CN108054779B - Optical storage charging and replacing power station and cloud energy storage system optimization control method thereof - Google Patents
Optical storage charging and replacing power station and cloud energy storage system optimization control method thereof Download PDFInfo
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- B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
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Abstract
The invention provides a light storage charging and converting station and a cloud energy storage system optimization control method thereof.A secondary regression relation model of the surplus of a battery pack, photovoltaic power generation and electric vehicle charging and converting quantity is fitted by an energy management system according to historical data acquired by the light storage charging and converting station through an optimization algorithm, and then real-time battery capacity for providing cloud energy storage service is obtained by combining real-time data acquired by the light storage charging and converting station; when the capacity requirement of a cloud energy storage user is received, an optimization decision can be made according to the real-time battery capacity, and quick response is carried out. The problem of among the prior art because hold the capacity of real-time idle battery resource unclear, lead to unable scientific and reasonable quick response cloud energy storage user demand is solved.
Description
Technical Field
The invention relates to the technical field of photovoltaic power generation systems, in particular to a light storage charging and converting station and a cloud energy storage system optimization control method thereof.
Background
Along with the more and more extensive application of electric automobile, its supporting facility also appears more and more in people's life, for example there is small-size alternating-current charging stake near the parking stall of various power consumption districts now mostly. In the face of the increasing number of electric vehicles, large-scale optical storage, charging and battery replacement stations are also gradually popularized.
In the existing optical storage charging and replacing power station, a photovoltaic array, an energy storage device or a power grid generally provides electric energy for various electric automobiles through a charging pile; and the cloud energy storage users connected with the power grid can be rented with the cloud energy storage service use rights of the energy storage devices and the idle batteries according to the capacity and the duration by combining the cloud energy storage systems in a few power stations, so that the cloud energy storage users also become service objects.
However, in the cloud energy storage service in the prior art, since the capacity of the real-time idle battery resource is not clearly mastered, the demands of cloud energy storage users cannot be scientifically and reasonably quickly responded, and the waste of system resources and the reduction of the income of the cloud energy storage service are caused.
Disclosure of Invention
The invention provides an optical storage charging and converting station and an optimization control method of a cloud energy storage system thereof, and aims to solve the problem that the cloud energy storage user demand cannot be scientifically and reasonably quickly responded due to unclear control over real-time idle battery resource capacity in the prior art.
In order to achieve the purpose, the technical scheme provided by the application is as follows:
a light storing, charging and replacing power station comprising: the system comprises a photovoltaic array and a converter thereof, an energy storage device and a converter thereof, a battery replacing group and a converter thereof, an energy management system, a charging pile and a controllable switch; wherein:
the surplus of the battery replacing group and the surplus of the energy storage device are idle battery resources;
the photovoltaic array, the energy storage device and the battery replacing group are respectively connected with a bus through respective converters;
the charging pile is connected with the bus and used for providing charging and exchanging electric quantity for the electric automobile;
the input and output ports of the controllable switch are respectively connected with the power grid and the bus;
the energy management system is respectively connected with the control end of the controllable switch and each converter;
the energy management system is to: fitting a quadratic regression relation model of the battery pack residue, photovoltaic power generation amount and electric vehicle charging and replacing quantity through an optimization algorithm according to historical collected data of the optical storage charging and replacing station, and combining real-time collected data of the optical storage charging and replacing station to obtain real-time battery capacity for providing cloud energy storage service; when the capacity requirement of a cloud energy storage user is received, an optimization decision can be made according to the real-time battery capacity, and quick response is carried out.
Preferably, the energy management system is further configured to: and fitting a quadratic regression relation model of the allowance of the energy storage device, the photovoltaic power generation and the charging and replacing electric quantity of the electric automobile through an optimization algorithm according to the historical collected data of the light storage charging and replacing power station.
Preferably, the process of obtaining the quadratic regression relationship model of the battery pack replacement allowance and the photovoltaic power generation amount and the electric vehicle charging and exchanging capacity and the quadratic regression relationship model of the energy storage device allowance and the photovoltaic power generation amount and the electric vehicle charging and exchanging capacity through the energy management system by fitting is as follows:
according to historical collected data of the light storage charging and replacing station, the photovoltaic power generation capacity and the charging and replacing capacity of the electric automobile are used as two independent variables, the battery replacing battery pack allowance and the energy storage device allowance are used as two objective functions, and a response surface method is used for carrying out multi-parameter working condition optimization design;
and performing data analysis by using Design Expert software to determine a quadratic regression relationship model and a corresponding response surface map of the battery pack replacement allowance, the photovoltaic power generation amount and the electric vehicle charging and replacing quantity, and a quadratic regression relationship model and a corresponding response surface map of the energy storage device allowance, the photovoltaic power generation amount and the electric vehicle charging and replacing quantity.
Preferably, the bus bar includes: a DC bus and an AC bus;
the converter of the photovoltaic array comprises: the photovoltaic inverter is connected with the alternating current bus, and the DC/DC converter is connected with the direct current bus;
the converter of the energy storage device includes: the energy storage converter is connected with the alternating current bus, and the DC/DC converter is connected with the direct current bus;
the converter for replacing the battery pack comprises: the AC/DC converter is connected with the alternating current bus, and the DC/DC converter is connected with the direct current bus;
fill electric pile includes: the alternating current charging pile is connected with the alternating current bus, and the direct current charging pile is connected with the direct current bus.
Preferably, the energy management system comprises: the system comprises a microgrid controller, an engineer station and a real-time server; wherein:
the microgrid controller is used for completing off-grid detection and automatic grid connection functions, realizing stable operation under different working conditions in the system, and monitoring and coordinately controlling each converter and the charging pile;
the engineer station and the real-time server are used for realizing the collection, analysis, statistics and storage of data in the optical storage charging and replacing station, report making, kinetic energy monitoring and operation and maintenance management.
Preferably, the energy management system further comprises: the bidirectional converter is respectively connected with the alternating current bus, the direct current bus and the energy management system; the bidirectional converter is used for:
when the grid-connected operation is carried out, the P/Q control is adopted, and the photovoltaic power generation power at the direct current side, the output power of the energy storage battery and the output power of the battery replacement pack are merged into a power grid through the alternating current bus and the controllable switch;
when the off-grid operation is carried out, the voltage and frequency stability of the alternating current bus and the direct current bus is supported by adopting V/f control, and the power balance between the alternating current side and the direct current side is realized;
and switching operation between P/Q control and V/f control according to the state of the controllable switch, so that stable switching between grid-connected and off-grid modes is realized.
A cloud energy storage system optimization control method of an optical storage charging and power exchanging station is provided, and the optical storage charging and power exchanging station comprises: the system comprises a photovoltaic array and a converter thereof, an energy storage device and a converter thereof, a battery replacing group and a converter thereof, an energy management system, a charging pile and a controllable switch; the battery replacement allowance and the energy storage device allowance are idle battery resources; the photovoltaic array, the energy storage device and the battery replacing group are respectively connected with a bus through respective converters; the charging pile is connected with the bus and used for providing charging and exchanging electric quantity for the electric automobile; the input and output ports of the controllable switch are respectively connected with the power grid and the bus; the energy management system is respectively connected with the control end of the controllable switch and each converter; the cloud energy storage system optimization control method of the optical storage charging and replacing power station comprises the following steps:
the energy management system fits a quadratic regression relation model of the battery pack residue, photovoltaic power generation and electric vehicle charging and replacing quantity through an optimization algorithm according to the historical collected data of the light storage charging and replacing station;
the energy management system obtains real-time battery capacity for providing cloud energy storage service by combining real-time acquisition data of the optical storage charging and replacing power station according to the secondary regression relation model;
and when the energy management system receives the capacity requirement of the cloud energy storage user, an optimization decision is made according to the real-time battery capacity, and quick response is carried out.
Preferably, before the energy management system obtains the real-time battery capacity providing the cloud energy storage service according to the secondary regression relationship model by combining the real-time collected data of the optical storage charging and converting station, the method further includes:
and the energy management system fits a quadratic regression relation model of the allowance of the energy storage device, the photovoltaic power generation amount and the charging and replacing electric quantity of the electric automobile through an optimization algorithm according to the historical collected data of the light storage charging and replacing power station.
Preferably, the energy management system fits a quadratic regression relationship model of the battery pack remaining amount and the photovoltaic power generation amount and the electric vehicle charging and replacing electric quantity and a quadratic regression relationship model of the energy storage device remaining amount and the photovoltaic power generation amount and the electric vehicle charging and replacing electric quantity through an optimization algorithm according to the historical data collected by the optical storage charging and replacing station, and includes:
the energy management system performs multi-parameter working condition optimization design by using a response surface method according to historical collected data of the light storage charging and replacing power station, the photovoltaic power generation amount and the charging and replacing power amount of the electric automobile as two independent variables, and the battery replacing battery pack allowance and the energy storage device allowance as two objective functions;
the energy management system utilizes Design Expert software to perform data analysis, and determines a quadratic regression relation model and a corresponding response surface graph of the battery pack replacement allowance, the photovoltaic power generation amount and the electric vehicle charging and replacing electric quantity, and a quadratic regression relation model and a corresponding response surface graph of the energy storage device allowance, the photovoltaic power generation amount and the electric vehicle charging and replacing electric quantity.
According to the optical storage charging and replacing power station, an energy management system is used for fitting a secondary regression relation model of the remaining amount of a battery replacing group, photovoltaic power generation amount and electric vehicle charging and replacing power according to historical data acquired by the optical storage charging and replacing power station through an optimization algorithm, and then real-time battery capacity for providing cloud energy storage service is obtained by combining real-time data acquired by the optical storage charging and replacing power station; when the capacity requirement of a cloud energy storage user is received, an optimization decision can be made according to the real-time battery capacity, and quick response is carried out. The problem of among the prior art because hold the capacity of real-time idle battery resource unclear, lead to unable scientific and reasonable quick response cloud energy storage user demand is solved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic structural diagram of an optical storage charging and swapping station provided in an embodiment of the present invention;
fig. 2 is another schematic structural diagram of an optical storage charging and swapping station provided in the embodiment of the present invention;
FIG. 3 is a graph of a response curve for energy storage device headroom provided by an embodiment of the present invention;
fig. 4 is a response surface diagram of the battery pack replacement residual capacity according to the embodiment of the present invention;
fig. 5 is a flowchart of a cloud energy storage system optimization control method for an optical storage charging and replacing power station according to another embodiment of the present invention;
fig. 6 is a flowchart of a cloud energy storage system optimization control method for an optical storage charging and replacing power station according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, 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 application.
The invention provides an optical storage charging and power exchanging station, which aims to solve the problem that the requirement of a cloud energy storage user cannot be scientifically and reasonably quickly responded due to unclear control over the real-time idle battery resource capacity in the prior art.
Specifically, referring to fig. 1, the optical storage charging and replacing power station is characterized by comprising: the system comprises a photovoltaic array 101 and a converter thereof, an energy storage device 102 and a converter thereof, a battery replacing group 103 and a converter thereof, an energy management system 104, a charging pile 105 and a controllable switch 106; wherein:
the surplus of the battery replacement pack and the surplus of the energy storage device are idle battery resources, so that the system can fully schedule the internal idle battery resources to provide service for cloud energy storage users, and the benefit is improved;
the photovoltaic array 101, the energy storage device 102 and the battery replacing group 103 are respectively connected with the bus through respective converters;
the charging pile 105 is connected with the bus and used for providing charging and exchanging electric quantity for the electric automobile;
the input and output ports of the controllable switch 106 are respectively connected with the power grid and the bus;
the energy management system 104 is respectively connected with the control end of the controllable switch 106 and each converter;
the energy management system 104 is configured to: according to historical collected data of the optical storage charging and converting station, fitting a secondary regression relation model of the residual amount of the battery pack, photovoltaic power generation amount and electric vehicle charging and converting amount through an optimization algorithm, and combining real-time collected data of the optical storage charging and converting station to obtain real-time battery capacity providing cloud energy storage service, wherein at the moment, the residual amount of the energy storage device is obtained by the real-time collected data of the optical storage charging and converting station; when the capacity requirement of a cloud energy storage user is received, an optimization decision can be made according to the real-time battery capacity, and quick response is carried out.
Preferably, the energy management system 104 is further configured to: and fitting a quadratic regression relation model of the allowance of the energy storage device, the photovoltaic power generation and the charging and replacing electric quantity of the electric automobile through an optimization algorithm according to the historical collected data of the light storage charging and replacing power station.
Preferably, the process of the energy management system 104 fitting to obtain the quadratic regression relationship model of the battery pack replacement allowance and the photovoltaic power generation amount and the electric vehicle charging and exchanging capacity and the quadratic regression relationship model of the energy storage device allowance and the photovoltaic power generation amount and the electric vehicle charging and exchanging capacity is as follows:
according to historical collected data of the light storage charging and replacing station, photovoltaic power generation and electric vehicle charging and replacing power are used as two independent variables, the remaining amount of the replacing battery pack 103 and the remaining amount of the energy storage device 102 are used as two objective functions, and a response surface method is used for carrying out multi-parameter working condition optimization design;
and performing data analysis by using Design Expert software, determining a quadratic regression relationship model and a corresponding response surface map of the battery pack replacement allowance, the photovoltaic power generation amount and the electric vehicle charging and replacing electric quantity, and a quadratic regression relationship model and a corresponding response surface map of the energy storage device allowance, the photovoltaic power generation amount and the electric vehicle charging and replacing electric quantity, so as to obtain the mutual relationship between the two independent variables and the two objective functions.
The specific working principle is as follows:
a large amount of historical collected data about the charging and exchanging electric quantity and the photovoltaic generating capacity of the electric automobile are stored in the energy management system 104; the energy management system 104 takes the charging and conversion capacity and the photovoltaic power generation capacity of the electric vehicle as independent variables, and the independent variables are respectively expressed as x1、x2. With the argument x1、x2The influence factors are the energy storage device residual capacity and the battery replacement battery pack residual capacity, namely the battery capacity of the cloud energy storage service provided by the optical storage charging and battery replacement station. Then, the energy management system 104 takes the quantitative measure of the battery capacity providing the cloud energy storage service as an objective function, i.e. the energy storage device residual amount Y1And the residual amount Y of battery replacement2Is an objective function, i.e. the argument x1、x2The response value of (2).
The Response Surface Method (RSM) is a data processing method combining mathematics and statistics, and is used for modeling and analyzing the problem that the Response value of an objective function is affected by a plurality of variables and optimizing the Response result.
Selecting a quadratic response surface equation, considering all the primary terms, the secondary terms and pairwise crossing terms, and expressing the response surface equation as follows:
wherein Y is a target function or called response value, and the model is provided with energy storage device residual amounts Y1And the residual amount Y of battery replacement2；XiAs independent variables, the charging and switching electric quantity x of the electric automobile is respectively shown in the model1And photovoltaic power generation x2；βi，βii，βijRegression coefficients representing primary, secondary, interaction terms; k is the number of influencing factors; e is the error. The regression coefficients in the above formula can be obtained by least squares fitting.
For convenience, all variables are normalized as follows:
wherein, XiHAnd XiLRespectively a maximum value and a minimum value of the variable,
is the average of the variables.
Carrying out multi-parameter working condition optimization design by using a response surface method, after an initial model is established, carrying out data analysis by using design expert software, specifically comprising numerical value iterative calculation, judging whether convergence occurs, saving a calculation file after the convergence is judged, outputting a solving calculation result, determining a quadratic regression relation model based on the response surface method result optimization design, and obtaining the mutual relation between two independent variables and a response value, namely the charging and changing capacity x of the electric automobile1And photovoltaic power generation x2And the margin Y of the energy storage device1Corresponding relation of (1), electric vehicle charging and battery replacement quantity x1And photovoltaic power generation x2And the residual amount Y of battery replacement2The corresponding relationship of (a); then, the calculation results are subjected to relevant post-processing to respectively obtain the energy storage device allowance Y1Graph of response surface (see fig. 3) and battery replacement margin Y2The response surface map of (see fig. 4).
The energy management system 104 can obtain the battery capacity of the cloud energy storage service which can be provided by the light storage, charging and replacing station in real time through the secondary regression relation model determined according to the response surface method based on the real-time data acquisition of the light storage, charging and replacing station; and then, quick response can be carried out according to the capacity requirement of the cloud energy storage customer, an optimization decision is made, so that the battery resources in the system are utilized to the maximum extent, and the benefit is ensured.
According to the optical storage charging and power exchanging station provided by the embodiment, the cloud energy storage and optical storage charging and power exchanging station are organically combined through the working principle to form a complete system with comprehensive functions and scientific and reasonable structure; when the capacity requirement of the cloud energy storage user is received, the energy management system 104 can make an optimization decision according to the real-time battery capacity, and perform quick response. The problem of among the prior art because hold the capacity of real-time idle battery resource unclear, lead to unable scientific and reasonable quick response cloud energy storage user demand is solved.
Another embodiment of the present invention further provides a specific optical storage charging and replacing power station, based on the above embodiment and fig. 1, with reference to fig. 2, the bus includes: a DC bus and an AC bus;
the converter of the photovoltaic array 101 comprises: a photovoltaic inverter 211 connected to the ac bus, and a DC/DC converter 212 connected to the DC bus; accordingly, the photovoltaic array 101 in FIG. 1 is divided into photovoltaic arrays 111 and 112 in FIG. 2.
The converter of the energy storage device 102 includes: an energy storage converter 221 connected to the ac bus, and a DC/DC converter 222 connected to the DC bus; accordingly, energy storage device 102 in fig. 1 is divided into energy storage devices 121 and 122 in fig. 2.
The converter that switches the battery pack 103 includes: an AC/DC converter 231 connected to the AC bus, and a DC/DC converter 232 connected to the DC bus; accordingly, the battery replacement sets 103 in fig. 1 are divided into the battery replacement sets 131 and 132 in fig. 2.
The charging pile 105 includes: an ac charging post 151 connected to the ac bus, and a dc charging post 152 connected to the dc bus.
Preferably, as shown in fig. 2, the energy management system 104 includes: the system comprises a microgrid controller, an engineer station, a real-time server and a bidirectional converter, wherein the bidirectional converter is respectively connected with an alternating current bus, a direct current bus and an energy management system 104; in fig. 2, solid lines connecting different devices indicate power cables, and dotted lines indicate communication cables; wherein:
the microgrid controller is used for completing off-grid detection and automatic grid connection functions, realizing stable operation under different working conditions in the system, and monitoring and coordinately controlling each converter and the charging pile 105;
the engineer station and the real-time server are used for realizing the collection, analysis, statistics and storage of data in the optical storage charging and replacing station, report making, kinetic energy monitoring and operation and maintenance management;
the bidirectional converter is used for:
during grid-connected operation, the photovoltaic power generation power at the direct current side, the output power of the energy storage battery and the output power of the battery replacing pack 103 are merged into a power grid through the alternating current bus and the controllable switch 106 by adopting P/Q control, and the power grid and cloud energy storage users obtain the power transmitted by the charging station;
when the off-grid operation is carried out, the voltage and frequency stability of an alternating current bus and a direct current bus is supported by adopting V/f control, and the power balance between an alternating current side and a direct current side is realized;
according to the state of the controllable switch 106, the operation is switched between P/Q control and V/f control, so that the stable switching between grid-connected mode and off-grid mode is realized, and the stable operation of the system before and after switching is ensured.
The optical storage charging and swapping station is divided into a dc side charging and swapping system, an ac side charging and swapping system, and an energy management system 104, as shown by a dashed line frame in fig. 2.
The alternating current side charging and replacing system comprises a photovoltaic array 111, a photovoltaic inverter 211, an energy storage device 121, an energy storage converter 221, a replacing battery pack 131, an AC/DC converter 231, an alternating current charging pile 151 and a public connection Point (PCC) provided with a controllable switch 106 on the alternating current side, a power grid is connected with a cloud energy storage user and the PCC respectively, and an electric vehicle receives electric energy on an alternating current bus through the alternating current charging pile 151; the alternating current side charging and exchanging system adopts a single bus structure, and the voltage level is AC 400V.
In the alternating current side charging and converting system, a supply side is mainly realized by photovoltaic power generation, electric energy stored by an energy storage device and a power grid, a photovoltaic array 111 is connected to an alternating current bus through a photovoltaic inverter 211, an energy storage device 121 is connected to the alternating current bus through an energy storage converter 221, and the power grid is connected with the alternating current bus through PCC. The demand side mainly comprises an electric automobile, an alternating-current charging pile 151, a battery replacing group 131 and a cloud energy storage user. The electric vehicle is connected to an AC bus through the AC charging post 151, and the battery replacement pack 131 is connected to the AC bus through the AC/DC converter 231.
The direct-current side charging and replacing system comprises a photovoltaic array 112 on the direct-current side, an energy storage device 122, a DC/DC converter 222, a replacing battery pack 132, a DC/DC converter 232 and a direct-current charging pile 152; the electric automobile receives electric energy on the direct current bus through the direct current charging pile 152; the direct current side charging and converting system adopts a single bus structure, and the voltage level is DC 750V.
In the DC-side charging and switching system, the supply side is mainly realized by the photovoltaic power generation and the electric energy stored in the energy storage device, the photovoltaic array 112 is connected to the DC bus through the DC/DC converter 212, and the energy storage device 122 is connected to the DC bus through the DC/DC converter 222. The demand side mainly comprises an electric automobile, a direct current charging pile 152 and a battery replacing group 132. The electric vehicle is connected with the DC bus through the DC charging pile 152, and the battery replacing pack 132 is connected to the DC bus through the DC/DC converter 232.
The cloud energy storage user is directly connected with the power grid, the charging station serves as a cloud energy storage provider, the cloud energy storage user obtains partial energy storage service of the charging station in a service purchasing mode, and mutual connection of energy is achieved through the power grid. Specifically, a cloud energy storage user can purchase the cloud energy storage service use rights of the energy storage devices (121 and 122) and the battery replacing groups (131 and 132) with certain power capacity and energy capacity in a certain period, and the cloud energy storage user charges and discharges the cloud end battery, namely the energy storage device and the battery replacing group of the charging station, according to the self requirement. The energy storage device and the battery pack replacing resources are shared by the charging stations, so that the resource utilization efficiency is improved, the comprehensive cost is reduced, and the energy storage use requirements of more cloud energy storage users can be further met on the basis.
The energy management system 104 includes a bidirectional converter, a microgrid controller, a real-time server, an engineer station, and several communication lines.
The bidirectional converter connects the alternating current side charging and battery replacing system with the direct current side charging and battery replacing system and plays a role in power exchange at two sides. The communication line connects the whole AC/DC hybrid optical storage battery charging and replacing station to the microgrid controller, and the microgrid controller, the real-time server and the engineer station are connected together to realize the stable operation control of the whole battery charging and replacing system.
The controllable switch 106 arranged at the PCC is in the alternating current side charging and battery replacing system, and the whole system can be switched between the grid-connected mode and the off-grid mode through controlling the controllable switch 106. When the optical storage battery charging and converting station is in grid-connected operation, the bidirectional converter adopts a P/Q control mode, the bidirectional converter controls the exchange of power and energy between the alternating current side battery charging and converting system and the direct current side battery charging and converting system, the power grid bears the stability of voltage and frequency in the alternating current side battery charging and converting system through PCC, and under the action of the microgrid controller, the energy storage device stabilizes the power fluctuation of photovoltaic module power generation and the fluctuation of charging load of the electric automobile in the direct current side battery charging and converting system and maintains the stability of voltage and frequency in the direct current side battery charging and converting system. When the optical storage charging and replacing power station system runs off-grid, the bidirectional converter adopts a V/f control mode, and the stability of the system bus voltage and frequency is supported through the energy storage device and the replacing battery pack.
The rest of the principle is the same as the above embodiments, and is not described in detail here.
Another embodiment of the present invention further provides a cloud energy storage system optimization control method for an optical storage charging and transforming station, where as shown in fig. 1, the optical storage charging and transforming station includes: the system comprises a photovoltaic array 101 and a converter thereof, an energy storage device 102 and a converter thereof, a battery replacing group 103 and a converter thereof, an energy management system 104, a charging pile 105 and a controllable switch 106; the surplus of the battery replacing group and the surplus of the energy storage device are idle battery resources; the photovoltaic array 101, the energy storage device 102 and the battery replacing group 103 are respectively connected with the bus through respective converters; the charging pile 105 is connected with the bus and used for providing charging and exchanging electric quantity for the electric automobile; the input and output ports of the controllable switch 106 are respectively connected with the power grid and the bus; the energy management system 104 is respectively connected with the control end of the controllable switch 106 and each converter; of course, the optical storage charging and replacing power station may also be as shown in fig. 2, which is not described herein again;
as shown in fig. 5, the cloud energy storage system optimization control method of the optical storage charging and converting station includes:
s101, fitting a quadratic regression relation model of the surplus of a battery replacing group, photovoltaic power generation and electric vehicle charging and replacing quantity through an optimization algorithm by an energy management system according to historical collected data of a light storage charging and replacing station;
s102, the energy management system obtains real-time battery capacity for providing cloud energy storage service by combining real-time data acquisition of the optical storage charging and converting station according to a secondary regression relation model;
s103, when the energy management system receives the capacity requirement of the cloud energy storage user, an optimization decision is made according to the real-time battery capacity, and quick response is carried out.
Preferably, step S101 further includes:
the energy management system fits a quadratic regression relation model of the allowance of the energy storage device, the photovoltaic power generation amount and the charging and replacing electric quantity of the electric automobile through an optimization algorithm according to historical collected data of the light storage charging and replacing power station.
More preferably, in this case, step S101 includes:
the energy management system performs multi-parameter working condition optimization design by using a response surface method according to historical collected data of the light storage charging and replacing station, with photovoltaic power generation and electric vehicle charging and replacing quantity as two independent variables, and with replacing battery pack allowance and energy storage device allowance as two objective functions;
the energy management system utilizes Design Expert software to carry out data analysis, and determines a quadratic regression relation model and a corresponding response surface graph of the surplus of the battery pack, the photovoltaic power generation amount and the electric vehicle charging and changing electric quantity, and a quadratic regression relation model and a corresponding response surface graph of the surplus of the energy storage device, the photovoltaic power generation amount and the electric vehicle charging and changing electric quantity.
For a specific implementation process of the cloud energy storage system optimization control method of the optical storage charging and converting station, reference may be made to fig. 6, a specific principle of which is the same as that in the above embodiment, and details are not repeated here.
The embodiments of the invention are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments can be referred to each other.
The foregoing is merely a preferred embodiment of the invention and is not intended to limit the invention in any manner. Although the present invention has been described with reference to the preferred embodiments, it is not intended to be limited thereto. Those skilled in the art can make numerous possible variations and modifications to the present teachings, or modify equivalent embodiments to equivalent variations, without departing from the scope of the present teachings, using the methods and techniques disclosed above. Therefore, any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present invention are still within the scope of the protection of the technical solution of the present invention, unless the contents of the technical solution of the present invention are departed.
Claims (9)
1. An optical storage charging and replacing station, comprising: the system comprises a photovoltaic array and a converter thereof, an energy storage device and a converter thereof, a battery replacing group and a converter thereof, an energy management system, a charging pile and a controllable switch; wherein:
the surplus of the battery replacing group and the surplus of the energy storage device are idle battery resources;
the photovoltaic array, the energy storage device and the battery replacing group are respectively connected with a bus through respective converters;
the charging pile is connected with the bus and used for providing charging and exchanging electric quantity for the electric automobile;
the input and output ports of the controllable switch are respectively connected with the power grid and the bus;
the energy management system is respectively connected with the control end of the controllable switch and each converter;
the energy management system is to: according to the historical collected data of the optical storage charging and replacing station, carrying out multi-parameter working condition optimization Design by a response surface method, then optimizing by using Design Expert software, fitting a secondary regression relation model of the remaining amount of the battery replacing group, the photovoltaic power generation amount and the charging and replacing electric quantity of the electric automobile, and combining the real-time collected data of the optical storage charging and replacing station to obtain the real-time battery capacity for providing cloud energy storage service; when the capacity requirement of a cloud energy storage user is received, an optimization decision can be made according to the real-time battery capacity, and quick response is carried out.
2. The light storage, charging and swapping station of claim 1, wherein the energy management system is further configured to: according to the historical collected data of the optical storage charging and replacing power station, multi-parameter working condition optimization Design is carried out through a response surface method, then Design Expert software is used for optimization, and a quadratic regression relation model of the allowance of the energy storage device, the photovoltaic power generation amount and the charging and replacing power of the electric automobile is fitted.
3. The optical storage charging and replacing station as claimed in claim 2, wherein the process of the energy management system fitting to obtain the quadratic regression relationship model of the battery pack replacement residual quantity and the photovoltaic power generation quantity and the electric vehicle charging and replacing quantity and the quadratic regression relationship model of the energy storage device residual quantity and the photovoltaic power generation quantity and the electric vehicle charging and replacing quantity is as follows:
according to historical collected data of the light storage charging and replacing station, the photovoltaic power generation capacity and the charging and replacing capacity of the electric automobile are used as two independent variables, the battery replacing battery pack allowance and the energy storage device allowance are used as two objective functions, and a response surface method is used for carrying out multi-parameter working condition optimization design;
and performing data analysis by using Design Expert software to determine a quadratic regression relationship model and a corresponding response surface map of the battery pack replacement allowance, the photovoltaic power generation amount and the electric vehicle charging and replacing quantity, and a quadratic regression relationship model and a corresponding response surface map of the energy storage device allowance, the photovoltaic power generation amount and the electric vehicle charging and replacing quantity.
4. A light storing, charging and replacing power station according to any one of claims 1 to 3, wherein the bus bar comprises: a DC bus and an AC bus;
the converter of the photovoltaic array comprises: the photovoltaic inverter is connected with the alternating current bus, and the DC/DC converter is connected with the direct current bus;
the converter of the energy storage device includes: the energy storage converter is connected with the alternating current bus, and the DC/DC converter is connected with the direct current bus;
the converter for replacing the battery pack comprises: the AC/DC converter is connected with the alternating current bus, and the DC/DC converter is connected with the direct current bus;
fill electric pile includes: the alternating current charging pile is connected with the alternating current bus, and the direct current charging pile is connected with the direct current bus.
5. The light storage charging and swapping station of claim 4, wherein the energy management system comprises: the system comprises a microgrid controller, an engineer station and a real-time server; wherein:
the microgrid controller is used for completing off-grid detection and automatic grid connection functions, realizing stable operation under different working conditions in the system, and monitoring and coordinately controlling each converter and the charging pile;
the engineer station and the real-time server are used for realizing the collection, analysis, statistics and storage of data in the optical storage charging and replacing station, report making, kinetic energy monitoring and operation and maintenance management.
6. The light storage charging and swapping station of claim 5, wherein the energy management system further comprises: the bidirectional converter is respectively connected with the alternating current bus, the direct current bus and the energy management system; the bidirectional converter is used for:
when the grid-connected operation is carried out, the P/Q control is adopted, and the photovoltaic power generation power at the direct current side, the output power of the energy storage battery and the output power of the battery replacement pack are merged into a power grid through the alternating current bus and the controllable switch;
when the off-grid operation is carried out, the voltage and frequency stability of the alternating current bus and the direct current bus is supported by adopting V/f control, and the power balance between the alternating current side and the direct current side is realized;
and switching operation between P/Q control and V/f control according to the state of the controllable switch, so that stable switching between grid-connected and off-grid modes is realized.
7. The cloud energy storage system optimization control method of the optical storage charging and power exchanging station is characterized in that the optical storage charging and power exchanging station comprises the following steps: the system comprises a photovoltaic array and a converter thereof, an energy storage device and a converter thereof, a battery replacing group and a converter thereof, an energy management system, a charging pile and a controllable switch; the battery replacement allowance and the energy storage device allowance are idle battery resources; the photovoltaic array, the energy storage device and the battery replacing group are respectively connected with a bus through respective converters; the charging pile is connected with the bus and used for providing charging and exchanging electric quantity for the electric automobile; the input and output ports of the controllable switch are respectively connected with the power grid and the bus; the energy management system is respectively connected with the control end of the controllable switch and each converter; the cloud energy storage system optimization control method of the optical storage charging and replacing power station comprises the following steps:
the energy management system carries out multi-parameter working condition optimization Design through a response surface method according to historical collected data of the optical storage charging and replacing station, then carries out optimization through Design Expert software, and fits a quadratic regression relation model of the battery replacing pack allowance, photovoltaic power generation amount and electric automobile charging and replacing quantity;
the energy management system obtains real-time battery capacity for providing cloud energy storage service by combining real-time acquisition data of the optical storage charging and replacing power station according to the secondary regression relation model;
and when the energy management system receives the capacity requirement of the cloud energy storage user, an optimization decision is made according to the real-time battery capacity, and quick response is carried out.
8. The cloud energy storage system optimization control method of claim 7, wherein before the energy management system obtains a real-time battery capacity providing a cloud energy storage service according to the quadratic regression relationship model in combination with real-time acquired data of the optical storage charging and replacing power station, the method further comprises:
the energy management system carries out multi-parameter working condition optimization Design through a response surface method according to historical collected data of the optical storage charging and replacing power station, then carries out optimization through Design Expert software, and fits a quadratic regression relation model of the allowance of the energy storage device, the photovoltaic power generation amount and the charging and replacing power of the electric automobile.
9. The cloud energy storage system optimization control method of claim 8, wherein the energy management system performs multi-parameter working condition optimization Design by a response surface method according to historical collected data of the optical storage charging and replacing power station, and then performs optimization by means of Design Expert software to fit a quadratic regression relation model of the battery pack residue and the photovoltaic power generation amount and the electric vehicle charging and replacing power amount and a quadratic regression relation model of the energy storage device residue and the photovoltaic power generation amount and the electric vehicle charging and replacing power amount, and the method comprises the following steps:
the energy management system performs multi-parameter working condition optimization design by using a response surface method according to historical collected data of the light storage charging and replacing power station, the photovoltaic power generation amount and the charging and replacing power amount of the electric automobile as two independent variables, and the battery replacing battery pack allowance and the energy storage device allowance as two objective functions;
the energy management system utilizes Design Expert software to perform data analysis, and determines a quadratic regression relation model and a corresponding response surface graph of the battery pack replacement allowance, the photovoltaic power generation amount and the electric vehicle charging and replacing electric quantity, and a quadratic regression relation model and a corresponding response surface graph of the energy storage device allowance, the photovoltaic power generation amount and the electric vehicle charging and replacing electric quantity.
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| CN109412249A (en) * | 2018-10-15 | 2019-03-01 | 深圳市安和威电力科技股份有限公司 | A kind of light power storage system and control method based on common DC bus |
| CN111474449B (en) * | 2020-03-31 | 2022-10-21 | 福建时代星云科技有限公司 | Insulation detection method and system of optical storage and charge detection system |
| CN112737037B (en) * | 2020-12-29 | 2022-10-21 | 吴晓刚 | Portable multi-functional light stores up charging device |
| CN113098060B (en) * | 2021-04-08 | 2022-10-04 | 河北工业大学 | Intelligent controllable power switch for new energy grid connection |
| CN113422419B (en) * | 2021-08-24 | 2022-02-08 | 中国华能集团清洁能源技术研究院有限公司 | Battery changing station |
| CN114633663A (en) * | 2022-05-20 | 2022-06-17 | 深圳市奥盛新能源有限公司 | Integrated power battery charging management system based on cloud data management |
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