CN112671022A - Optical storage charging station capacity optimal configuration method, system, terminal and storage medium - Google Patents
Optical storage charging station capacity optimal configuration method, system, terminal and storage medium Download PDFInfo
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Abstract
The invention provides a capacity optimal configuration method, a system, a terminal and a storage medium for an optical storage charging station, wherein the capacity optimal configuration method comprises the following steps: generating a user charging demand by utilizing a Markov chain Monte Carlo simulation method; an operation strategy of the optical storage charging station is made according to the charging requirement of the user, wherein the operation strategy comprises electricity purchasing information, charging information, electricity selling information and carbon emission power; establishing an operation cost function of the optical storage charging station with capacity configuration as a variable; establishing a benefit target function of the optical storage charging station according to the operation cost function, the electricity purchasing information, the charging information, the electricity selling information and the carbon emission power; and calculating the optimal capacity allocation when the benefit objective function takes the maximum value by utilizing a genetic algorithm. The invention can automatically generate the optimal capacity configuration of the optical storage charging station, thereby greatly improving the income of the charging station and reducing the carbon emission.
Description
Technical Field
The invention relates to the technical field of optical storage charging stations, in particular to a capacity optimal configuration method, a system, a terminal and a storage medium for an optical storage charging station.
Background
Although Electric Vehicles (EVs) can realize energy conservation and emission reduction of cities, primary energy at the power generation side of a power system in China still mainly comprises coal (accounting for about 75-80%), and the EVs are connected to a power grid through charging infrastructure for charging and do not realize real energy conservation and emission reduction. The efficient utilization of distributed renewable energy is an important scheme for reducing the fossil rate of primary energy. If a photovoltaic power generation system with a certain scale can be arranged according to local conditions and is combined with an EV Charging Station to establish a photovoltaic Storage-Charging Station (PSCS), the proportion of clean energy in EV primary energy can be effectively increased, the indirect carbon emission of the EV is reduced, and the local consumption of renewable energy is promoted. For PSCS operators, the major concern is the economics of PSCS. If the photovoltaic capacity configuration is too large, the photovoltaic energy cannot be fully utilized by the EV, so that more photovoltaic energy is sent into a power grid to earn a small amount of internet surfing cost; if the photovoltaic capacity is configured too small, the photovoltaic output cannot meet the charging requirement of the EV, the PSCS needs to buy more power from the power grid, the economy of the PSCS is affected, and the indirect carbon emission of the EV cannot be effectively reduced. The utilization rate of the EV to the photovoltaic energy can be increased through the energy storage system, but the current energy storage cost is high, and the investment cost of the energy storage system and the profit of price arbitrage through the energy storage system need to be comprehensively considered. Therefore, how to reasonably configure the PSCS optical storage capacity has important research significance.
The PSCS architecture mainly comprises a photovoltaic power generation system, an energy storage unit, a public power network, a charging load and an energy management system. The energy management system may collect, view data from power generation, energy storage, and loads and control the flow of energy throughout the PSCS. In this PSCS system, three main entities are mainly involved, namely, the grid operator, the PSCS, and the EV users. The power grid operator is a power supplier and an electricity purchasing supplier of the PSCS, provides electric energy for the PSCS and provides purchasing service for redundant photovoltaic power generation of the PSCS. The PSCS operator is a provider of charging service and builds a photovoltaic power generation and energy storage system of the PSCS operator, the economy is mainly concerned, and in order to enable EV users to use the photovoltaic energy of the EV users more, the PSCS sets up charging subsidy prices of the PSCS operators on the basis of electricity purchasing cost of a power grid. The EV user is the final consumer of the charging service, and can change the charging demand of the EV user in response to the subsidy price made by the PSCS operator.
At present, in the aspect of PSCS capacity configuration research, a plurality of scholars at home and abroad are carried out, and certain achievements are obtained. The existing capacity allocation method can better improve the economy of PSCS and reduce the indirect carbon emission level of EV to a certain extent. However, the existing research does not fully consider the influence of the PSCS operation strategy on the capacity planning, and if part of EV users can be guided to change their charging periods by coordinating the charging technology, so that the EV directly consumes more photovoltaic output, a certain influence will be caused on the capacity configuration planning of the PSCS. In the aspect of an operation strategy, the existing strategy can better realize the direct consumption of new energy by the EV, but two means of EV demand response and energy storage are not deeply combined to fully consume the photovoltaic energy.
In summary, further research is needed to optimize the photovoltaic and energy storage capacity of the PSCS in consideration of the PSCS operation strategy combining EV demand response and energy storage, so as to minimize the economic minimum carbon economy of the PSCS and the indirect carbon emission of the EV.
Disclosure of Invention
In view of the above-mentioned deficiencies in the prior art, the present invention provides a method, a system, a terminal and a storage medium for optimizing and configuring capacity of an optical storage and charging station, so as to solve the above-mentioned technical problems.
In a first aspect, the present invention provides a capacity optimization configuration method for an optical storage charging station, including:
generating a user charging demand by utilizing a Markov chain Monte Carlo simulation method;
an operation strategy of the optical storage charging station is made according to the charging requirement of the user, wherein the operation strategy comprises electricity purchasing information, charging information, electricity selling information and carbon emission power;
establishing an operation cost function of the optical storage charging station with capacity configuration as a variable;
establishing a benefit target function of the optical storage charging station according to the operation cost function, the electricity purchasing information, the charging information, the electricity selling information and the carbon emission power;
and calculating the optimal capacity allocation when the benefit objective function takes the maximum value by utilizing a genetic algorithm.
Further, the method for customizing the operation strategy of the optical storage charging station according to the charging requirement of the user comprises the following steps:
defining the difference value between the generating power of the photovoltaic system and the charging load demand of the charging station as the net power of the optical storage power station;
formulating an incentive subsidy price according to the net power value;
if the net power is larger than 0, the surplus electricity can be selected to be on line or sent to an energy storage system;
and if the net power is less than 0, controlling the energy storage system to charge the photovoltaic system, and if the capacity of the energy storage system is insufficient, purchasing electricity from the power grid.
Further, the establishing the optical storage charging station as an operation cost function with the capacity configured as a variable includes:
constructing a photovoltaic system investment year conversion cost function by utilizing the photovoltaic installed capacity, the photovoltaic capacity manufacturing cost, the photovoltaic conversion rate and the photovoltaic service life;
constructing an energy storage system investment year reduced cost by utilizing the energy storage installed capacity, the energy storage capacity manufacturing cost, the energy storage conversion rate and the energy storage service life;
the operation cost function is the sum of the photovoltaic system investment year reduced cost function and the energy storage system investment year reduced cost.
Further, the establishing a benefit objective function of the optical storage charging station according to the operation cost function, the electricity purchasing information, the charging information, the electricity selling information and the carbon emission power includes:
the electricity purchasing information comprises the price of the electricity purchasing period and the power of the electricity purchasing period;
the charging information comprises a charging period price and a charging period power;
the electricity selling information comprises price of electricity selling time period and power of electricity selling time period;
the benefit objective function is the product of the price of the electricity purchasing period and the power of the electricity purchasing period, the product of the price of the electricity charging period and the power of the electricity charging period, the product of the price of the electricity selling period and the power of the electricity selling period, the product of the carbon emission power and the carbon emission coefficient and the carbon emission weight, and the accumulated value of the operation cost function.
Further, the method further comprises:
taking the sum of the photovoltaic installed capacity, the stored energy charging and discharging power, the electricity purchasing power and the charging power of the light storage charging station as 0 as a first constraint condition of the benefit objective function;
constraining an energy storage system state of charge of the optical storage charging station as a second constraint condition for the benefit objective function;
a third constraint condition that the working state of the energy storage system of the optical storage charging station can only be one of a charging state or a discharging state is taken as the benefit objective function;
taking the maximum charge/discharge capacity of the energy storage system of the optical storage charging station as a fourth constraint condition of the benefit objective function;
a fifth constraint condition that takes a maximum charging/discharging power of an energy storage system of the optical storage charging station as the benefit objective function.
Further, the calculating, by using a genetic algorithm, an optimal capacity configuration when the benefit objective function takes a maximum value includes:
substituting the first constraint, the first constraint and the fifth constraint into the benefit objective function;
determining a boundary value of the photovoltaic installed capacity and a boundary value of the energy storage installed capacity;
randomly generating a photovoltaic initialization population and an energy storage initialization population by using a binary coding mode;
carrying out chromosome decoding, determining the process photovoltaic installed capacity and the process energy storage installed capacity, and calculating a current benefit objective function value according to the process photovoltaic installed capacity and the process energy storage installed capacity;
calculating the individual adaptation degree, and performing selection, crossing and mutation operations until the maximum iteration number is reached;
and outputting the optimal photovoltaic installed capacity and the energy storage installed capacity.
In a second aspect, the present invention provides a capacity optimization configuration system for an optical storage and charging station, including:
a demand generation unit configured to generate a user charging demand using a markov chain monte carlo simulation method;
the strategy making unit is configured for making an operation strategy of the optical storage charging station according to the charging requirement of the user, wherein the operation strategy comprises electricity purchasing information, charging information, electricity selling information and carbon emission power;
the cost modeling unit is configured to establish an operation cost function of the optical storage charging station with capacity configuration as a variable;
the function construction unit is configured to establish a benefit objective function of the optical storage charging station according to the operation cost function, the electricity purchasing information, the charging information, the electricity selling information and the carbon emission power;
and the configuration calculation unit is used for calculating the optimal capacity configuration when the benefit objective function takes the maximum value by utilizing a genetic algorithm.
Further, the cost modeling unit includes:
the photovoltaic calculation module is configured for constructing a photovoltaic system investment year conversion cost function by utilizing the photovoltaic installed capacity, the photovoltaic capacity manufacturing cost, the photovoltaic conversion rate and the photovoltaic service life;
the energy storage calculation module is configured for constructing an energy storage system investment year reduced cost by utilizing the energy storage installed capacity, the energy storage capacity manufacturing cost, the energy storage conversion rate and the energy storage service life;
and the cost calculation module is configured to enable the operation cost function to be the sum of the photovoltaic system investment year conversion cost function and the energy storage system investment year conversion cost.
In a third aspect, a terminal is provided, including:
a processor, a memory, wherein,
the memory is used for storing a computer program which,
the processor is used for calling and running the computer program from the memory so as to make the terminal execute the method of the terminal.
In a fourth aspect, a computer storage medium is provided having stored therein instructions that, when executed on a computer, cause the computer to perform the method of the above aspects.
The beneficial effect of the invention is that,
the capacity optimal configuration method, the system, the terminal and the storage medium of the optical storage charging station can automatically generate the optimal capacity configuration of the optical storage charging station, thereby greatly improving the income of the charging station and reducing the carbon emission.
In addition, the invention has reliable design principle, simple structure and very wide application prospect.
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In order to more clearly illustrate the embodiments or technical solutions in the prior art of the present invention, the drawings used in the description of the embodiments or prior art will be briefly described below, and it is obvious for those skilled in the art that other drawings can be obtained based on these drawings without creative efforts.
FIG. 1 is a schematic flow diagram of a method of one embodiment of the invention.
FIG. 2 is a schematic flow diagram of an operating policy generation process of a method of one embodiment of the invention.
FIG. 3 is a schematic block diagram of a system of one embodiment of the present invention.
Fig. 4 is a schematic structural diagram of a terminal according to an embodiment of the present invention.
Detailed Description
In order to make those skilled in the art better understand the technical solution of the present invention, the technical solution in the embodiment of the present invention will be clearly and completely described below with reference to the drawings in the embodiment of the present invention, and it is obvious that the described embodiment is only a part of the embodiment of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The following explains key terms appearing in the present invention.
FIG. 1 is a schematic flow diagram of a method of one embodiment of the invention. The implementation body in fig. 1 may be a capacity optimization configuration system for an optical storage and charging station.
As shown in fig. 1, the method includes:
and 150, calculating the optimal capacity allocation when the benefit objective function takes the maximum value by using a genetic algorithm.
Specifically, the capacity optimization configuration method for the optical storage charging station comprises the following steps:
and S1, generating the user charging requirement by utilizing a Markov chain Monte Carlo simulation method.
Application MarkThe KoffleMonte Carlo simulation method generates the arrival time, the parking duration and the EV state of charge requirements at the departure of the EV in the parking lot PSCS. Charging of the EV is started upon arrival at the PSCS, without considering the demand response of the EV charging load, until the electric charge is full. Charging data structure CX of EV user iiAs shown in formula (1).
In the formula, Ai，Li，
Ei，PiThe arrival time, the departure time, the current state of charge, the state of charge required at the departure time, the total capacity of the EV battery and the charging power of the EV user i are respectively represented.
The charging demand power P of the PSCS at time jL(j) Is composed of
According to the formula, N is the total number of EV users in a day;
indicating the charging state of the EV user i at the time j, the charging state is 1, otherwise, the charging state is 0.
If the PSCS can guide the charging behavior of the EV according to the photovoltaic output characteristics, the EV charging demand and the photovoltaic output can generate an obvious synergistic effect. The price type incentive method is an effective means for guiding the charging behavior, and can induce EV users to select time periods with higher incentive prices for charging instead of immediate charging; if the incentive price is high enough, a portion of EV users may also be enticed to alter the arrival or departure times of their original travel plans. Under the same incentive price, the responsiveness of different users is different, and in general, the closer the arrival time or departure time of the user is to the demand response time j (response time length), the higher the responsiveness is. The response function of the user to the electricity price is established by adopting a fuzzy theory method, and the user responsiveness function can be expressed as
R(i,j)＝M[pa(j)]*[1-M(Ti j)] (3)
In the formula, M [ rho a (j)]The responsiveness of the user to the subsidy price is represented, rho a (j) represents the excitation subsidy price at the moment j, and m represents the upper limit of the excitation price at the moment j; m (T)i j) And the willingness of the user i to adjust the travel plan to respond to the time j is shown, and the m indicates that the user i does not respond after the response distance of the requirement of the user i at the moment j is beyond the m in the preset stay period.
S2, please refer to fig. 2, the user' S charging requirement makes the operation strategy of the optical storage charging station, wherein the operation strategy includes electricity purchasing information, charging information, electricity selling information and carbon emission power.
In order to gain greater benefit when the grid electricity rate is a fixed rate, the PSCS expects its photovoltaic output at each moment to be fully absorbed by the charging load. When the photovoltaic output is greater than the charging demand, the PSCS has two options, one is to send redundant photovoltaic energy into the power grid to obtain the electricity selling benefit, and the other is to store the redundant photovoltaic energy through the energy storage system when the energy storage system is not full, so that the benefit can be obtained through the output of the energy storage system when the photovoltaic output is less than the charging demand. In fact, in addition to the above two ways, the user may be guided to change their charging period by means of price subsidy, so that the EV load directly consumes more photovoltaic energy.
And defining the difference value between the generated power of the photovoltaic system and the charging load demand of the charging station as the net power of the optical storage power station, and recording the difference value as delta P.
ΔP(j)＝PPV(j)-Pload(j) (5)
In the formula, PPV(j)，Pload(j) The requirements of the photovoltaic power generation system on output and charging load in the period of j are respectively met.
The larger Δ P (j), the higher ρ is requireda(j) To attract more users to change their original charging schedule. Rhoa(j) ρ at which the equation is satisfied is determined to be minimum by equation (5)a(j) I.e. the incentive subsidy price at time j.
ρa(j) Is of upper limit, in Δ P (j)>At 0, the PSCS can choose to send the surplus electric quantity back to the power grid or store the surplus electric quantity through energy storage to sell the surplus electric quantity in the peak electricity price period, so that rho is obtained through the formula (5)a(j) If it is greater than the upper limit value
Then
In the formula, ρIRepresenting the power price of the power grid; rhoO(j) Representing the photovoltaic grid-connected electricity price at the moment j;
and the average loss cost of energy storage of photovoltaic residual electricity stored in the energy storage at the moment j is shown.
Establishing rhoa(j) Then, the EV charging requirement under the demand response can be determined according to the formula (3), at this time, if the photovoltaic output is greater than the charging requirement, surplus power can be selected to be on line or be sent to the stored energy, if the photovoltaic output is less than the charging requirement, the stored energy output is provided, if the stored energy capacity is insufficient, the power grid purchases power, and the specific operation flow is shown in fig. 2. Therein, SOCminRepresenting the lower limit of the safe operation capacity of the energy storage system; SOCmaxAnd representing the upper limit of the safe operation capacity of the energy storage system.
And S3, establishing the optical storage charging station as an operation cost function with the capacity configuration as a variable. And establishing a benefit target function of the optical storage charging station according to the operation cost function, the electricity purchasing information, the charging information, the electricity selling information and the carbon emission power.
The objective function is shown in the formula by considering the electricity purchasing cost of the charging station to the power grid, the electricity selling income to the power grid, the charging service income providing, the photovoltaic power generation cost, the energy storage loss cost and the carbon emission conversion cost.
In the formula, C1Representing the electricity purchase cost of the PSCS to the power grid; c2Representing the benefit of the PSCS in providing charging service to EV users; c3The investment cost of the photovoltaic and energy storage equipment and the cost loss cost of the energy storage equipment during operation are represented, and the cost is reduced to one year through an equal-year value method; c4Representing the electricity selling cost of the PSCS to the power grid; c5Represents the amount of carbon emission;
a weight coefficient indicating the amount of carbon emission.
When in use
When the value is zero, namely the carbon emission is not considered, the optimization target is reduced to only consider the economy of the PSCS;
the larger the value is, the more attention is paid to the carbon emission, and the stricter the control on the carbon emission is; when in use
When the value is large enough, the economic efficiency of the PSCS is not considered at the moment, and only the low-carbon operation of the PSCS is considered. In the context of the carbon trading market,
can be regarded as the carbon emission price, and the carbon emission has the priceAnd (4) combining the two targets (economy and carbon emission) with the same property, and uniformly considering the economy.
For C1The method comprises the following steps:
in the formula, ρI(j) The representation represents the electricity purchase price at the time j; pG_I(j) Representing the power delivered by the grid to the PSCS at moment j; Δ t represents a time interval, set herein to 1h (the same below).
For C2The method comprises the following steps:
in the formula, ρS(j) Represents the EV charge fee at time j; pL(j) Representing the PSCS charging load power at time j.
For C3The method comprises the following steps:
C3＝CPV+CB (11)
in the formula, CPVRepresenting the conversion cost of the photovoltaic system in the next year; cBRepresents the annual investment conversion cost P of the energy storage systemPV_mRepresenting installed capacity of the photovoltaic; ePVRepresenting the unit capacity photovoltaic cost; r ispRepresenting a photovoltaic equipment discount rate; z is a radical ofpRepresents the service life of the photovoltaic device; pB_mRepresenting the installed capacity of the energy storage; eBExpressing the unit capacity energy storage cost; r isBRepresenting the discount rate of the sports equipment; z is a radical ofBIndicating the useful life of the energy storage device.
For C4The method comprises the following steps:
in the formula, ρO(j) The price of the electricity sold to the power grid at the moment j is represented; pG_O(j) Indicating that PSCS delivers power to the grid at time j.
For C5The method comprises the following steps:
in the formula (I), the compound is shown in the specification,
the carbon emission coefficient is expressed herein as a national average of 1.03 kg/(kW. h).
Setting constraints of an objective function
(1) Power balance constraint
PPV(j)+PG(j)-PB(j)-PL(j)＝0 (16)
In the formula, PB(j) Representing the charging power (discharging is negative power) of the energy storage system at the moment j; pG(j) Indicating that the grid is delivering power to the PSCS at time j.
(2) Energy storage system state of charge constraint
SOCmin<SOC(j)<SOCmax (17)
In the formula, SOCmax，SOCminThe maximum and minimum charge rates of the energy storage system are respectively, so that the energy storage system is prevented from being overcharged and overdischarged.
(3) Energy storage system operating state constraints
ε0+ε1＝1 (18)
In the formula, epsilon0Indicating that the energy storage system is in a charging state; epsilon1Indicating that the energy storage system is in a discharge state; and epsilon0,ε1∈{0,1}。
(4) Energy storage system charge capacity constraints
EB(j+Δt)＝EB(j)+PB_I(j)ΔtηI (19)
In the formula, EB(j) Representing the capacity of the energy storage system at the moment j; pB_I(j) Representing the charging power of the energy storage system at the moment j; etaIIndicating the energy storage system charging efficiency.
(5) Energy storage system discharge capacity constraints
EB(t+Δt)＝EB(t)+PB_O(j)/ηO (20)
In the formula, PB_O(j) Representing the discharge power of the energy storage system at the moment j; etaOIndicating the energy storage system discharge efficiency.
(6) Energy storage system charge and discharge power constraints
The maximum charging and discharging power of the energy storage system must be limited to prevent the battery performance from being affected by too fast charging and discharging.
-PB_max<PB(j)<PB_max (21)
In the formula, PB_maxAnd the maximum charge and discharge power of the energy storage system is represented.
And S4, calculating the optimal capacity allocation when the benefit objective function takes the maximum value by using a genetic algorithm.
Solving the model based on a genetic algorithm, wherein the optimization decision variable is photovoltaic and energy storage capacity configuration, and for the convenience of solving, the photovoltaic and energy storage capacities are assumed to be integers. The specific solving steps are as follows.
(1) And (6) initializing an algorithm. And determining system parameters, and determining the upper limit and the lower limit of two decision variables of photovoltaic capacity and energy storage capacity.
(2) And randomly generating an initialization population by adopting a binary coding mode.
(3) And (3) decoding the chromosome, determining the energy storage and photovoltaic configuration capacity, determining the charging demand under demand response based on a Monte Carlo simulation method, and calculating the objective function value of the formula (8) according to the PSCS operation strategy.
(4) And (4) calculating the individual adaptation degree, and performing selection, crossing and mutation operations.
(5) And (4) if the maximum iteration times are not reached, returning to the step (3), otherwise, ending the iteration, and outputting an optimal capacity configuration result.
The larger the light storage system capacity configuration is not the better. In the system, when the energy storage capacity is unchanged, the annual operating cost of the PSCS is reduced and then increased along with the increase of the photovoltaic configuration capacity, because the photovoltaic system can reduce the electricity purchasing quantity of the charging station from the power grid, but the photovoltaic consumption of the charging load and the energy storage system is limited, and when the limit is exceeded, the electricity generated by the photovoltaic system can only be sent into the power grid and cannot be sold to EV users.
As shown in fig. 3, the system 300 includes:
a requirement generation unit 310 configured to generate a user charging requirement using a markov chain monte carlo simulation method;
the strategy making unit 320 is configured to make an operation strategy of the optical storage charging station according to the charging requirement of the user, wherein the operation strategy comprises electricity purchasing information, charging information, electricity selling information and carbon emission power;
a cost modeling unit 330 configured to establish an operating cost function of the optical storage charging station configured as a variable in capacity;
the function construction unit 340 is configured to establish a benefit objective function of the optical storage charging station according to the operation cost function, the electricity purchasing information, the charging information, the electricity selling information and the carbon emission power;
a configuration calculating unit 350 configured to calculate an optimal capacity configuration when the benefit objective function takes a maximum value using a genetic algorithm.
Optionally, as an embodiment of the present invention, the cost modeling unit includes:
the photovoltaic calculation module is configured for constructing a photovoltaic system investment year conversion cost function by utilizing the photovoltaic installed capacity, the photovoltaic capacity manufacturing cost, the photovoltaic conversion rate and the photovoltaic service life;
the energy storage calculation module is configured for constructing an energy storage system investment year reduced cost by utilizing the energy storage installed capacity, the energy storage capacity manufacturing cost, the energy storage conversion rate and the energy storage service life;
and the cost calculation module is configured to enable the operation cost function to be the sum of the photovoltaic system investment year conversion cost function and the energy storage system investment year conversion cost.
Fig. 4 is a schematic structural diagram of a terminal 400 according to an embodiment of the present invention, where the terminal 400 may be used to execute the method for optimally configuring the capacity of the optical storage and charging station according to the embodiment of the present invention.
Among them, the terminal 400 may include: a processor 410, a memory 420, and a communication unit 430. The components communicate via one or more buses, and those skilled in the art will appreciate that the architecture of the servers shown in the figures is not intended to be limiting, and may be a bus architecture, a star architecture, a combination of more or less components than those shown, or a different arrangement of components.
The memory 420 may be used for storing instructions executed by the processor 410, and the memory 420 may be implemented by any type of volatile or non-volatile storage terminal or combination thereof, such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. The executable instructions in memory 420, when executed by processor 410, enable terminal 400 to perform some or all of the steps in the method embodiments described below.
The processor 410 is a control center of the storage terminal, connects various parts of the entire electronic terminal using various interfaces and lines, and performs various functions of the electronic terminal and/or processes data by operating or executing software programs and/or modules stored in the memory 420 and calling data stored in the memory. The processor may be composed of an Integrated Circuit (IC), for example, a single packaged IC, or a plurality of packaged ICs connected with the same or different functions. For example, the processor 410 may include only a Central Processing Unit (CPU). In the embodiment of the present invention, the CPU may be a single operation core, or may include multiple operation cores.
A communication unit 430, configured to establish a communication channel so that the storage terminal can communicate with other terminals. And receiving user data sent by other terminals or sending the user data to other terminals.
The present invention also provides a computer storage medium, wherein the computer storage medium may store a program, and the program may include some or all of the steps in the embodiments provided by the present invention when executed. The storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM) or a Random Access Memory (RAM).
Therefore, the optimal capacity configuration of the optical storage charging station can be automatically generated, so that the benefits of the charging station are greatly improved, and the carbon emission is reduced.
Those skilled in the art will readily appreciate that the techniques of the embodiments of the present invention may be implemented as software plus a required general purpose hardware platform. Based on such understanding, the technical solutions in the embodiments of the present invention may be embodied in the form of a software product, where the computer software product is stored in a storage medium, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and the like, and the storage medium can store program codes, and includes instructions for enabling a computer terminal (which may be a personal computer, a server, or a second terminal, a network terminal, and the like) to perform all or part of the steps of the method in the embodiments of the present invention.
The same and similar parts in the various embodiments in this specification may be referred to each other. Especially, for the terminal embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant points can be referred to the description in the method embodiment.
In the embodiments provided in the present invention, it should be understood that the disclosed system and method can be implemented in other ways. For example, the above-described system embodiments are merely illustrative, and for example, the division of the units is only one logical functional division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, systems or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
Although the present invention has been described in detail by referring to the drawings in connection with the preferred embodiments, the present invention is not limited thereto. Various equivalent modifications or substitutions can be made on the embodiments of the present invention by those skilled in the art without departing from the spirit and scope of the present invention, and these modifications or substitutions are within the scope of the present invention/any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. An optical storage charging station capacity optimization configuration method is characterized by comprising the following steps:
generating a user charging demand by utilizing a Markov chain Monte Carlo simulation method;
an operation strategy of the optical storage charging station is made according to the charging requirement of the user, wherein the operation strategy comprises electricity purchasing information, charging information, electricity selling information and carbon emission power;
establishing an operation cost function of the optical storage charging station with capacity configuration as a variable;
establishing a benefit target function of the optical storage charging station according to the operation cost function, the electricity purchasing information, the charging information, the electricity selling information and the carbon emission power;
and calculating the optimal capacity allocation when the benefit objective function takes the maximum value by utilizing a genetic algorithm.
2. The method of claim 1, wherein the customizing the optical storage charging station operating strategy according to the user charging demand comprises:
defining the difference value between the generating power of the photovoltaic system and the charging load demand of the charging station as the net power of the optical storage power station;
formulating an incentive subsidy price according to the net power value;
if the net power is larger than 0, the surplus electricity can be selected to be on line or sent to an energy storage system;
and if the net power is less than 0, controlling the energy storage system to charge the photovoltaic system, and if the capacity of the energy storage system is insufficient, purchasing electricity from the power grid.
3. The method of claim 1, wherein establishing the optical storage charging station as a function of operating cost with capacity configured as a variable comprises:
constructing a photovoltaic system investment year conversion cost function by utilizing the photovoltaic installed capacity, the photovoltaic capacity manufacturing cost, the photovoltaic conversion rate and the photovoltaic service life;
constructing an energy storage system investment year reduced cost by utilizing the energy storage installed capacity, the energy storage capacity manufacturing cost, the energy storage conversion rate and the energy storage service life;
the operation cost function is the sum of the photovoltaic system investment year reduced cost function and the energy storage system investment year reduced cost.
4. The method of claim 1, wherein establishing a benefit objective function for the optical storage charging station based on the operating cost function, the electricity purchase information, the charging information, the electricity sale information, and the carbon emission power comprises:
the electricity purchasing information comprises the price of the electricity purchasing period and the power of the electricity purchasing period;
the charging information comprises a charging period price and a charging period power;
the electricity selling information comprises price of electricity selling time period and power of electricity selling time period;
the benefit objective function is the product of the price of the electricity purchasing period and the power of the electricity purchasing period, the product of the price of the electricity charging period and the power of the electricity charging period, the product of the price of the electricity selling period and the power of the electricity selling period, the product of the carbon emission power and the carbon emission coefficient and the carbon emission weight, and the accumulated value of the operation cost function.
5. The method of claim 4, further comprising:
taking the sum of the photovoltaic installed capacity, the stored energy charging and discharging power, the electricity purchasing power and the charging power of the light storage charging station as 0 as a first constraint condition of the benefit objective function;
constraining an energy storage system state of charge of the optical storage charging station as a second constraint condition for the benefit objective function;
a third constraint condition that the working state of the energy storage system of the optical storage charging station can only be one of a charging state or a discharging state is taken as the benefit objective function;
taking the maximum charge/discharge capacity of the energy storage system of the optical storage charging station as a fourth constraint condition of the benefit objective function;
a fifth constraint condition that takes a maximum charging/discharging power of an energy storage system of the optical storage charging station as the benefit objective function.
6. The method of claim 5, wherein the calculating the optimal capacity configuration for the benefit objective function at the maximum using a genetic algorithm comprises:
substituting the first constraint, the first constraint and the fifth constraint into the benefit objective function;
determining a boundary value of the photovoltaic installed capacity and a boundary value of the energy storage installed capacity;
randomly generating a photovoltaic initialization population and an energy storage initialization population by using a binary coding mode;
carrying out chromosome decoding, determining the process photovoltaic installed capacity and the process energy storage installed capacity, and calculating a current benefit objective function value according to the process photovoltaic installed capacity and the process energy storage installed capacity;
calculating the individual adaptation degree, and performing selection, crossing and mutation operations until the maximum iteration number is reached;
and outputting the optimal photovoltaic installed capacity and the energy storage installed capacity.
7. An optical storage charging station capacity optimal configuration system, comprising:
a demand generation unit configured to generate a user charging demand using a markov chain monte carlo simulation method;
the strategy making unit is configured for making an operation strategy of the optical storage charging station according to the charging requirement of the user, wherein the operation strategy comprises electricity purchasing information, charging information, electricity selling information and carbon emission power;
the cost modeling unit is configured to establish an operation cost function of the optical storage charging station with capacity configuration as a variable;
the function construction unit is configured to establish a benefit objective function of the optical storage charging station according to the operation cost function, the electricity purchasing information, the charging information, the electricity selling information and the carbon emission power;
and the configuration calculation unit is used for calculating the optimal capacity configuration when the benefit objective function takes the maximum value by utilizing a genetic algorithm.
8. The system of claim 7, wherein the cost modeling unit comprises:
the photovoltaic calculation module is configured for constructing a photovoltaic system investment year conversion cost function by utilizing the photovoltaic installed capacity, the photovoltaic capacity manufacturing cost, the photovoltaic conversion rate and the photovoltaic service life;
the energy storage calculation module is configured for constructing an energy storage system investment year reduced cost by utilizing the energy storage installed capacity, the energy storage capacity manufacturing cost, the energy storage conversion rate and the energy storage service life;
and the cost calculation module is configured to enable the operation cost function to be the sum of the photovoltaic system investment year conversion cost function and the energy storage system investment year conversion cost.
9. A terminal, comprising:
a processor;
a memory for storing instructions for execution by the processor;
wherein the processor is configured to perform the method of any one of claims 1-6.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1-6.
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