in dispatching is carried out.
Model one: 100 private cars participate in power change;
model two: 50 private cars and 50 electric taxis participate in the battery replacement.
Table 1 is a comparative table of the results of two model calculations, as shown in table 1. The total yield of the two models is improved by 14.6 percent relative to the model.
TABLE 1 comparison of two model calculation results
Figure BDA0002978038900000141
The first model battery replacement load and the system output condition are shown in fig. 5, and because the wind abandon cost and the fan maintenance cost are low, the power supply of the charging and discharging motor in the BSS is supplied by the fan firstly, except that the wind abandon is caused by the system maintenance in the 1 st hour, and the wind power is completely utilized in other time. Since the electricity purchase price from the power grid in the valley period is lower than the MT2 unit electricity generation cost and higher than the MT1 unit electricity generation cost, the system preferentially uses the MT1 to supply electricity under the condition that the wind electricity is fully utilized, and then purchases electricity from the external power grid.
From 8 o ' clock, the electricity purchase price is increased to the ordinary period price, which is higher than the MT2 unit electricity generation cost, the system preferentially uses the fan and the MT to supply electricity, and the MT2 outputs partial power at 7 o ' clock and fully sends the electricity after 8 o ' clock because the MT has the upper limit of the power climbing. Due to the fact that the power change peaks occur in the morning and evening at 8: 00-10: 00 and 18: 00-20: 00, the system purchases a large amount of power from the power grid at the usual time, the power purchase from the power grid is stopped at the peak time, all the power is used for supplying vehicles to replace batteries, and the peak-valley power price difference cannot be fully utilized to trade with the power grid. Meanwhile, in the peak hours of morning and evening, all fully charged batteries in the model I are completely used for replacement service, so that the spare batteries cannot be kept, and the spare battery loss penalty cost is higher than that of the model II.
The charging load and the system output situation of the model two are shown in fig. 6, the charging composition and the output sequence of the model two system are the same as those of the model one, but the peak-valley difference of the optimized charging load relative to the model one is reduced due to the addition of the electric taxi. Due to the reduction of the early peak intensity of the second load of the model, the system purchases less power from the power grid at 8: 00-10: 00, and purchases a large amount of power at the utilization valley time of 4: 00. This makes the system more rationally utilize the electricity price charging during millet to reduce and purchase the electric quantity at ordinary times section, make model two purchase the electricity cost lower than model one.
The number of discharged batteries and the electricity selling power before and after optimization are shown in fig. 7, the peak of the load in the morning and at the evening is partially overlapped with the peak of the electricity price sold to the power grid, and most of the electricity of the model I and the model II is sold at the peak of the electricity price. Because the system of the model II has more electricity in the peak period, the electricity sold by the model II is larger than that of the model I, and the total income is improved by 14.6 percent.
To verify the effectiveness of the transfer mechanism of the joining vehicle, a comparison of the different schemes is made:
scheme one and model two.
And in the second scheme, the vehicle is allowed to transfer 1 time in advance on the basis of the first scheme, and the transfer compensation is set to be 5 yuan/vehicle.
And a third scheme allows the vehicle to be delayed to transfer for 1 time on the basis of the first scheme, and the transfer compensation is set to be 5 yuan/vehicle.
And a fourth scheme, the vehicle (without direction limitation) is allowed to shift 1 time on the basis of the first scheme, and the shift compensation is set to be 5 yuan/vehicle.
Table 2 is a detailed operational benefit table of the results of the calculations after optimization of the 4 schemes, as shown in table 2. The yield of the scheme II, the scheme III and the scheme IV is respectively improved by 1.35 percent, 0.81 percent and 2.19 percent compared with the yield of the scheme I.
Table 2 lists the detailed operating revenue (dollars) for the four scheduling schemes.
TABLE 2 detailed operational benefit table of the example results after optimization of 4 schemes
Figure BDA0002978038900000151
Figure BDA0002978038900000161
The battery replacement load curves and the transfer vehicles of the second scheme, the third scheme and the fourth scheme are shown in fig. 8, and the second scheme transfers 2 vehicle numbers in advance at 8:00 and 11:00 respectively. Since these 2 times are the power rate valley period boundary and the power rate peak period boundary, respectively. The combined system transfers part of vehicles to a time period with relatively low electricity purchasing price under the constraint of the system battery number and the constraint of a charger, thereby effectively reducing the MT gas cost, improving the tradable electric quantity of the storage battery pack in the peak time period and correspondingly improving the system electricity selling income. The third scheme is the same as the second scheme in principle, and the 1 train number and the 2 train number are respectively transferred in a delayed mode by utilizing the peak mean-time section valence difference of 16 points and 22 points.
Because the flexibility of the transfer direction of the scheme four is increased, the scheme four can simultaneously utilize the advantages of early transfer and late transfer, and the total income is improved by 0.85 percent and 1.38 percent compared with the scheme two and the scheme three by 5 times of early transfer and 2 times of late transfer.
As shown in fig. 9, when the unit transfer compensation is less, the system preferentially selects the transfer vehicle to increase the total amount of electricity of the system during the time period of higher electricity price, and obtains more benefits through the electricity price difference.
When the transfer compensation is gradually increased, the vehicle transfer is stopped due to the fact that the benefit obtained by the electric price difference is smaller than the transfer compensation in a part of the time period, and the system benefit is linearly reduced along with the increase of the transfer compensation. When the compensation is 15-18 yuan/car, the transfer train number is further reduced. Until the transfer compensation increases to 19 dollars/vehicle, the system no longer performs vehicle transfers.
As can be seen from table 2, the scheduling method provided in this embodiment increases system stability and battery swapping flexibility, and the results of the examples show that the total profit of the second, third, and fourth schemes is increased by 1.35%, 0.81%, and 2.19% compared with the schemes, thereby verifying the superiority of applying the vehicle transfer mechanism.
In addition, the vehicle transfer mechanism provided by the embodiment can effectively optimize the battery replacement load curve and reduce the power grid dispatching pressure.
EXAMPLE III
As shown in fig. 10, the optimal scheduling system of a BSS-microgrid integrated system provided in this embodiment includes:
a vehicle transfer mechanism model building module 1001, configured to build a vehicle transfer mechanism model according to a vehicle transfer manner and approach.
A vehicle transfer compensation cost and actual battery replacement vehicle number determining module 1002, configured to determine a vehicle transfer compensation cost and an actual battery replacement vehicle number according to the vehicle transfer mechanism model.
A power constraint determining module 1003, configured to determine a power constraint according to the first difference and the second difference. The first difference is a battery residual capacity difference; and the second difference is the difference between the input power and the output power of the BSS-contained microgrid joint system.
And a total power constraint determining module 1004 configured to determine a total power constraint according to the total battery power.
A battery state quantity change constraint determining module 1005, configured to determine a battery state quantity change constraint according to the battery state; the battery state includes: charging, full, discharging, and waiting to charge.
And a charge and discharge machine output limit constraint determining module 1006, configured to determine a charge and discharge machine output limit constraint according to the charge and discharge machine power, the number of charged batteries, and the number of discharged batteries.
And a battery number constraint determining module 1007, configured to determine a battery number constraint according to the actual number of battery swapping vehicles and the number of fully charged batteries.
The optimal scheduling model building module 1008 is used for building an optimal scheduling model of the BSS-containing microgrid combined system by taking the maximum operating yield of the BSS-containing microgrid combined system as an objective function and taking the power constraint, the total electric quantity constraint, the battery state quantity change constraint, the charge and discharge machine output limit constraint and the battery number constraint as constraint conditions; the operation income of the BSS-containing microgrid combined system comprises: the system sells electricity income, trades electric income, fan cost, micro-gas turbine cost to the electric wire netting, the vehicle shifts compensation cost, battery cost, purchases electric cost and system construction cost from the external electric wire netting.
And an optimized scheduling module 1009, configured to perform optimized scheduling according to the optimized scheduling model of the BSS-containing microgrid combined system, so as to obtain optimized time for charging and swapping the vehicles and the number of the vehicles for charging and swapping the vehicles.
In practical application, the module 1002 for determining the vehicle transfer compensation cost and the actual battery replacement vehicle number specifically includes:
an actual battery replacement vehicle number determination unit for determining an actual battery replacement vehicle number using the following formula:
Figure BDA0002978038900000171
wherein, Plnew,tFor the actual number of battery change vehicles, Pl,tThe number of the electric vehicles is changed for the original plan,
Figure BDA0002978038900000172
the number of vehicles is shifted i cycles ahead for cycle t to cycle t-i,
Figure BDA0002978038900000181
advancing the number of vehicles from cycle t + i to cycle t, wherein i > 0,
Figure BDA0002978038900000182
the number of vehicles is transferred for the delay from period t to period t + i,
Figure BDA0002978038900000183
and transferring the number of vehicles for delaying the period T-i to the period T, wherein i is greater than 0, and T is the total number of periods.
A vehicle transfer compensation cost determination unit for determining a vehicle transfer compensation cost using the following formula:
Figure BDA0002978038900000184
wherein P is total transfer compensation; dtq,iPrice compensation is carried out for i period transfer in advance; dyh,iThe price is compensated for the delayed i-cycle transfer.
In practical reference, the power constraint determining module 1003 specifically includes:
a power constraint determining unit for determining a power constraint using the following formula:
Figure BDA0002978038900000185
wherein, BtIs the total electric quantity of the battery pack in the station in the period t, Bt+1Is the total electric quantity of the battery pack in the station in the period of t +1, EbRated capacity, P, for a single cellw,tThe power flowing from the fan to the BSS in the t period;
Figure BDA0002978038900000186
is the power, P, output by the nth MT to the charger during the t periodbuy,tFor the electricity purchasing power, P, of the system in the t periodlcar,tNumber of changed taxis in t period, SmaxThe upper limit of the state of charge of the storage battery; ssocRemaining battery SOC, P when replacing battery for electric vehiclesell,tAnd the power sold to the power grid by the combined system is in the period t.
The invention firstly proposes a vehicle transfer characteristic based on flexible load extension, secondly establishes a vehicle transfer mechanism for day-ahead scheduling, then introduces the vehicle transfer mechanism, changes a battery transfer load curve by properly compensating a vehicle for transferring battery transfer time, enables BSS to reformulate a better battery charging and exchanging plan, and finally constructs a BSS-containing microgrid combined system optimization scheduling model considering the vehicle transfer mechanism by taking the maximum goal of the sum of the system power selling income, the battery transferring income, the fan cost, the MT cost, the vehicle transfer compensation cost, the battery cost, the electricity purchasing cost from an external power grid and the system construction cost into consideration. The method has the advantages of being scientific and reasonable, strong in applicability, good in effect and the like, and can improve energy utilization efficiency and operation economy.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.

Claims (10)

1. An optimal scheduling method for a BSS-containing microgrid combined system is characterized by comprising the following steps:
constructing a vehicle transfer mechanism model according to the vehicle transfer mode and the vehicle transfer way;
determining vehicle transfer compensation cost and the number of actual battery replacement vehicles according to the vehicle transfer mechanism model;
determining a power constraint based on the first difference and the second difference; the first difference is a battery residual capacity difference; the second difference is the difference between the input power and the output power of the BSS-containing microgrid joint system;
determining total electric quantity constraint according to the total electric quantity of the battery;
determining a battery state quantity change constraint according to the battery state; the battery state includes: charging, full, discharging, and waiting to charge;
determining output limit constraints of the charge and discharge machine according to the power of the charge and discharge machine, the number of the charged batteries and the number of the discharged batteries;
determining battery number constraint according to the actual number of the battery replacement vehicles and the number of fully charged batteries;
constructing an optimized scheduling model of the BSS-containing microgrid combined system by taking the maximum operating yield of the BSS-containing microgrid combined system as an objective function and taking the power constraint, the total electric quantity constraint, the battery state quantity change constraint, the charge and discharge machine output limit constraint and the battery number constraint as constraint conditions; the operation income of the BSS-containing microgrid combined system comprises: the system sells electricity income, trades electricity income, fan cost, miniature gas turbine cost to the electric wire netting, the said vehicle shifts the compensation cost, battery cost, purchases electricity cost and system construction cost from the external electric wire netting;
and performing optimized scheduling according to the optimized scheduling model of the BSS-containing micro-grid combined system to obtain the optimized time for charging and replacing the electric power of the vehicle and the number of the vehicles for charging and replacing the electric power.
2. The optimal scheduling method of the BSS-containing microgrid integrated system according to claim 1, wherein the determining of the vehicle transfer compensation cost and the actual battery replacement vehicle number according to the vehicle transfer mechanism model specifically comprises:
determining the actual number of battery replacement vehicles by using the following formula:
Figure FDA0002978038890000011
wherein, Plnew,tFor the actual number of battery change vehicles, Pl,tThe number of the electric vehicles is changed for the original plan,
Figure FDA0002978038890000012
the number of vehicles is shifted i cycles ahead for cycle t to cycle t-i,
Figure FDA0002978038890000013
advancing the number of vehicles from cycle t + i to cycle t, wherein i > 0,
Figure FDA0002978038890000021
the number of vehicles is transferred for the delay from period t to period t + i,
Figure FDA0002978038890000022
transferring the number of vehicles to a period T after the period T-i is delayed, wherein i is greater than 0, and T is the total number of periods;
determining a vehicle transfer compensation cost using the following equation:
Figure FDA0002978038890000023
wherein P is total transfer compensation; dtq,iPrice compensation is carried out for i period transfer in advance; dyh,iThe price is compensated for the delayed i-cycle transfer.
3. The optimal scheduling method of the BSS-containing piconet integrated system according to claim 2, wherein the determining of the power constraint according to the first difference and the second difference specifically includes:
the power constraint is determined using the following equation:
Figure FDA0002978038890000024
wherein, BtIs the total electric quantity of the battery pack in the station in the period t, Bt+1Is the total electric quantity of the battery pack in the station in the period of t +1, EbRated capacity, P, for a single cellw,tThe power flowing from the fan to the BSS in the t period;
Figure FDA0002978038890000025
is the power, P, output by the nth MT to the charger during the t periodbuy,tIs a systemPower purchase in t period, Plcar,tNumber of changed taxis in t period, SmaxThe upper limit of the state of charge of the storage battery; ssocRemaining battery SOC, P when replacing battery for electric vehiclesell,tAnd the power sold to the power grid by the combined system is in the period t.
4. The optimal scheduling method of the BSS-included microgrid integrated system according to claim 3, wherein the determining of the total power constraint according to the total battery power specifically includes:
the total charge constraint is determined using the following equation:
(Nc,t+Nd,t+Nw,t)EbSmin+Nr,tEbSmax≤Bt≤Nw,tEbSmin+(Nc,t+Nd,t+Nr,t)EbSmax
wherein N isc,tNumber of batteries being charged for t period, Nd,tNumber of cells being discharged for t period, Nw,tNumber of batteries waiting to be charged for t period, Nr,tThe number of BSS full-charge batteries in t period, SminThe lower limit of the SOC of the battery is c, the state of charge is w, the state of charge is waiting, d is the state of discharge, and r is the full state.
5. The optimal scheduling method of the BSS-containing microgrid integrated system according to claim 4, wherein the determining of the battery state quantity change constraint according to the battery state specifically includes:
determining a battery state quantity change constraint using the following equation:
Figure FDA0002978038890000031
wherein M is the total number of batteries in BSS, Nc,t-1Number of batteries being charged for t-1 cycle, Nd,t-1Number of cells being discharged for t-1 cycle, Nw,t-1Waiting to be charged for t-1 periodNumber of batteries, Ncar,t-1And the total number of the vehicles participating in the battery replacement in the scheduling day of the t-1 period.
6. The optimal scheduling method of the BSS-containing microgrid integrated system according to claim 5, wherein the determining of the charging and discharging machine output limit constraints according to the charging and discharging machine power, the number of the charging batteries and the number of the discharging batteries specifically comprises:
determining a charge-discharge machine output limit constraint using the following equation:
Figure FDA0002978038890000032
wherein k iscIs the total number of chargers, Pc,maxUpper limit of power flowing in charge and discharge machine, Pc,tIs the charge state of the charge and discharge machine.
7. The optimal scheduling method of the BSS-containing microgrid integrated system according to claim 6, wherein the determining of the battery number constraint according to the actual number of battery replacement vehicles and the number of full batteries specifically comprises:
the number of cells constraint is determined using the following equation:
Plnew,t≤Nr,t
wherein N isr,tThe number of BSS full-charge batteries in t period, Plnew,tThe number of the actual battery replacement vehicles is shown.
8. An optimized scheduling system of a BSS-containing microgrid combined system is characterized by comprising:
the vehicle transfer mechanism model building module is used for building a vehicle transfer mechanism model according to the vehicle transfer mode and the vehicle transfer way;
the vehicle transfer compensation cost and actual battery replacement vehicle number determining module is used for determining the vehicle transfer compensation cost and the actual battery replacement vehicle number according to the vehicle transfer mechanism model;
a power constraint determination module for determining a power constraint based on the first difference and the second difference; the first difference is a battery residual capacity difference; the second difference is the difference between the input power and the output power of the BSS-containing microgrid joint system;
the total electric quantity constraint determining module is used for determining total electric quantity constraint according to the total electric quantity of the battery;
the battery state quantity change constraint determining module is used for determining battery state quantity change constraints according to the battery states; the battery state includes: charging, full, discharging, and waiting to charge;
the charge and discharge machine output limit restriction determining module is used for determining the charge and discharge machine output limit restriction according to the charge and discharge machine power, the number of the charged batteries and the number of the discharged batteries;
the battery number constraint determining module is used for determining battery number constraint according to the actual battery replacement vehicle number and the full-charge battery number;
the optimization scheduling model building module is used for building an optimization scheduling model of the BSS-containing microgrid combined system by taking the maximum operating yield of the BSS-containing microgrid combined system as an objective function and taking the power constraint, the total electric quantity constraint, the battery state quantity change constraint, the charge and discharge machine output limit constraint and the battery number constraint as constraint conditions; the operation income of the BSS-containing microgrid combined system comprises: the system sells electricity income, trades electricity income, fan cost, miniature gas turbine cost to the electric wire netting, the said vehicle shifts the compensation cost, battery cost, purchases electricity cost and system construction cost from the external electric wire netting;
and the optimized scheduling module is used for performing optimized scheduling according to the optimized scheduling model of the BSS-containing micro-grid combined system to obtain the optimized time for charging and exchanging the battery and the number of vehicles for charging and exchanging the battery.
9. The optimal scheduling system of the BSS-containing microgrid integrated system of claim 8, wherein the module for determining the vehicle transfer compensation cost and the actual battery replacement vehicle number specifically comprises:
an actual battery replacement vehicle number determination unit for determining an actual battery replacement vehicle number using the following formula:
Figure FDA0002978038890000041
wherein, Plnew,tFor the actual number of battery change vehicles, Pl,tThe number of the electric vehicles is changed for the original plan,
Figure FDA0002978038890000042
the number of vehicles is shifted i cycles ahead for cycle t to cycle t-i,
Figure FDA0002978038890000043
advancing the number of vehicles from cycle t + i to cycle t, wherein i > 0,
Figure FDA0002978038890000044
the number of vehicles is transferred for the delay from period t to period t + i,
Figure FDA0002978038890000045
transferring the number of vehicles to a period T after the period T-i is delayed, wherein i is greater than 0, and T is the total number of periods;
a vehicle transfer compensation cost determination unit for determining a vehicle transfer compensation cost using the following formula:
Figure FDA0002978038890000051
wherein P is total transfer compensation; dtq,iPrice compensation is carried out for i period transfer in advance; dyh,iThe price is compensated for the delayed i-cycle transfer.
10. The optimal scheduling system of the BSS-containing microgrid integrated system of claim 9, wherein the power constraint determining module specifically comprises:
a power constraint determining unit for determining a power constraint using the following formula:
Figure FDA0002978038890000052
wherein, BtIs the total electric quantity of the battery pack in the station in the period t, Bt+1Is the total electric quantity of the battery pack in the station in the period of t +1, EbRated capacity, P, for a single cellw,tThe power flowing from the fan to the BSS in the t period;
Figure FDA0002978038890000053
is the power, P, output by the nth MT to the charger during the t periodbuy,tFor the electricity purchasing power, P, of the system in the t periodlcar,tNumber of changed taxis in t period, SmaxThe upper limit of the state of charge of the storage battery; ssocRemaining battery SOC, P when replacing battery for electric vehiclesell,tAnd the power sold to the power grid by the combined system is in the period t.
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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113315148A (en) * 2021-07-08 2021-08-27 傲普(上海)新能源有限公司 Capacity configuration method and system of energy storage system in frequency modulation of unit system
CN113486504A (en) * 2021-06-28 2021-10-08 上海电机学院 Battery management control method based on scheduling cost
WO2023028882A1 (en) * 2021-08-31 2023-03-09 宁德时代新能源科技股份有限公司 Electrical energy transmission method and apparatus, device, and medium

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113486504A (en) * 2021-06-28 2021-10-08 上海电机学院 Battery management control method based on scheduling cost
CN113486504B (en) * 2021-06-28 2022-05-27 上海电机学院 Battery management control method based on scheduling cost
CN113315148A (en) * 2021-07-08 2021-08-27 傲普(上海)新能源有限公司 Capacity configuration method and system of energy storage system in frequency modulation of unit system
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