CN113690941A - Method, device, equipment and storage medium for configuring capacity of optical storage and charging microgrid - Google Patents

Method, device, equipment and storage medium for configuring capacity of optical storage and charging microgrid Download PDF

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CN113690941A
CN113690941A CN202110312413.3A CN202110312413A CN113690941A CN 113690941 A CN113690941 A CN 113690941A CN 202110312413 A CN202110312413 A CN 202110312413A CN 113690941 A CN113690941 A CN 113690941A
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energy storage
storage system
cost
photovoltaic
output power
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魏一凡
韩天倚
王烁祺
韩雪冰
卢兰光
欧阳明高
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Tsinghua University
Toyota Motor Corp
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Toyota Motor Corp
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • 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
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • 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
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/50—Photovoltaic [PV] energy
    • Y02E10/56—Power conversion systems, e.g. maximum power point trackers
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E70/00—Other energy conversion or management systems reducing GHG emissions
    • Y02E70/30—Systems combining energy storage with energy generation of non-fossil origin

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  • Supply And Distribution Of Alternating Current (AREA)
  • Charge And Discharge Circuits For Batteries Or The Like (AREA)

Abstract

The embodiment of the invention provides a method, a device, equipment and a storage medium for configuring capacity of a light storage and charging microgrid. The method comprises the steps of obtaining photovoltaic capacity and energy storage capacity based on configuration points, and calculating output power of a photovoltaic system based on solar illumination radiation intensity and parameters related to the photovoltaic capacity; the output power of the photovoltaic system, the user load working condition and the power grid output power obtained by intelligent algorithm optimization are input into a power balance model, the output power of the energy storage system is calculated, and the real-time SOC is calculated; inputting the output power and the real-time SOC of the energy storage system into a battery capacity attenuation model, and calculating a replacement period of the energy storage system; inputting the replacement period and the economic relevant parameters into an objective function relevant to the net present value of the cost, and calculating the lowest total net present value cost at the configuration point; repeating the steps until the lowest total net present value cost of each configuration point on the plane is obtained; and comparing the lowest total net present value cost at each configuration point to obtain the configuration point with the optimal cost.

Description

Method, device, equipment and storage medium for configuring capacity of optical storage and charging microgrid
Technical Field
The invention relates to the technical field of optical storage and charging micro-networks, in particular to a method, a device, equipment and a storage medium for configuring capacity of an optical storage and charging micro-network.
Background
A Micro-Grid (also referred to as a "Micro-Grid") is a small power generation and distribution system composed of a distributed power system, an energy storage system and the like; the distributed power system may be a distributed renewable power source, such as a system that generates electricity via photovoltaic, and the microgrid is capable of safely and reliably incorporating intermittent energy randomly generated by the renewable power source into the Grid (Grid).
Conventional microgrids have problems with energy storage and capacity allocation. For example, the prior art (e.g., prior documents i and ii, whose patent numbers are CN201610182863.4 and CN201510473703.0, respectively) constructs an energy storage model and an operation strategy in advance, but does not consider how the microgrid can achieve cost optimization in a plurality of different application scenarios; the prior art (e.g., prior documents three and four, with patent numbers CN201810146185.5 and CN201710703339.1, respectively) relates to the calculation of the microgrid operation cost, but does not consider the technical and economic impact caused by the capacity change of the microgrid.
Disclosure of Invention
The technical problem solved by the invention is how to solve the problems of the microgrid in energy storage and capacity configuration.
The embodiment of the invention provides a method for configuring capacity of a light storage and charging microgrid, wherein the light storage and charging microgrid comprises a photovoltaic system and an energy storage system, and the method comprises the following steps: the method comprises the steps that firstly, the photovoltaic capacity of a photovoltaic system and the energy storage capacity of an energy storage system are obtained based on configuration points, the output power of the photovoltaic system is calculated based on the solar illumination radiation intensity and parameters related to the photovoltaic capacity, and the configuration points are points on a plane formed by the two variables of the energy storage capacity and the photovoltaic capacity; inputting the output power of the photovoltaic system, the user load working condition and the power grid output power obtained by the optimization of the intelligent algorithm into a power balance model, thereby calculating the output power of the energy storage system and calculating the real-time SOC; inputting the output power and the real-time SOC of the energy storage system into a battery capacity attenuation model, thereby calculating the replacement period of the energy storage system; inputting the replacement period and the economic relevant parameters into an objective function relevant to the net present value of the cost, and calculating the lowest total net present value cost at the configuration point by the objective function based on iterative optimization; step five, repeating the steps one to four until the lowest total net present value cost of each configuration point on the plane is obtained; and step six, comparing the lowest total net present value cost at each configuration point on the plane to obtain the configuration point with the optimal cost.
Optionally, step one includes calculating the power output P of the photovoltaic system by the following formulaPV：
PPV＝IPVVPV，
Figure BDA0002989907580000021
Figure BDA0002989907580000022
Figure BDA0002989907580000023
Figure BDA0002989907580000024
Figure BDA0002989907580000025
Wherein the photovoltaic system comprises a photovoltaic cell, IPVIs the operating current of the photovoltaic cell, VPVIs the operating voltage of the photovoltaic cell, IphIs photocurrent, IoFor reverse current, RsIs the resistance of the equivalent series resistor, q is the electronic electric quantity, N is the factor of the photodiode, k is the Boltzmann constant, T is the test temperature, NsIs the number of cells in the photovoltaic cell, KiFor temperature coefficient of short-circuit current, TrefFor standard test temperature, IscnIs a forward current of PN junction, ImIs the output current of the photovoltaic cell at maximum output power, IonFor the forward current of the PN junction under standard test, VmFor the output voltage of the photovoltaic cell at maximum output power, EgIs the band gap, V, of the photoelectric material in the photodiodeocnIs open circuit voltage, S is intensity of solar radiation, SrefThe intensity of the solar illumination radiation under the standard test is shown.
Optionally, the power balance model has the following formula:
PEV(t)＝PPV(t)+PESS(t)+Pgrid(t)，
wherein, PEV(t) load power, P, of the charging pile under the user load conditionPV(t) power output of the photovoltaic system, PESS(t) is the output power of the energy storage system, PgridAnd (t) is the output power of the power grid.
Optionally, in the second step, the real-time SOC of the energy storage system is calculated by the following formula:
Figure BDA0002989907580000031
Figure BDA0002989907580000032
Vbat＝OCVbat-IbatRbat，
wherein the energy storage system comprises an energy storage batteryReal time SOC and SOC0The real-time battery state of charge value and the initial state of charge value, eta of the energy storage system respectivelycFor the charge-discharge efficiency of energy-storage cells, IbatFor charging and discharging currents of energy-storing batteries, QbatFor charging and discharging the charge, OCV, of energy-storing batteriesbatFor open-circuit voltage of energy-storage cells, PbatFor the output power of the energy storage cell, RbatTo the internal resistance of the energy storage cell, VbatIs the operating voltage of the energy storage battery.
Optionally, step three includes: and setting the time for the energy storage capacity to decay from the initial capacity to the proportional threshold as the replacement period of the energy storage system.
Optionally, the objective function takes the lowest total net present value cost based on the sum of the total initial investment cost, the total net present value of replacement cost and the total net present value of operation and maintenance cost, and step four includes calculating the lowest total net present value cost min { NPC } at the configuration point by the following formula:
min{NPC}＝min{Cinv+NPCrep+NPCo&m}，
Cinv＝Cinv,PV+Cinv,ESS+Cinv,DC/DC，
Figure BDA0002989907580000033
Figure BDA0002989907580000041
Figure BDA0002989907580000042
wherein, CinvFor total initial investment cost, NPCrepNet present value of total replacement cost, NPCo&mThe net present value of the total operation and maintenance cost, Cinv,PVFor initial investment costs of the photovoltaic system, Cinv,ESSFor initial investment costs of the energy storage system, Cinv,DC/DCFor the initial investment cost of the DC-DC converter, ROD isPrice depreciation rate, NrepThe number of times of replacement of the energy storage system in the life cycle, L is the life cycle of the energy storage system, LrepFor the replacement cycle of the energy storage system, i is the risk-free interest rate, CgridCost of purchasing electricity from the microgrid to the grid, CmThe annual average maintenance cost of the energy storage system, the photovoltaic system and the DC/DC converter is shown, and the Inf is the annual average currency expansion rate.
Optionally, step four includes calculating the lowest total net present value cost at the configuration point based on the iterative optimization if the objective function satisfies a constraint, the constraint comprising at least one of the following formulas:
-PDC/DC≤PESS≤PDC/DC，
-Pwire≤Pgrid≤Pwire，
Figure BDA0002989907580000043
SOCmin(t)≦SOC(t)≦SOCmax(t)，
Figure BDA0002989907580000044
Figure BDA0002989907580000045
wherein, PDC/DCIs the output power of the DC-DC converter, PESSIs the output power of the energy storage system, PwireFor transmission power of network cables, PgridThe output power of the power grid, delta DOD is the variation of the charging and discharging depth of the energy storage battery in the energy storage system, SOC (t) is the battery state of charge (SOC) of the energy storage system changing along with time, and SOCmin(t) is the minimum value of the SOC of the energy storage system at the end of operation, SOCmax(t) is the maximum value of the SOC of the energy storage system at the end of operation, LPSP is a ratio, Sloss(t) the energy of the microgrid short of electricity at time t, Sdemand(t) is time t microgridEnergy to be charged, LPSPmaxMaximum value of LPSP, LPPV is the ratio of photovoltaic power generation loss, PPV,loss(t) energy not stored in the energy storage system or not utilized at time t, PPV(t) energy of photovoltaic system generation at time t, LPPVmaxT is the maximum value of LPPV and T is the number of time instants.
Optionally, the fourth step includes: the objective function is subjected to iterative optimization through an intelligent optimization algorithm to calculate the lowest total net present value cost at the configuration point, wherein the intelligent optimization algorithm comprises a particle swarm optimization algorithm, a genetic algorithm and an annealing algorithm.
An embodiment of the present invention further provides an apparatus, including a memory and a processor, where the memory stores computer instructions executable on the processor, and the processor executes the computer instructions to perform any of the steps of the method described above.
The embodiment of the invention also provides a storage medium, wherein computer instructions are stored on the storage medium, and when the computer instructions are operated, the steps of any one of the methods are executed.
The embodiment of the invention also provides a device for configuring the capacity of the optical storage and charging microgrid, wherein the optical storage and charging microgrid comprises a photovoltaic system and an energy storage system, and the device comprises: the first acquisition module is suitable for acquiring the photovoltaic capacity of the photovoltaic system and the energy storage capacity of the energy storage system based on a configuration point, calculating the output power of the photovoltaic system based on the solar illumination radiation intensity and parameters related to the photovoltaic capacity, and the configuration point is a point on a plane formed by two variables, namely the energy storage capacity and the photovoltaic capacity; the first calculation module is suitable for inputting the output power of the photovoltaic system, the user load working condition and the power grid output power obtained by intelligent algorithm optimization into the power balance model, so that the output power of the energy storage system is calculated, and the real-time SOC is calculated; the second calculation module is suitable for inputting the output power and the real-time SOC of the energy storage system into the battery capacity attenuation model so as to calculate the replacement period of the energy storage system; a third calculation module adapted to input the permutation period and the economic-related parameter to an objective function related to net present value of cost, the objective function calculating a lowest total net present value cost at the configuration point based on iterative optimization; the second acquisition module is suitable for acquiring the lowest total net present value cost at the rest configuration points on the plane; and the comparison module is suitable for comparing the lowest total net present value cost at each configuration point on the plane to obtain the configuration point with the optimal cost.
Optionally, the first obtaining module is adapted to calculate the output power P of the photovoltaic system by the following formulaPV：
PPV＝IPVVPV，
Figure BDA0002989907580000061
Figure BDA0002989907580000062
Figure BDA0002989907580000063
Figure BDA0002989907580000064
Figure BDA0002989907580000065
Wherein the photovoltaic system comprises a photovoltaic cell, IPVIs the operating current of the photovoltaic cell, VPVIs the operating voltage of the photovoltaic cell, IphIs photocurrent, IoFor reverse current, RsIs the resistance of the equivalent series resistor, q is the electronic electric quantity, N is the factor of the photodiode, k is the Boltzmann constant, T is the test temperature, NsIs the number of cells in the photovoltaic cell, KiFor temperature coefficient of short-circuit current, TrefFor standard test temperature, IscnIs a forward current of PN junction, ImIs the output current of the photovoltaic cell at maximum output power, IonFor standard testing of PN junctionsForward current, VmFor the output voltage of the photovoltaic cell at maximum output power, EgIs the band gap, V, of the photoelectric material in the photodiodeocnIs open circuit voltage, S is intensity of solar radiation, SrefThe intensity of the solar illumination radiation under the standard test is shown.
Optionally, the power balance model has the following formula:
PEV(t)＝PPV(t)+PESS(t)+Pgrid(t)，
wherein, PEV(t) load power, P, of the charging pile under the user load conditionPV(t) power output of the photovoltaic system, PESS(t) is the output power of the energy storage system, PgridAnd (t) is the output power of the power grid.
Optionally, the first calculation module is adapted to calculate the real-time SOC of the energy storage system by the following formula:
Figure BDA0002989907580000071
Figure BDA0002989907580000072
Vbat＝OCVbat-IbatRbat，
the energy storage system comprises an energy storage battery, a real-time SOC and an SOC0The real-time battery state of charge value and the initial state of charge value, eta of the energy storage system respectivelycFor the charge-discharge efficiency of energy-storage cells, IbatFor charging and discharging currents of energy-storing batteries, QbatFor charging and discharging the charge, OCV, of energy-storing batteriesbatFor open-circuit voltage of energy-storage cells, PbatFor the output power of the energy storage cell, RbatTo the internal resistance of the energy storage cell, VbatIs the operating voltage of the energy storage battery.
Optionally, the second calculation module is adapted to set a time for the energy storage capacity to decay from the initial capacity to the proportional threshold as a replacement period of the energy storage system.
Optionally, the objective function takes the lowest total net present value cost based on the sum of the total initial investment cost, the total net present value of replacement cost and the total net present value of operation and maintenance cost, and the third calculation module is adapted to calculate the lowest total net present value cost min { NPC } at the configuration point by:
min{NPC}＝min{Cinv+NPCrep+NPCo&m}，
Cinv＝Cinv,PV+Cinv,ESS+Cinv,DC/DC，
Figure BDA0002989907580000073
Figure BDA0002989907580000074
Figure BDA0002989907580000075
wherein, CinvFor total initial investment cost, NPCrepNet present value of total replacement cost, NPCo&mThe net present value of the total operation and maintenance cost, Cinv,PVFor initial investment costs of the photovoltaic system, Cinv,ESSFor initial investment costs of the energy storage system, Cinv,DC/DCFor initial investment cost of the DC-DC converter, ROD is price depreciation, NrepThe number of times of replacement of the energy storage system in the life cycle, L is the life cycle of the energy storage system, LrepFor the replacement cycle of the energy storage system, i is the risk-free interest rate, CgridCost of purchasing electricity from the microgrid to the grid, CmThe annual average maintenance cost of the energy storage system, the photovoltaic system and the DC/DC converter is shown, and the Inf is the annual average currency expansion rate.
Optionally, the third calculation module is adapted to calculate the lowest total net present value cost at the configuration point based on the iterative optimization if the objective function satisfies a constraint, the constraint comprising at least one of the following formulas:
-PDC/DC≤PESS≤PDC/DC，
-Pwire≤Pgrid≤Pwire，
Figure BDA0002989907580000081
SOCmin(t)≦SOC(t)≦SOCmax(t)，
Figure BDA0002989907580000082
Figure BDA0002989907580000083
wherein, PDC/DCIs the output power of the DC-DC converter, PESSIs the output power of the energy storage system, PwireFor transmission power of network cables, PgridThe output power of the power grid, delta DOD is the variation of the charging and discharging depth of the energy storage battery in the energy storage system, SOC (t) is the battery state of charge (SOC) of the energy storage system changing along with time, and SOCmin(t) is the minimum value of the SOC of the energy storage system at the end of operation, SOCmax(t) is the maximum value of the SOC of the energy storage system at the end of operation, LPSP is a ratio, Sloss(t) the energy of the microgrid short of electricity at time t, Sdemand(t) the energy to be charged in the microgrid at time t, LPSPmaxMaximum value of LPSP, LPPV is the ratio of photovoltaic power generation loss, PPV,loss(t) energy not stored in the energy storage system or not utilized at time t, PPV(t) energy of photovoltaic system generation at time t, LPPVmaxT is the maximum value of LPPV and T is the number of time instants.
Optionally, the third computing module is adapted to calculate the lowest total net present value cost at the configuration point by performing iterative optimization on the objective function through an intelligent optimization algorithm, where the intelligent optimization algorithm includes a particle swarm optimization algorithm, a genetic algorithm, and an annealing algorithm.
The embodiment of the invention can solve various problems in planning of the light storage and charging micro-grid and has corresponding beneficial effects.
For example, in the embodiment of the present invention, in consideration of multiple application scenarios and different working conditions of the microgrid, the net present cost value during the operating life of the microgrid may be different in different application scenarios or different working conditions, and the technical solution provided in the embodiment of the present invention relates the objective function to the net present cost value during the operating life of the microgrid, so as to match the specific situation of actual construction or operation and maintenance of the microgrid.
For another example, in the embodiment of the present invention, in consideration of the capacity change of the microgrid during the operation process and the value (i.e., the time value) of the asset that changes with time due to technical reasons, such as the performance of the energy storage system may be degraded and needs to be replaced, the technical solution provided by the embodiment of the present invention calculates the output power, the real-time SOC (State of Charge), the replacement period, and the like of the energy storage system as parameters to be input into the objective function, so that the microgrid may operate at a configuration point with the optimal cost.
For another example, in an embodiment of the present invention, a specific application of an intelligent optimization algorithm is provided, and the lowest total net present value cost at a configuration point is calculated through iterative optimization; the intelligent optimization algorithm comprises a particle swarm optimization algorithm, a Genetic Algorithm (GA) and an annealing algorithm (SA).
Drawings
Fig. 1 is a schematic structural diagram of an optical storage and charging microgrid coupled to a power grid in an embodiment of the present invention;
fig. 2 is a flowchart of a method for configuring optical storage and microgrid capacity in an embodiment of the present invention;
FIG. 3 is a schematic diagram of six operating conditions in an embodiment of the present invention;
FIG. 4 is a schematic diagram of a photovoltaic cell single diode equivalent circuit model of a photovoltaic system in an embodiment of the invention;
FIG. 5 is a graph of operating current and output power of a photovoltaic system as a function of operating voltage in an embodiment of the present invention;
FIG. 6 is a schematic diagram of the open circuit voltage and internal resistance of an energy storage battery as a function of state of charge in an embodiment of the present invention;
FIG. 7 is a graph comparing simulation results of a battery capacity fade model with actual experimental data in an embodiment of the present invention;
FIG. 8 is a schematic diagram of a particle swarm optimization algorithm in an embodiment of the present invention;
FIG. 9 is a schematic diagram of the operating power over time under six operating conditions in an embodiment of the present invention;
FIG. 10 is a three-dimensional schematic diagram of the lowest total net present value cost with respect to the configuration point under six conditions in an embodiment of the present invention;
fig. 11 is a schematic structural diagram of an apparatus for configuring optical storage and microgrid capacity according to an embodiment of the present invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in detail below.
As shown in fig. 1, a light storage and charging microgrid 100 is provided. The light storage and charging microgrid 100 is a type of microgrid, wherein a power supply system is a system for generating power through photovoltaic power; the light storage and charging microgrid 100 has functions of energy storage and external charging (such as charging piles).
The light Storage and charging microgrid 100 includes a Photovoltaic (PV) System 110 and an Energy Storage System (ESS) 120.
The photovoltaic system 110 includes a photovoltaic array (also referred to as a photovoltaic module) for converting solar energy into electrical energy.
The energy storage system 120 may include at least one battery system (also referred to as an energy storage battery), and each battery system may include a number of battery modules for storing energy. In fig. 1, n battery systems are illustrated. The Energy storage System 120 may be managed by an Energy Management System (EMS).
The power grid 130 may be coupled to the optical storage and charging microgrid 100 through an alternating current/direct current conversion device (AC/DC converter), so as to receive energy from the optical storage and charging microgrid 100 or transmit energy to the optical storage and charging microgrid 100.
At least one of photovoltaic system 110, energy storage system 120, and grid 130 may deliver energy to charging pile 140 via a dc bus or an ac bus; the charging piles 140 include direct current charging piles and alternating current charging piles.
A Battery Array Management System (BAMS) may connect the EMS and the charging pile 140 to manage both.
As shown in fig. 2, the method 200 is used to configure the capacity of the optical storage and charging microgrid; the capacity of the light storage and charging microgrid comprises photovoltaic capacity related to output power of the photovoltaic system and energy storage capacity related to electric power stored by the energy storage system.
In an embodiment of the present invention, the energy storage system may include at least one energy storage battery, and therefore, the parameter related to the energy storage system is a parameter of the at least one energy storage battery. Specifically, energy storage capacity, output power, real-time battery state of charge (SOC), and initial SOC0Attenuation parameter, substitution period LrepInitial investment cost Cinv,ESSAnd the replacement frequency N of the energy storage system in the life cyclerepLife cycle L, annual average maintenance cost CmThe isoparameters are respectively associated with at least one energy storage battery.
The method 200 includes the steps of: the method comprises the steps that firstly, the photovoltaic capacity of a photovoltaic system and the energy storage capacity of an energy storage system are obtained based on configuration points, the output power of the photovoltaic system is calculated based on the solar illumination radiation intensity and parameters related to the photovoltaic capacity, and the configuration points are points on a plane formed by the two variables of the energy storage capacity and the photovoltaic capacity; inputting the output power of the photovoltaic system, the user load working condition and the power grid output power obtained by the optimization of the intelligent algorithm into a power balance model, thereby calculating the output power of the energy storage system and calculating the real-time SOC; inputting the output power and the real-time SOC of the energy storage system into a battery capacity attenuation model, thereby calculating the replacement period of the energy storage system; inputting the replacement period and the economic relevant parameters into an objective function relevant to the net present value of the cost, and calculating the lowest total net present value cost at the configuration point by the objective function based on iterative optimization; step five, repeating the steps one to four until the lowest total net present value cost of each configuration point on the plane is obtained; and step six, comparing the lowest total net present value cost at each configuration point on the plane to obtain the configuration point with the optimal cost.
In an embodiment of the present invention, the capacity of the optical storage and charging microgrid is configured through a two-layer structure, wherein the two-layer structure includes a capacity configuration layer located at an upper layer and an energy management layer located at a lower layer. Through the arrangement of the double-layer structure, the combination of the energy storage capacity and the photovoltaic capacity with the optimal cost can be predetermined in the design stage of the microgrid.
As shown in FIG. 2, the two-layer structure reflected in method 200 includes a capacity configuration layer and an energy management layer.
The capacity configuration layer is used for configuring the energy storage capacity and the photovoltaic capacity of the light storage and charging micro-grid; for example, the method comprises the steps of receiving input of parameters such as the working condition of a photovoltaic system and the solar illumination radiation intensity, determining the working ranges of the energy storage capacity and the photovoltaic capacity, determining the lowest total net present value cost of various combinations (the combination can be called as a 'configuration point') of the energy storage capacity and the photovoltaic capacity, and comparing the lowest total net present value cost of each configuration point on a plane to obtain a configuration point with the optimal cost.
The energy management layer may calculate a corresponding lowest total net present value cost and energy operation strategy based on any combination of energy storage capacity and photovoltaic capacity; for example, the output power and the real-time SOC of the energy storage system are calculated based on a power balance model, the replacement cycle of the energy storage system is calculated based on a battery capacity attenuation model, and the lowest total net present value cost at the selected configuration point is calculated based on an objective function related to the net present value of cost, wherein the objective function is based on input parameters such as various costs of the optical storage charging microgrid in the whole life cycle of the optical storage charging microgrid, for example, the total initial investment cost, the replacement cost and the operation and maintenance cost.
The following is described in connection with specific steps.
In the performance of step one, both the energy storage capacity and the photovoltaic capacity may be varied, and the ranges of both may be determined and taken as one and the other of the X-axis and the Y-axis, thereby forming an XY plane. Different energy storage capacities and different photovoltaic capacities can be combined with each other to form a specific configuration point on the XY plane.
The photovoltaic capacity of the photovoltaic system and the energy storage capacity of the energy storage system can be obtained based on the configuration points, and the output power of the photovoltaic system is calculated based on the solar illumination radiation intensity and the parameters related to the photovoltaic capacity.
The working conditions include two situations: firstly, the charging condition of the electric vehicle on the charging pile (such as the charging condition of 24 hours) can be called as a user load condition; and secondly, under the working condition of solar radiation, the solar illumination radiation intensity can be extracted.
Fig. 3 includes sub-diagrams 3a to 3f, which respectively show six different operating conditions, all relating to the operating condition of charging the electric vehicle on the charging pile, wherein the abscissa corresponds to 24 hours of the day and the ordinate represents the charging power. The six different working conditions correspond to different charging behaviors, so that the charging power on the charging pile is distributed differently based on time and is irrelevant to solar illumination radiation.
In particular implementations, the operating conditions may be extracted periodically, such as on an hourly or daily basis.
The operating conditions may be extracted by a clustering algorithm. Clustering algorithms include k-means clustering algorithms or other unsupervised machine learning methods.
The following description is based on the k-means clustering algorithm.
All data sets were determined to be: p ═ P1,P2,...,Pi,...,Pn]TWherein P is a complete data set comprising n pieces of data, P1、P2、Pi、PnRespectively 1 st, 2 nd, i th and n th data, and T is matrix transposition.
Each piece of data in the complete data set may have m dimensions, e.g., PiCan be expressed as: pi＝[Pi1,Pi2,...,Pit,...,Pim]Wherein P isi1、Pi2、Pii、PimRespectively the 1 st, 2 nd, t th and m th dimensions of the ith piece of data. The period may be in hours, where m is 24.
K clusters can be generatedA central cluster: { mu. }1,μ2,...,μ k1 < k ≦ n, and the Euclidean distances from all points to the center cluster are calculated by the following formula:
Figure BDA0002989907580000131
wherein, mujtIs the jth center cluster mujThe t-th dimension of (a).
Taking the nearest mean vector as PjCluster marking of (2):
λj＝argmini∈{1,2,3,...,k}dji (2)
wherein d isjiThe distance from the ith data to the jth center cluster.
Sample PjDividing into corresponding clusters:
Figure BDA0002989907580000132
wherein the content of the first and second substances,
Figure BDA0002989907580000133
set of samples marked with λ j for a cluster,
Figure BDA0002989907580000134
To sample PjThe corresponding cluster scribed in.
Calculate the new mean vector:
Figure BDA0002989907580000135
wherein, | CiAnd | is the modulus of all samples currently belonging to the ith cluster.
Iterating repeatedly so that:
|μ′i-μi|＜ε (5)
wherein epsilon is a set iteration termination condition.
The output power P of the photovoltaic system can be calculated through the obtained solar illumination radiation intensity S and parameters related to the photovoltaic capacity (such as the working current, the working voltage, the photocurrent, the reverse current, the equivalent series internal resistance, the quality factor of a photodiode, the number of cells in the photovoltaic cell and the like of the photovoltaic system or the photovoltaic cell)PV。
As shown in FIG. 4, the photovoltaic cell of the photovoltaic system includes a photodiode, an equivalent series circuit RsAnd a shunt resistor RshThe output power P of the photovoltaic system can be calculated by the following formulaPV：
PPV＝IPVVPV (6)
Figure BDA0002989907580000141
Figure BDA0002989907580000142
Wherein, in the photovoltaic cell, IPVFor operating current, VPVIs an operating voltage, IphIs photocurrent, IoFor reverse current, RsEquivalent series internal resistance, q is electronic electric quantity, N is quality factor of photodiode, k is Boltzmann constant, T is test temperature, NsIs the number of cells in the photovoltaic cell, KiFor temperature coefficient of short-circuit current, TrefFor standard test temperatures (e.g. T)refCan be taken at 25 ℃), S is the intensity of solar illumination radiation, SrefFor standard test of intensity of solar radiation (e.g. S)refCan be 1000W/m2)。
In the formulas (6) to (8), the operating voltage V can be variedPVTo obtain the maximum output power of the photovoltaic system.
As shown in FIG. 5, the abscissa is the operating voltage VPVThe ordinate on the left side is the operating Current (Current) IPV(ii) a With operating voltage VPVGradually increase from 0 and workCurrent IPVFrom the initial current (between 200 and 300A) gradually decreases to 0. The ordinate on the right side is Power output (Power); with operating voltage VPVIncreasing from 0, the output Power increases from 0 to the maximum output Power (marked Max Power Point in the figure) and then decreases to 0.
The equivalent series internal resistance R can be calculated by the following formulasAnd a reverse current Io：
Figure BDA0002989907580000143
Figure BDA0002989907580000151
Figure BDA0002989907580000152
Wherein, in the photovoltaic cell, IscnIs a forward current of PN junction, ImIs the output current of the photovoltaic cell at maximum output power, IonFor the forward current of the PN junction under standard test, VmFor the output voltage of the photovoltaic cell at maximum output power, EgIs the band gap, V, of the photoelectric material in the photodiodeocnIs an open circuit voltage.
In one embodiment of the invention, the photovoltaic cell has set parameters, as shown in table 1.
TABLE 1
Figure BDA0002989907580000153
In the execution of step two, the output power P of the energy storage system can be calculatedESS(t)。
Specifically, the calculated output power PPV(since the output power is a time-varying parameter, it can also be represented by PPV(t) represents transportEntering the power balance model to calculate the output power P of the energy storage systemESS(t); the power balance model has the following formula:
PEV(t)＝PPV(t)+PESS(t)+Pgrid(t) (12)
wherein, PEV(t) is the load power (belonging to the user load working condition) of the charging pile, PPV(t) power output of the photovoltaic system, PESS(t) is the output power of the energy storage system, PgridAnd (t) is the output power of the power grid (which can be obtained by performing optimization calculation through an existing intelligent algorithm).
Load power P in charging pileEV(t) and the power grid output power Pgrid(t) when determined, the power output P of the photovoltaic system can be determinedPV(t) calculating the output power P of the energy storage systemESS(t)。
In the execution of step two, the real-time SOC of the energy storage system may be calculated.
Specifically, the real-time SOC may be calculated by the following formula:
Figure BDA0002989907580000161
Figure BDA0002989907580000162
Vbat＝OCVbat-IbatRbat (15)
the energy storage system comprises an energy storage battery, a real-time SOC and an SOC0The real-time battery state of charge value and the initial state of charge value, eta of the energy storage system respectivelycFor the charge-discharge efficiency of energy-storage cells, IbatFor charging and discharging current (I) of energy storage batteriesbat>0 denotes discharge of the energy storage cell, Ibat<0 represents energy storage battery charging), QbatFor charging and discharging the charge, OCV, of energy-storing batteriesbatFor open-circuit voltage of energy-storage cells, PbatFor the output power of the energy storage cell, RbatTo the internal resistance of the energy storage cell, VbatThe working voltage of the energy storage battery.
As shown in FIG. 6, the abscissa is the state of charge SOC, and the ordinate on the left side is the internal discharge resistance (R)dch) Or internal resistance to charging (R)cha) (ii) a As the state of charge SOC increases from 0, RdchOr RchaGradually decreases. The ordinate on the right side is the open circuit voltage OCV of the energy storage cellbat(ii) a Open-circuit voltage OCV as state of charge SOC increases from 0batAnd gradually increases.
In the third step, the output power P of the energy storage system is adjustedESSAnd (t) inputting the real-time SOC into a battery capacity attenuation model, and calculating the replacement period of the energy storage system. Wherein the output power PESS(t) and battery charging and discharging multiplying power and working current I of energy storage systemESS(t) and the real-time SOC is related to absolute values Of change Of battery capacity Δ Ah and DOD (Depth Of Discharge).
The energy storage system has an initial capacity, the energy storage capacity is continuously attenuated in the operation process, a proportional threshold of the initial capacity (the product of the proportional threshold and the initial capacity is a capacity threshold) can be preset, and the time for the energy storage system to be attenuated from the initial capacity to the capacity threshold is set as a replacement period of the energy storage system.
The proportional threshold is 70-90%; for example, the proportional threshold may be 85%, i.e., when the energy storage capacity decays to 85% of the initial capacity, at least one energy storage cell in the energy storage system needs to be replaced.
Specifically, a battery capacity attenuation model can be established to calculate the degradation degree of the battery of the light storage and charging micro-grid under the working condition of dynamic operation, so that the replacement period of the energy storage system or the energy storage battery is calculated. In this way, the replacement cost of the energy storage system or the energy storage battery can be made more accurate.
The battery capacity fading model is a Combined Arrhenius-Peukert-NREL discrete model including an empirical formula (PLET) based on Peukert's law associated with DOD (Depth Of Discharge), an NREL model associated with total available ampere hours and SOC Of the battery, and an Arrhenius model associated with battery charge and Discharge rate.
Can be calculated by the following formula
Figure BDA0002989907580000171
Figure BDA0002989907580000172
Wherein the content of the first and second substances,
Figure BDA0002989907580000173
and
Figure BDA0002989907580000174
respectively, the battery capacity attenuation based on a combined CAPN model, the battery capacity attenuation based on an Arrhenius model, the battery capacity attenuation based on a PLET empirical formula and the battery capacity attenuation based on an NREL model, wherein T is discrete time, lambda1、λ2And λ3Are the corresponding adjustment parameters.
Can be calculated by the following formula
Figure BDA0002989907580000175
Figure BDA0002989907580000176
Figure BDA0002989907580000177
Wherein z is an index parameter to be identified, A is a calibrated parameter, EaFor activation energy, R is the gas constant, TamIs the absolute temperature of the environment and is,
Figure BDA0002989907580000178
delta. for the accumulated capacity fade at time T-1Ah is the absolute value of the change in battery capacity from T-1 to T, IESSAnd (t) is the working current of the energy storage system.
Can be calculated by the following formula
Figure BDA0002989907580000181
Figure BDA0002989907580000182
Wherein the content of the first and second substances,
Figure BDA0002989907580000183
and kpAll are empirically determined constants, and Δ dod (t) is the change of the energy storage battery SOC in the interval of time t.
Can be calculated by the following formula
Figure BDA0002989907580000184
Figure BDA0002989907580000185
Figure BDA0002989907580000186
Wherein, gamma isRAs parameters relating to the nominal battery capacity, the nominal depth of discharge and the nominal number of cycles, DAAnd CADOD and battery capacity and, C, respectively, during actual charging and dischargingRAnd DRAre respectively measured atRThe rated capacity and the rated discharge depth are set in the process, and other parameters are obtained from experimental calibrated empirical parameters.
The battery capacity fading model needs to calibrate physical parameters conforming to the actual fading process according to experiments.
As shown in fig. 7, the abscissa is the number of cycles (Cycle number), and the ordinate is the battery State of health parameter (State of health); the battery state of health parameter gradually decreases as the number of cycles of charge and discharge gradually increases. Wherein the Root Mean Square Error (RMSE) of the values scaled by experimental data (experimental data) and calculated based on the CAPN discrete model (CAPN model) is 0.15%.
In one embodiment, as shown in table 2, the battery capacity fade model has the following parameters and corresponding values.
TABLE 2
Figure BDA0002989907580000187
Figure BDA0002989907580000191
In the execution of the step four, the replacement period and the economic relevant parameters are input into an objective function relevant to the net present value of the cost, and the objective function calculates the lowest total net present value cost at the configuration point based on iterative optimization; wherein the objective function is a function related to the net present value of the cost, and the objective function is a function based on the sum of the net present values of the total initial investment cost, the total replacement cost and the total operation and maintenance cost, and takes the lowest net present value cost.
Specifically, the lowest total net present value cost min { NPC } at the configuration point may be calculated by the following equation:
min{NPC}＝min{Cinv+NPCrep+NPCo&m} (22)
wherein, CinvFor total initial investment cost, NPCrepNet present value of total replacement cost, NPCo&mThe net current value of the total operation and maintenance cost is obtained; all three parameters belong to economically relevant parameters.
The total initial investment cost C can be calculated by the following formulainv：
Cinv＝Cinv,PV+Cinv,ESS+Cinv,DC/DC (23)
Wherein, Cinv,PVFor initial investment costs of the photovoltaic system, Cinv,ESSFor initial investment costs of the energy storage system, Cinv,DC/DCIs the initial investment cost of the DC-DC converter.
In particular implementations, the total initial investment cost C may beinvCalculating by day and assuming that the cash flow change for each year is equal, the total initial investment cost C can be calculated by the following formulainv：
Figure BDA0002989907580000192
Wherein, Cinv,yearFor annual investment costs of the photovoltaic system, r is the risk free return rate, and N is the number of years from the start of the investment to the end of the project.
Assuming that the cost or value does not vary greatly over the course of a year, the investment cost C for a day can be calculated by the following formulainv,day：
Figure BDA0002989907580000201
The energy storage system has a relatively short service life compared to photovoltaic systems and DC/DC converters having a longer service life. Different from the prior art, in the embodiment of the present invention, the replacement cost of the energy storage system based on the life cycle of the energy storage system is considered, including the replacement cycle of the energy storage system, the Rate of price depreciation (ROD), the remaining value of the energy storage battery at the end of life, and the like, where the replacement cycle of the energy storage system includes the replacement cycle L of at least one energy storage battery in the energy storage systemrep。
The net present value of total replacement cost NPC can be calculated by the following formularep：
Figure BDA0002989907580000202
Wherein N isrepThe replacement times of the energy storage battery in the whole life cycle of the energy storage system, i is the risk-free interest rate, and L is the life of the energy storage systemAnd (4) the service life.
The number of permutations N can be calculated by the following formularep：
Figure BDA0002989907580000203
Due to the replacement of the energy storage battery, the initial investment cost of the energy storage system changes; the initial investment cost of the energy storage system after the change can be calculated by the following formula
Figure BDA0002989907580000204
Figure BDA0002989907580000205
Similarly, the initial investment cost may also be reduced
Figure BDA0002989907580000206
Converted to a cost per day.
In the process of microgrid operation, the cost C of electricity purchase from the microgrid to the power grid can be consideredgridAnd the annual average maintenance costs C of the photovoltaic system, the energy storage system and the DC/DC converterm。
The net present value NPC of the total operation and maintenance cost can be calculated by the following formulao&m：
Figure BDA0002989907580000211
Wherein Inf is the annual average inflation rate.
The annual average maintenance cost C can be calculated by the following formulam：
Cm＝Cm,PV+Cm,ESS+Cm,DC/DC (30)
Wherein, Cm,PVFor annual average maintenance costs of photovoltaic systems, Cm,ESSFor the annual average maintenance cost of the energy storage system, Cm,DC/DCThe annual average maintenance cost of the DC/DC converter.
The net present cost per day NPC can be calculated by the following formuladay：
Figure BDA0002989907580000212
In one embodiment, as shown in tables 3 and 4, the optical storage and charging microgrid has the following economically relevant parameters and corresponding values.
TABLE 3
Figure BDA0002989907580000213
Figure BDA0002989907580000221
TABLE 4
Figure BDA0002989907580000222
In the execution of step four, the objective function may calculate the lowest total net present value cost at the configuration point based on iterative optimization if a constraint is satisfied, the constraint comprising at least one of the following formulas:
-PDC/DC≤PESS≤PDC/DC (32)
-Pwire≤Pgrid≤Pwire (33)
Figure BDA0002989907580000223
SOCmin(t)≦SOC(t)≦SOCmax(t)(35)
Figure BDA0002989907580000224
Figure BDA0002989907580000225
wherein, PDC/DCIs the output power of the DC-DC converter, PESSIs the output power of the energy storage system, PwireFor transmission power of network cables, PgridThe output power of the power grid, delta DOD is the variation of the charging and discharging depth of the energy storage battery in the energy storage system, SOC (t) is the battery state of charge (SOC) of the energy storage system changing along with time, and SOCmin(t) is the minimum value of the SOC of the energy storage system at the end of operation, SOCmax(t) is the maximum value of the SOC of the energy storage system at the end of operation, LPSP is a ratio, Sloss(t) the energy of the microgrid short of electricity at time t, Sdemand(t) the energy to be charged in the grid at time t, LPSPmaxMaximum value of LPSP, LPPV is the ratio of photovoltaic power generation loss, PPV,loss(t) energy not stored in the energy storage system or not utilized at time t, PPV(t) energy of photovoltaic system generation at time t, LPPVmaxT is the maximum value of LPPV and T is the number of time instants.
In one embodiment, as shown in Table 5, the objective function may have constraints and corresponding values that need to be satisfied as follows.
TABLE 5
Figure BDA0002989907580000231
In the execution of the step four, the objective function is subjected to iterative optimization through an intelligent optimization algorithm to calculate the lowest total net present value cost at the configuration point, wherein the intelligent optimization algorithm comprises a particle swarm optimization algorithm, a genetic algorithm and an annealing algorithm.
As shown in fig. 8, iterative optimization based on a particle swarm optimization algorithm can be performed to calculate the lowest total net present cost at the configuration point.
The Particle Swarm Optimization (PSO) is a Swarm intelligent evolution algorithm, which is often applied to search for the global optimal solution of large-scale data, and the basic idea is based on the process of enabling the whole to approach the optimal solution by sharing information by individuals in bird predation behaviors.
In an embodiment of the present invention, a 24 hour P may be searchedgrid(t) photovoltaic output power P obtained by combining the previous clustering algorithmPV(t), the output power P of the energy storage system can be obtained through the formula (12) of the power balance modelESS(t); the lowest total net present value cost min { NPC } and the corresponding 24-dimensional P are calculated by substituting the above calculation into equation (22)grid(corresponding to 24 hours) is represented by (x)1,x2,…,xD)。
Firstly, inputting microgrid model parameters, and initializing a particle swarm composed of economic relevant parameters and reliability optimization variables.
Assuming that the search space of the target has D dimensions (e.g., 24 dimensions based on 24 hours a day), initializing M particles; the current position and velocity of the ith particle can be expressed by the following formulas:
Xm＝(xm1,xm2,…,xmD),m＝1,2,…,M (38)
Vm＝(vm1,vm2,…,vmD),m＝1,2,…,M (39)
wherein M is the number of particles, XmI.e. P corresponding to the m-th particlegrid,VmAnd the random number which corresponds to the m-th particle and is initialized in the search value range.
Next, the generated particle group (such as its position and velocity) is substituted into the power balance model, the output characteristic parameters are calculated, and the fitness of the particle group is calculated based on the objective function related to the net present cost value.
In each iteration process, when the single particle is optimized to obtain the optimal fitness value, the value is recorded as an individual extreme value Pbest(ii) a The individual extremum P can be expressed by the following formulabest：
pbest＝(pm1,pm2,…,pmD),m＝1,2,…,M (40)
Wherein, Pm1、Pm2And PmDRespectively, the position of the m-th particle (i.e., P in the embodiment of the present invention) at which the fitness value (i.e., NPC in the embodiment of the present invention) is minimizedgrid) The value of (a).
When the whole particle swarm is optimized to obtain the optimal position after the search of all the particles in one round is finished, the global extreme value g is recordedbest(ii) a The global extremum g can be expressed by the following formulabest：
gbest＝(pg1,pg2,…,pgD) (41)
Wherein, Pg1、Pg2And PgDRespectively, the position of the global particle (i.e., P in the embodiment of the present invention) when the fitness value (i.e., NPC in the embodiment of the present invention) is minimizedgrid) The value of (a).
Next, the velocity, position of the particle and the position of the globally optimal particle are updated.
The velocity of the update particle can be expressed by the following formula
Figure BDA0002989907580000241
Figure BDA0002989907580000242
Wherein, omega is a weight factor, q is the iteration number,
Figure BDA0002989907580000243
to the particle velocity before update, c1,c2Are weight factors representing sociality and individuality, respectively, r1,r2Are respectively [0,1]A random number within the range of the random number,
Figure BDA0002989907580000244
for the pre-update particle position, XgbestFor globally optimal particle position, Xpbest,mIs the locally optimal particle position.
In equation (42), the updated particle velocity
Figure BDA0002989907580000251
The three items are included, and the inertia of the particles maintaining the self state, the social cooperation attribute tending to the group optimization and the attribute towards the self history optimal state are sequentially represented.
The updated particle position may be represented by the following formula
Figure BDA0002989907580000252
Figure BDA0002989907580000253
Globally optimal particle position XgbestIt can also be updated by equation (43).
In the execution of the step five, repeating the steps one to four until the lowest total net present value cost of each configuration point on the plane is obtained, so as to obtain the optimal energy operation strategy of each configuration point; wherein, the plane is formed by two variables of energy storage capacity and photovoltaic capacity.
Fig. 9 includes sub-diagrams 9a to 9f, which respectively show the discharge power of the energy storage battery, the photovoltaic power generation power, the grid output power and the variation of the battery SOC with time at the configuration point with the optimal cost under six operating conditions (corresponding to the six operating conditions shown in fig. 3); the abscissa is 24 hours a day, the ordinate may be power, such as power generation power of the photovoltaic system PV, power taken from a Grid, power charged to the electric vehicle by the Charging pile Charging, output power of the energy storage system ESS (the output power is positive indicating that the energy storage battery discharges, and negative indicating that the energy storage battery charges), and the ordinate may also be state of charge SOC (the value of the output power may be controlled between 10% and 90%) of the energy storage battery.
FIG. 10 includes sub-graphs 10a through 10f, which respectively show three-dimensional plots of the lowest total net present cost with respect to the configuration points for six conditions (corresponding to the six conditions shown in FIG. 3), in which the relationship of the lowest total net present cost for each configuration point as a function of energy storage capacity and photovoltaic capacity is reflected; wherein, the X coordinate is the capacity (i.e. energy storage capacity) of the energy storage system ESS, the Y coordinate is the installed capacity (i.e. photovoltaic capacity) of the photovoltaic system PV, and the Z coordinate is the net annual present value cost (i.e. the lowest total present value cost).
In the XY plane of sub-graphs 10a to 10f, the two variables of the energy storage capacity and the photovoltaic capacity may be changed respectively, and different combinations of the two variables form a plurality of configuration points in the light storage and charging microgrid.
In the XY plane of sub-graphs 10a through 10f, the plurality of curves shown are contour lines (i.e., a plurality of lines corresponding to equal net present annual value costs).
In the XYZ space of sub-graphs 10a to 10f, the different surfaces presented are changed surfaces whose net present annual value costs are based on different configuration points, and the lowest point in the surfaces is the configuration point which is expected to be obtained and whose net present annual value costs are optimal.
And in the execution of the step six, comparing the lowest total net present value cost at each configuration point on the plane, thereby obtaining the configuration point with the optimal cost and relevant configuration information at the configuration point, wherein the relevant configuration information comprises all economic relevant parameters such as the total cost of microgrid investment, total income, investment recovery time limit, investment return rate and the like.
The lowest total net present value cost at each configuration point may be compared based on the three-dimensional schematic of fig. 10 to obtain a cost-optimized configuration point and associated configuration information at that configuration point.
In one embodiment, as shown in table 6, the values of the configuration parameters at the configuration point with the optimal cost under six conditions are obtained, and the configuration parameters include technical parameters and economic related parameters.
TABLE 6
Figure BDA0002989907580000261
The embodiment of the invention also discloses equipment for configuring the capacity of the optical storage and charging microgrid, which comprises a memory and a processor, wherein computer instructions capable of running on the processor are stored in the memory. The processor, when executing the computer instructions, may perform the steps of the method for configuring optical storage and charging microgrid capacity described above.
The embodiment of the invention also discloses a storage medium for configuring the capacity of the optical storage and charging microgrid, wherein a computer instruction is stored on the storage medium, and the steps of the method for configuring the capacity of the optical storage and charging microgrid can be executed when the computer instruction is operated. The storage medium may include ROM, RAM, magnetic or optical disks, or the like. The storage medium may further include a non-volatile (non-volatile) memory or a non-transitory (non-transient) memory, etc.
The embodiment of the invention also provides a device 300 for configuring the capacity of the optical storage and charging microgrid, wherein the optical storage and charging microgrid comprises a photovoltaic system and an energy storage system.
As shown in fig. 11, the apparatus 300 includes: the first obtaining module 310 is adapted to obtain a photovoltaic capacity of the photovoltaic system and an energy storage capacity of the energy storage system based on a configuration point, and calculate an output power of the photovoltaic system based on the solar illumination radiation intensity and a parameter related to the photovoltaic capacity, wherein the configuration point is a point on a plane formed by two variables, namely the energy storage capacity and the photovoltaic capacity; the first calculation module 320 is adapted to input the output power of the photovoltaic system, the user load condition and the power grid output power obtained by the optimization of the intelligent algorithm into the power balance model, so as to calculate the output power of the energy storage system and calculate the real-time SOC; a second calculation module 330, adapted to input the output power and the real-time SOC of the energy storage system into the battery capacity fade model, so as to calculate a replacement period of the energy storage system; a third calculation module 340 adapted to input the permutation period and the economic-related parameter to an objective function related to net present value of cost, the objective function calculating a lowest total net present value cost at the configuration point based on iterative optimization; a second obtaining module 350, adapted to obtain a lowest total net present value cost at each of the remaining configuration points on the plane; a comparison module 360 adapted to compare the lowest total net present value cost at each configuration point on the plane to obtain a configuration point with the best cost.
In an implementation, the first obtaining module 310 is adapted to calculate the output power P of the photovoltaic system by the following formulaPV：
PPV＝IPVVPV，
Figure BDA0002989907580000271
Figure BDA0002989907580000272
Figure BDA0002989907580000273
Figure BDA0002989907580000281
Figure BDA0002989907580000282
Wherein the photovoltaic system comprises a photovoltaic cell, IPVIs the operating current of the photovoltaic cell, VPVIs the operating voltage of the photovoltaic cell, IphIs photocurrent, IoFor reverse current, RsIs the resistance of the equivalent series resistor, q is the electronic electric quantity, N is the factor of the photodiode, k is the Boltzmann constant, T is the test temperature, NsIs the number of cells in the photovoltaic cell, KiFor temperature coefficient of short-circuit current, TrefFor standard test temperature, IscnIs a forward current of PN junction, ImIs the output current of the photovoltaic cell at maximum output power, IonFor the forward current of the PN junction under standard test, VmFor the output voltage of the photovoltaic cell at maximum output power, EgIs the band gap, V, of the photoelectric material in the photodiodeocnIs open circuit voltage, S is intensity of solar radiation, SrefThe intensity of the solar illumination radiation under the standard test is shown.
In a specific implementation, the power balance model has the following formula:
PEV(t)＝PPV(t)+PESS(t)+Pgrid(t)，
wherein, PEV(t) load power, P, of the charging pile under the user load conditionPV(t) power output of the photovoltaic system, PESS(t) is the output power of the energy storage system, PgridAnd (t) is the output power of the power grid.
In a specific implementation, the first calculation module 320 is adapted to calculate the real-time SOC of the energy storage system by the following formula:
Figure BDA0002989907580000283
Figure BDA0002989907580000284
Vbat＝OCVbat-IbatRbat，
the energy storage system comprises an energy storage battery, a real-time SOC and an SOC0The real-time battery state of charge value and the initial state of charge value, eta of the energy storage system respectivelycFor the charge-discharge efficiency of energy-storage cells, IbatFor charging and discharging currents of energy-storing batteries, QbatFor charging and discharging the charge, OCV, of energy-storing batteriesbatFor open-circuit voltage of energy-storage cells, PbatFor the output power of the energy storage cell, RbatIs the internal resistance of the energy storage battery.
In a specific implementation, the second calculation module 330 is adapted to set the time for the energy storage capacity to decay from the initial capacity to the proportional threshold as a replacement period of the energy storage system.
In a specific implementation, the objective function takes the lowest total net present value cost based on the sum of the total initial investment cost, the total net present value of replacement cost and the total net present value of operation and maintenance cost, and the third calculation module 340 is adapted to calculate the lowest total net present value cost min { NPC } at the configuration point by the following formula:
min{NPC}＝min{Cinv+NPCrep+NPCo&m}，
Cinv＝Cinv,PV+Cinv,ESS+Cinv,DC/DC，
Figure BDA0002989907580000291
Figure BDA0002989907580000292
Figure BDA0002989907580000293
wherein, CinvFor total initial investment cost, NPCrepNet present value of total replacement cost, NPCo&mThe net present value of the total operation and maintenance cost, Cinv,PVFor initial investment costs of the photovoltaic system, Cinv,ESSFor initial investment costs of the energy storage system, Cinv,DC/DCFor initial investment cost of the DC-DC converter, ROD is price depreciation, NrepThe number of times of replacement of the energy storage system in the life cycle, L is the life cycle of the energy storage system, LrepFor a replacement cycle of the energy storage system, CgridCost of purchasing electricity from the microgrid to the grid, CmThe annual average maintenance cost of the energy storage system, the photovoltaic system and the DC/DC converter is shown, and the Inf is the annual average currency expansion rate.
In a specific implementation, the third calculation module 340 is adapted to calculate the lowest total net present value cost at the configuration point based on the iterative optimization if the objective function satisfies a constraint, the constraint comprising at least one of the following formulas:
-PDC/DC≤PESS≤PDC/DC，
-Pwire≤Pgrid≤Pwire，
Figure BDA0002989907580000301
SOCmin(t)≦SOC(t)≦SOCmax(t)，
Figure BDA0002989907580000302
Figure BDA0002989907580000303
wherein, PDC/DCIs the output power of the DC-DC converter, PESSIs the output power of the energy storage system, PwireFor transmission power of network cables, PgridThe output power of the power grid, delta DOD is the variation of the charging and discharging depth of the energy storage battery in the energy storage system, SOC (t) is the battery state of charge (SOC) of the energy storage system changing along with time, and SOCmin(t) is the minimum value of the SOC of the energy storage system at the end of operation, SOCmax(t) is the maximum value of the SOC of the energy storage system at the end of operation, LPSP is a ratio, Sloss(t) the energy of the microgrid short of electricity at time t, Sdemand(t) the energy to be charged in the microgrid at time t, LPSPmaxMaximum value of LPSP, LPPV is the ratio of photovoltaic power generation loss, PPV,loss(t) energy not stored in the energy storage system or not utilized at time t, PPV(t) energy of photovoltaic system generation at time t, LPPVmaxT is the maximum value of LPPV and T is the number of time instants.
In a specific implementation, the third calculation module 340 is adapted to calculate the lowest total net present value cost at the configuration point by performing iterative optimization on the objective function through intelligent optimization algorithms, where the intelligent optimization algorithms include a particle swarm optimization algorithm, a genetic algorithm, and an annealing algorithm.
For more details on the working principle and the working mode of the apparatus 300 for configuring the capacity of the optical storage and charging microgrid, reference may be made to the above description on the method for configuring the capacity of the optical storage and charging microgrid, and further description is omitted here.
Although the present invention is disclosed above, the present invention is not limited thereto. Various changes and modifications may be effected therein by one skilled in the art without departing from the spirit and scope of the invention as defined in the appended claims.

Claims (18)

1. A method for configuring the capacity of an optical storage and charging microgrid, wherein the optical storage and charging microgrid comprises a photovoltaic system and an energy storage system, and the method comprises the following steps:
the method comprises the steps of firstly, acquiring photovoltaic capacity of a photovoltaic system and energy storage capacity of an energy storage system based on a configuration point, and calculating output power of the photovoltaic system based on solar illumination radiation intensity and parameters related to the photovoltaic capacity, wherein the configuration point is a point on a plane formed by two variables of the energy storage capacity and the photovoltaic capacity;
inputting the output power of the photovoltaic system, the user load working condition and the power grid output power obtained by the optimization of the intelligent algorithm into a power balance model, thereby calculating the output power of the energy storage system and calculating the real-time SOC;
inputting the output power of the energy storage system and the real-time SOC into a battery capacity attenuation model, thereby calculating the replacement period of the energy storage system;
inputting the replacement period and the economic relevant parameters into an objective function relevant to the net present value of the cost, and calculating the lowest total net present value cost at the configuration point by the objective function based on iterative optimization;
step five, repeating the steps one to four until the lowest total net present value cost of each configuration point on the plane is obtained;
and step six, comparing the lowest total net present value cost at each configuration point on the plane to obtain the configuration point with the optimal cost.
2. The method of claim 1, wherein step one comprises calculating the power output P of the photovoltaic system by the following equationPV：
PPV＝IPVVPV，
Figure FDA0002989907570000011
Figure FDA0002989907570000012
Figure FDA0002989907570000021
Figure FDA0002989907570000022
Figure FDA0002989907570000023
Wherein the photovoltaic system comprises a photovoltaic cell, IPVIs the operating current of the photovoltaic cell, VPVIs the operating voltage of the photovoltaic cell, IphIs photocurrent, IoFor reverse current, RsIs the resistance of the equivalent series resistor, q is the electronic electric quantity, N is the factor of the photodiode, k is the Boltzmann constant, T is the test temperature, NsThe number of the cells in the photovoltaic cell, KiFor temperature coefficient of short-circuit current, TrefFor standard test temperature, IscnIs a forward current of PN junction, ImIs the output current, I, of the photovoltaic cell at maximum output poweronFor the forward current of the PN junction under standard test, VmIs the output voltage of the photovoltaic cell at maximum output power, EgIs the band gap, V, of the photoelectric material in the photodiodeocnIs open circuit voltage, S is the intensity of the solar radiation, SrefThe intensity of the solar illumination radiation under the standard test is shown.
3. The method of claim 1, wherein the power balance model has the following formula:
PEV(t)＝PPV(t)+PESS(t)+Pgrid(t)，
wherein, PEV(t) is the load power of the charging pile in the user load condition, PPV(t) is the output power of the photovoltaic system, PESS(t) is the output power of the energy storage system, PgridAnd (t) is the output power of the power grid.
4. The method of claim 3, wherein step two calculates the real-time SOC of the energy storage system by the following formula:
Figure FDA0002989907570000031
Figure FDA0002989907570000032
Vbat＝OCVbat-IbatRbat，
the energy storage system comprises an energy storage battery, a real-time SOC and an SOC0Respectively a real-time battery state-of-charge value and an initial state-of-charge value, η, of the energy storage systemcFor the charging and discharging efficiency of the energy storage cell, IbatFor charging and discharging current, Q, of said energy storage cellbatCharge, discharge, OCV, of said energy storage cellbatIs the open circuit voltage, P, of the energy storage cellbatIs the output power, R, of the energy storage cellbatIs the internal resistance, V, of the energy storage cellbatThe working voltage of the energy storage battery.
5. The method of claim 1, wherein step three comprises: setting the time for the energy storage capacity to decay from the initial capacity to a proportional threshold as a displacement period of the energy storage system.
6. The method of claim 4, wherein the objective function takes a lowest total net present value cost based on a sum of total initial investment cost, total replacement cost net present value, and total operation and maintenance cost net present value, and wherein the fourth step comprises calculating a lowest total net present value cost min { NPC } at the configuration point by:
min{NPC}＝min{Cinv+NPCrep+NPCo&m}，
Cinv＝Cinv,PV+Cinv,ESS+Cinv,DC/DC，
Figure FDA0002989907570000033
Figure FDA0002989907570000034
Figure FDA0002989907570000041
wherein, CinvFor the total initial investment cost, NPCrepFor the net present value of the total replacement cost, NPCo&mIs the net present value of the total operation and maintenance cost, Cinv,PVFor the initial investment cost of the photovoltaic system, Cinv,ESSFor the initial investment cost of the energy storage system, Cinv,DC/DCFor initial investment cost of the DC-DC converter, ROD is price depreciation, NrepIs the number of times of replacement of the energy storage system in the life cycle, L is the life cycle of the energy storage system, LrepIs the replacement cycle of the energy storage system, i is the risk free rate, CgridCost of purchasing electricity from the microgrid to the grid, CmFor the annual average maintenance cost of the energy storage system, the photovoltaic system and the DC/DC converter, Inf is the annual average traffic expansion rate.
7. The method of claim 6, wherein the step four comprises calculating a lowest total net present value cost at the configuration point based on iterative optimization if the objective function satisfies a constraint, the constraint comprising at least one of the following formulas:
-PDC/DC≤PESS≤PDC/DC，
-Pwire≤Pgrid≤Pwire，
Figure FDA0002989907570000042
SOCmin(t)≦SOC(t)≦SOCmax(t)，
Figure FDA0002989907570000043
Figure FDA0002989907570000044
wherein, PDC/DCIs the output power of the DC-DC converter, PESSIs the output power of the energy storage system, PwireFor transmission power of network cables, PgridThe output power of a power grid, delta DOD (delta DOD) is the variation of the charging and discharging depth of an energy storage battery in the energy storage system, SOC (t) is the battery state of charge quantity of the energy storage system changing along with time, and SOCmin(t) is the minimum value of the SOC of the energy storage system at the end of operation, SOCmax(t) is the maximum value of the SOC of the energy storage system at the end of operation, LPSP is a ratio, Sloss(t) the energy of the microgrid short of electricity at time t, Sdemand(t) the energy to be charged in the microgrid at time t, LPSPmaxMaximum value of LPSP, LPPV is the ratio of photovoltaic power generation loss, PPV,loss(t) is not stored in the energy storage system at time tSystematic or unutilized energy, PPV(t) energy generated by the photovoltaic system at time t, LPPVmaxT is the maximum value of LPPV and T is the number of time instants.
8. The method of claim 7, wherein the fourth step comprises: and the objective function is subjected to iterative optimization through an intelligent optimization algorithm to calculate the lowest total net present value cost at the configuration point, wherein the intelligent optimization algorithm comprises a particle swarm optimization algorithm, a genetic algorithm and an annealing algorithm.
9. An apparatus comprising a memory and a processor, the memory having stored thereon computer instructions executable on the processor, wherein the processor, when executing the computer instructions, performs the steps of the method of any one of claims 1 to 8.
10. A storage medium having stored thereon computer instructions, wherein the computer instructions when executed perform the steps of the method of any one of claims 1 to 8.
11. An apparatus for configuring the capacity of a light storage and charging microgrid, the light storage and charging microgrid comprising a photovoltaic system and an energy storage system, the apparatus comprising:
the first acquisition module is suitable for acquiring the photovoltaic capacity of the photovoltaic system and the energy storage capacity of the energy storage system based on a configuration point, and calculating the output power of the photovoltaic system based on the solar illumination radiation intensity and parameters related to the photovoltaic capacity, wherein the configuration point is a point on a plane formed by two variables, namely the energy storage capacity and the photovoltaic capacity;
the first calculation module is suitable for inputting the output power of the photovoltaic system, the user load working condition and the power grid output power obtained by intelligent algorithm optimization into a power balance model so as to calculate the output power of the energy storage system and calculate the real-time SOC;
a second calculation module, which is suitable for inputting the output power of the energy storage system and the real-time SOC into a battery capacity attenuation model so as to calculate a replacement period of the energy storage system;
a third calculation module adapted to input the permutation period and the economic-related parameter to an objective function related to a net present value of cost, the objective function calculating a lowest total net present value cost at the configuration point based on iterative optimization;
a second obtaining module, adapted to obtain a lowest total net present value cost at each of the remaining configuration points on the plane;
and the comparison module is suitable for comparing the lowest total net present value cost at each configuration point on the plane to obtain the configuration point with the optimal cost.
12. The apparatus according to claim 11, wherein the first obtaining module is adapted to calculate the power output P of the photovoltaic system by the following formulaPV：
PPV＝IPVVPV，
Figure FDA0002989907570000061
Figure FDA0002989907570000062
Figure FDA0002989907570000063
Figure FDA0002989907570000064
Figure FDA0002989907570000071
Wherein the photovoltaic system comprises a photovoltaic cell, IPVIs the operating current of the photovoltaic cell, VPVIs the operating voltage of the photovoltaic cell, IphIs photocurrent, IoFor reverse current, RsIs the resistance of the equivalent series resistor, q is the electronic electric quantity, N is the factor of the photodiode, k is the Boltzmann constant, T is the test temperature, NsThe number of the cells in the photovoltaic cell, KiFor temperature coefficient of short-circuit current, TrefFor standard test temperature, IscnIs a forward current of PN junction, ImIs the output current, I, of the photovoltaic cell at maximum output poweronFor the forward current of the PN junction under standard test, VmIs the output voltage of the photovoltaic cell at maximum output power, EgIs the band gap, V, of the photoelectric material in the photodiodeocnIs open circuit voltage, S is the intensity of the solar radiation, SrefThe intensity of the solar illumination radiation under the standard test is shown.
13. The apparatus of claim 11, wherein the power balancing model has the following formula:
PEV(t)＝PPV(t)+PESS(t)+Pgrid(t)，
wherein, PEV(t) is the load power of the charging pile in the user load condition, PPV(t) is the output power of the photovoltaic system, PESS(t) is the output power of the energy storage system, PgridAnd (t) is the output power of the power grid.
14. The apparatus of claim 13, wherein the first calculation module is adapted to calculate the real-time SOC of the energy storage system by the formula:
Figure FDA0002989907570000072
Figure FDA0002989907570000073
Vbat＝OCVbat-IbatRbat，
the energy storage system comprises an energy storage battery, a real-time SOC and an SOC0Respectively a real-time battery state-of-charge value and an initial state-of-charge value, η, of the energy storage systemcFor the charging and discharging efficiency of the energy storage cell, IbatFor charging and discharging current, Q, of said energy storage cellbatCharge, discharge, OCV, of said energy storage cellbatIs the open circuit voltage, P, of the energy storage cellbatIs the output power, R, of the energy storage cellbatIs the internal resistance, V, of the energy storage cellbatThe working voltage of the energy storage battery.
15. The apparatus of claim 11, wherein the second calculation module is adapted to set a time for the energy storage capacity to decay from an initial capacity to a proportional threshold as a replacement period for the energy storage system.
16. The apparatus of claim 14, wherein the objective function takes a lowest total net present value cost based on a sum of total initial investment cost, total replacement cost net present value and total operation and maintenance cost net present value, and wherein the third calculation module is adapted to calculate the lowest total net present value cost min { NPC } at the configuration point by:
min{NPC}＝min{Cinv+NPCrep+NPCo&m}，
Cinv＝Cinv,PV+Cinv,ESS+Cinv,DC/DC，
Figure FDA0002989907570000081
Figure FDA0002989907570000082
Figure FDA0002989907570000083
wherein, CinvFor the total initial investment cost, NPCrepFor the net present value of the total replacement cost, NPCo&mIs the net present value of the total operation and maintenance cost, Cinv,PVFor the initial investment cost of the photovoltaic system, Cinv,ESSFor the initial investment cost of the energy storage system, Cinv,DC/DCFor initial investment cost of the DC-DC converter, ROD is price depreciation, NrepIs the number of times of replacement of the energy storage system in the life cycle, L is the life cycle of the energy storage system, LrepIs the replacement cycle of the energy storage system, i is the risk free rate, CgridCost of purchasing electricity from the microgrid to the grid, CmFor the annual average maintenance cost of the energy storage system, the photovoltaic system and the DC/DC converter, Inf is the annual average traffic expansion rate.
17. The apparatus of claim 16, wherein the third calculation module is adapted to calculate the lowest total net present cost at the configuration point based on iterative optimization if the objective function satisfies a constraint, the constraint comprising at least one of the following formulas:
-PDC/DC≤PESS≤PDC/DC，
-Pwire≤Pgrid≤Pwire，
Figure FDA0002989907570000091
SOCmin(t)≦SOC(t)≦SOCmax(t)，
Figure FDA0002989907570000092
Figure FDA0002989907570000093
wherein, PDC/DCIs the output power of the DC-DC converter, PESSIs the output power of the energy storage system, PwireFor transmission power of network cables, PgridThe output power of a power grid, delta DOD (delta DOD) is the variation of the charging and discharging depth of an energy storage battery in the energy storage system, SOC (t) is the battery state of charge quantity of the energy storage system changing along with time, and SOCmin(t) is the minimum value of the SOC of the energy storage system at the end of operation, SOCmax(t) is the maximum value of the SOC of the energy storage system at the end of operation, LPSP is a ratio, Sloss(t) the energy of the microgrid short of electricity at time t, Sdemand(t) the energy to be charged in the microgrid at time t, LPSPmaxMaximum value of LPSP, LPPV is the ratio of photovoltaic power generation loss, PPV,loss(t) energy not stored in the energy storage system or not utilized at time t, PPV(t) energy generated by the photovoltaic system at time t, LPPVmaxT is the maximum value of LPPV and T is the number of time instants.
18. The apparatus of claim 17, wherein the third computing module is adapted to compute the lowest total net present value cost at the configuration point by iterative optimization of the objective function through intelligent optimization algorithms, the intelligent optimization algorithms comprising a particle swarm optimization algorithm, a genetic algorithm, and an annealing algorithm.
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