CN116979619A - Micro-grid energy storage configuration method and device, computer equipment and storage medium - Google Patents

Micro-grid energy storage configuration method and device, computer equipment and storage medium Download PDF

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Publication number
CN116979619A
CN116979619A CN202310955042.XA CN202310955042A CN116979619A CN 116979619 A CN116979619 A CN 116979619A CN 202310955042 A CN202310955042 A CN 202310955042A CN 116979619 A CN116979619 A CN 116979619A
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China
Prior art keywords
energy storage
target
grid
micro
cost
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Inventor
苏一博
王琳
王鹏磊
乐波
周旭艳
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China Three Gorges Corp
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China Three Gorges Corp
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/007—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources
    • H02J3/0075—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources for providing alternative feeding paths between load and source according to economic or energy efficiency considerations, e.g. economic dispatch
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/22—The renewable source being solar energy
    • H02J2300/24—The renewable source being solar energy of photovoltaic origin
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/28—The renewable source being wind energy

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  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Supply And Distribution Of Alternating Current (AREA)

Abstract

The invention relates to the technical field of micro-grid energy storage configuration, and discloses a micro-grid energy storage configuration method, a device, computer equipment and a storage medium, wherein the method comprises the following steps: acquiring the full life cycle cost of an energy storage system in a micro-grid; acquiring energy storage benefits and wind and light utilization rate of an energy storage system in a preset time period, and determining an objective function based on the energy storage benefits and the wind and light utilization rate; establishing an energy storage capacity planning model based on the full life cycle cost; and solving the energy storage capacity planning model based on the objective function and a preset constraint condition set to obtain an energy storage configuration result of the objective micro-grid. According to the invention, the total life cycle cost of the energy storage system is considered, the energy storage income and the wind and light utilization rate are taken as target functions, the energy utilization rate of the energy storage system in a preset time period is optimized to obtain the energy storage configuration result of the target micro-grid, the energy storage economy is ensured, and the energy storage utilization rate of the micro-grid can be improved under the configuration of the energy storage configuration result of the target micro-grid.

Description

Micro-grid energy storage configuration method and device, computer equipment and storage medium
Technical Field
The invention relates to the technical field of micro-grid energy storage configuration, in particular to a micro-grid energy storage configuration method, a micro-grid energy storage configuration device, computer equipment and a storage medium.
Background
The energy storage system is an important component of the power system of 'acquisition-generation-transmission-distribution-utilization-storage', and is a foundation for constructing a new energy micro-grid. New energy power generation such as wind power, photovoltaic and the like belongs to intermittent energy power generation, has obvious instability and is deeply plagued by 'wind abandoning' and 'limited power generation'.
As a solution for new energy access, the concept of micro-grid has been developed. The micro-grid combines the generator, load, energy storage device, control device, etc. from a system perspective to form a single controllable unit. Because the total supply power and the load of the power supply cannot be in a supply-demand balance state at any time, the energy storage system is required to absorb the redundant energy of the system or release the energy to make up for the energy shortage of the system. The purpose of safe and reliable power supply is achieved by utilizing the energy storage equipment to stabilize voltage and adjust frequency during off-grid and on-grid operation, and the system power is balanced when a distributed power supply is connected and high-quality electric energy is supplied to a load. Therefore, the energy storage system is necessary in the micro-grid, the problem of unbalance of supply and demand of electric energy can be solved, and the energy storage system mainly plays roles of regulating peak power of electric power and improving running stability and electric energy quality of the micro-grid in the electric power system.
At present, researches on micro-grid energy storage configuration planning mainly focus on medium-short term configuration planning considering economic targets such as cost reduction and income maximization, and researches on long term configuration planning considering the whole energy storage life cycle are less. Therefore, a new energy storage configuration method of the micro-grid considering the whole energy storage life cycle is needed to be proposed.
Disclosure of Invention
In view of the above, the invention provides a micro-grid energy storage configuration method to solve the problems that the existing research on micro-grid energy storage configuration planning is mainly focused on medium-short term configuration planning considering economic targets such as cost reduction and income maximization, and the research on long term configuration planning considering the whole energy storage life cycle is less.
In a first aspect, the present invention provides a method for storing energy in a micro-grid, where the method includes:
acquiring the full life cycle cost of an energy storage system in a micro-grid; acquiring energy storage benefits and wind and light utilization rate of an energy storage system in a preset time period, and determining an objective function based on the energy storage benefits and the wind and light utilization rate; establishing an energy storage capacity planning model based on the full life cycle cost; and solving the energy storage capacity planning model based on the objective function and a preset constraint condition set to obtain an energy storage configuration result of the objective micro-grid.
According to the micro-grid energy storage configuration method provided by the invention, the total life cycle cost of the energy storage system is considered, the energy storage income and the wind-light utilization rate are taken as target functions, the energy utilization rate of the energy storage system in a preset time period is optimized to obtain a target micro-grid energy storage configuration result, the energy storage economy is ensured, and further, the energy storage utilization rate of the micro-grid can be improved under the configuration of the target micro-grid energy storage configuration result.
In an alternative embodiment, acquiring full life cycle costs of an energy storage system in a micro-grid includes:
acquiring target comprehensive cost and equivalent cycle times of an energy storage system in a micro-grid; establishing an energy storage life model based on the equivalent cycle times; and determining the full life cycle cost of the energy storage system based on the energy storage life model and the target comprehensive cost.
According to the invention, the energy storage life model is built according to the equivalent cycle times of the energy storage system, the influence of other factors on the energy storage life is ignored, and further, the total life cycle cost of the energy storage system is determined by combining the target comprehensive cost of the energy storage system, so that the accuracy of the total life cycle cost of the energy storage system is improved.
In an alternative embodiment, obtaining the target integrated cost and the equivalent cycle number of the energy storage system in the micro-grid includes:
Acquiring investment cost, replacement cost, operation and maintenance cost, treatment cost, recovery cost and energy storage discharge capacity of the energy storage system; determining a target composite cost based on the investment cost, the replacement cost, the operation and maintenance cost, the processing cost, and the recovery cost; and determining the equivalent cycle times based on the energy storage and discharge quantity.
The target comprehensive cost provided by the invention considers the investment cost, the replacement cost, the operation and maintenance cost, the treatment cost and the recovery cost of the energy storage system, and meets the international standard IEC 60300-3-3.
In an alternative embodiment, building the energy storage life model based on the equivalent number of cycles includes:
acquiring a first energy storage cycle number of an energy storage system; determining a second energy storage cycle number based on the energy storage discharge amount and the first energy storage cycle number; and establishing an energy storage life model based on the second energy storage cycle times and the equivalent cycle times.
According to the invention, the cycle times under different discharge depths are calculated to be 100% of the cycle times under the discharge depths, the energy storage life model is built, the discharge depths are only used as core factors influencing the life loss of the battery, and the influence of other factors on the energy storage life is ignored.
In an alternative embodiment, solving the energy storage capacity planning model based on the objective function and the preset constraint condition set to obtain an energy storage configuration result of the target micro-grid includes:
Acquiring a preset constraint condition set; and solving the energy storage capacity planning model by utilizing an improved simulated annealing algorithm and a main target method based on the target function and a preset constraint condition set to obtain an energy storage configuration result of the target micro-grid.
The improved simulated annealing algorithm solves the problem that the traditional particle swarm optimization algorithm is easy to be trapped in a local extremum, solves the energy storage capacity planning model by combining a main target method, does not mask the change characteristics of the energy storage of the micro-grid in different time periods, improves the configuration accuracy of the energy storage configuration result of the target micro-grid, and further can improve the energy storage utilization rate of the micro-grid.
In an alternative embodiment, based on the objective function and the preset constraint condition set, the energy storage capacity planning model is solved by using the improved simulated annealing algorithm and the main objective method to obtain an energy storage configuration result of the objective micro-grid, including:
determining a first target, a second target and a third target of the energy storage system based on the objective function; solving an energy storage capacity planning model by utilizing an improved simulated annealing algorithm based on a preset constraint condition set to obtain an initial micro-grid energy storage configuration result meeting a first target; and solving the energy storage capacity planning model by using a main target method based on the second target, the preset constraint condition set and the initial micro-grid energy storage configuration result to obtain a target micro-grid energy storage configuration result meeting the third target.
According to the method, the final target micro-grid energy storage configuration result is determined by meeting different targets by the energy storage capacity planning model, the change characteristics of the micro-grid energy storage in different time periods are not covered, the configuration accuracy of the target micro-grid energy storage configuration result is improved, and the energy storage utilization rate of the micro-grid can be further improved.
In an alternative embodiment, based on a preset constraint condition set, solving the energy storage capacity planning model by using an improved simulated annealing algorithm to obtain an initial micro-grid energy storage configuration result meeting the first objective, including:
determining a target energy storage output of the energy storage system meeting a preset constraint condition set by utilizing an improved simulated annealing algorithm; and solving the energy storage capacity planning model based on the target energy storage output to obtain an initial micro-grid energy storage configuration result meeting the first target.
The invention solves the problem that the traditional particle swarm optimization algorithm is easy to be trapped into a local extremum by utilizing an improved simulated annealing algorithm to solve an energy storage capacity planning model.
In an alternative embodiment, based on the second target, the preset constraint condition set and the initial micro-grid energy storage configuration result, the energy storage capacity planning model is solved by using a main target method, so as to obtain the micro-grid energy storage configuration result meeting the third target, including:
Obtaining a target scaling factor; converting the initial micro-grid energy storage configuration result into a target constraint condition based on the target scaling coefficient, the second target and a preset constraint condition set; and solving the energy storage capacity planning model based on the target constraint condition to obtain a target micro-grid energy storage configuration result meeting a third target.
According to the method, the energy storage capacity planning model is solved by using the main target method, the change characteristics of the energy storage of the micro-grid in different time periods are not covered, the configuration accuracy of the target micro-grid energy storage configuration result is improved, and the energy storage utilization rate of the micro-grid can be further improved.
In an alternative embodiment, the preset constraint set includes: genset operation constraints, energy storage system constraints, and power system constraints.
The invention can ensure the stable operation of the energy storage system by considering the operation constraint of the generator set, the constraint of the energy storage system and the constraint of the power system.
In an alternative embodiment, the genset operating constraints include: gas turbine operating constraints and renewable energy operating constraints.
In an alternative embodiment, the energy storage system constraints include: energy storage operation constraints, energy storage power constraints, and sustained discharge time constraints.
In an alternative embodiment, the power system constraints include: power balance constraints, reserve capacity constraints, and renewable energy permeability constraints.
In a second aspect, the present invention provides a micro-grid energy storage configuration device, comprising:
the acquisition module is used for acquiring the full life cycle cost of the energy storage system in the micro-grid; the acquisition and determination module is used for acquiring energy storage benefits and wind and light utilization rates of the energy storage system in a preset time period and determining an objective function based on the energy storage benefits and the wind and light utilization rates; the building module is used for building an energy storage capacity planning model based on the total life cycle cost; and the solving module is used for solving the energy storage capacity planning model based on the objective function and the preset constraint condition set to obtain an energy storage configuration result of the target micro-grid.
According to the micro-grid energy storage configuration device, the total life cycle cost of the energy storage system is considered, the energy storage income and the wind-light utilization rate are taken as target functions, the energy utilization rate of the energy storage system in a preset time period is optimized to obtain a target micro-grid energy storage configuration result, the energy storage economy is guaranteed, and further, the energy storage utilization rate of the micro-grid can be improved under the configuration of the target micro-grid energy storage configuration result.
In a third aspect, the present invention provides a computer device comprising: the micro-grid energy storage configuration method comprises a memory and a processor, wherein the memory and the processor are in communication connection, the memory stores computer instructions, and the processor executes the computer instructions, so that the micro-grid energy storage configuration method of the first aspect or any corresponding implementation mode of the first aspect is executed.
In a fourth aspect, the present invention provides a computer readable storage medium, on which computer instructions are stored, the computer instructions being configured to cause a computer to perform the method for storing energy in a micro grid according to the first aspect or any one of the embodiments corresponding thereto.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic structural diagram of a dc bus independent micro-grid according to an embodiment of the present invention;
Fig. 2 is a schematic flow chart of a micro-grid energy storage configuration method according to an embodiment of the invention;
FIG. 3 is a flow chart of another micro-grid energy storage configuration method according to an embodiment of the invention;
FIG. 4 is a flow chart of yet another method of micro-grid energy storage configuration according to an embodiment of the invention;
FIG. 5 is a block diagram of a micro-grid energy storage configuration device according to an embodiment of the invention;
fig. 6 is a schematic diagram of a hardware structure of a computer device according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The energy storage system is an important component of the power system of 'acquisition-generation-transmission-distribution-utilization-storage', and is a foundation for constructing a new energy micro-grid. New energy power generation such as wind power, photovoltaic and the like belongs to intermittent energy power generation, has obvious instability and is deeply plagued by 'wind abandoning' and 'limited power generation'.
As a solution for new energy access, the concept of micro-grid has been developed.
When the micro-grid independently operates, local wind power resources, photovoltaic resources and the like are converted into electric energy and are used for supplying loads in the micro-grid in cooperation with the energy storage system. The micro-grid is divided into a direct current hybrid micro-grid, an alternating current hybrid micro-grid and an alternating current hybrid micro-grid according to the differences of the bus bars. The distributed power supply and the energy storage in the direct current bus microgrid are connected to the bus through the converter, the direct current bus independent type microgrid modeling analysis is selected in the embodiment of the invention, and the structure is shown in figure 1.
Wherein P is w (t) represents the total output power of the wind generating set; p (P) v (t) is the total power output by photovoltaic power generation; p (P) g (t) is the total power of the power generation output of the gas turbine; p (P) l (t) is load demand power; p (P) hess (t) is the output power of the energy storage system; p (P) bat And (t) is the output power of the storage battery.
Further, according to the micro-grid structure, the embodiment of the invention provides a micro-grid energy storage configuration method, by taking the total life cycle cost of an energy storage system into consideration, and taking the energy storage income and the wind-solar utilization rate as objective functions, the energy utilization rate of the energy storage system in a preset time period is optimized to obtain a target micro-grid energy storage configuration result, so that the effects of guaranteeing the energy storage economy and further improving the energy storage utilization rate of the micro-grid are achieved.
According to an embodiment of the present invention, there is provided a micro-grid energy storage configuration method embodiment, it should be noted that the steps illustrated in the flowchart of the drawings may be performed in a computer system such as a set of computer executable instructions, and that although a logical order is illustrated in the flowchart, in some cases the steps illustrated or described may be performed in an order different from that herein.
In this embodiment, a method for storing energy in a micro-grid is provided, which may be used in the above-mentioned dc bus independent micro-grid shown in fig. 1, and fig. 2 is a flowchart of a method for storing energy in a micro-grid according to an embodiment of the present invention, as shown in fig. 2, where the flowchart includes the following steps:
step S201, acquiring the full life cycle cost of the energy storage system in the micro-grid.
The full life cycle cost of the energy storage system represents the cost of the energy storage system running throughout the life cycle.
Step S202, obtaining energy storage benefits and wind and light utilization rates of an energy storage system in a preset time period, and determining an objective function based on the energy storage benefits and the wind and light utilization rates.
The preset time period in this embodiment represents 4 seasons of spring, summer, autumn and winter; the wind and light utilization rate represents the wind and light rejection rate; the objective function includes maximum energy storage gain and minimum wind utilization.
Specifically, when the renewable energy source absorbing capacity of the power grid is insufficient, the energy storage can store electric quantity, and the electric quantity is released when the renewable energy source absorbing capacity is abundant, so that the problems of wind abandoning and light abandoning are solved. Therefore, in this embodiment, wind power and photovoltaic grid-connected benefits added after energy storage are used as energy storage benefits, and the maximum energy storage benefits are shown in the following relation (1):
maxS ess ＝S new -C cost (1)
wherein: s is S ess Representing energy storage benefits;S new the newly added renewable energy source grid-connected income is represented as the following relational expression (2); c (C) cost Representing the annual cost of energy storage.
Wherein: i=1, 2,3,4 represents 4 seasons of spring, summer, autumn, winter; t=1, 2,3,4 represents 24 periods of the day;wind power output representing season i period t; />The new wind power generation capacity of the season i in the period t is represented; />Photovoltaic output representing season i period t; />The new photovoltaic power generation amount of the period t of the season i is represented; p is p w Representing the wind power online electricity price; p is p pv And the photovoltaic internet electricity price is represented.
Further, the minimum wind and light utilization rate is shown in the following relational expression (3):
wherein: l represents the wind and light utilization rate;the upper limit of wind power output of the season i period t is represented; />The upper limit of the photovoltaic output for the period t of season i is indicated.
Step S203, an energy storage capacity planning model is established based on the full life cycle cost.
Specifically, according to the description of step S201, the full life cycle cost may represent the cost generated by the energy storage system running in the whole life cycle, so the embodiment establishes a corresponding energy storage capacity planning model based on the full life cycle cost, so that the constructed energy storage capacity planning model considers the full life cycle cost of the energy storage system, and improves the accuracy of the model.
And step S204, solving the energy storage capacity planning model based on the objective function and the preset constraint condition set to obtain an energy storage configuration result of the target micro-grid.
The target micro-grid energy storage configuration result comprises energy storage optimal capacity and power.
Specifically, with the maximum energy storage income and the minimum wind and light utilization rate as targets, solving the energy storage capacity planning model can obtain a target micro-grid energy storage configuration result meeting a preset constraint condition set.
According to the micro-grid energy storage configuration method, the total life cycle cost of the energy storage system is considered, the energy storage income and the wind-solar utilization rate are taken as target functions, the energy utilization rate of the energy storage system in a preset time period is optimized to obtain a target micro-grid energy storage configuration result, the energy storage economy is guaranteed, and further, the energy storage utilization rate of the micro-grid can be improved under the configuration of the target micro-grid energy storage configuration result.
In this embodiment, a method for storing energy in a micro-grid is provided, which may be used in the above-mentioned dc bus independent micro-grid shown in fig. 1, and fig. 3 is a flowchart of a method for storing energy in a micro-grid according to an embodiment of the present invention, as shown in fig. 3, where the flowchart includes the following steps:
step S301, acquiring the full life cycle cost of the energy storage system in the micro-grid.
Specifically, the step S301 includes:
step S3011, obtaining target comprehensive cost and equivalent cycle times of the energy storage system in the micro-grid.
The target comprehensive cost represents the energy storage annual cost of the energy storage system.
Specifically, the cycle times of the battery in the energy storage system under different discharge depths can be calculated as the equivalent full cycle times under 100% discharge depths, namely the equivalent cycle times of the energy storage system.
Step S3012, an energy storage life model is built based on the equivalent cycle times.
Specifically, the energy storage life model is an equivalent cycle life model based on the depth of discharge, and the principle is that the cycle use times under different depths of discharge are calculated to be equivalent cycle times under 100% of the depth of discharge, so that the corresponding energy storage life model can be established through the obtained equivalent cycle times.
Step S3013, determining a full life cycle cost of the energy storage system based on the energy storage life model and the target integrated cost.
Specifically, according to the description of step S201, the full life cycle cost represents the cost generated by the energy storage system running in the whole life cycle, and therefore, according to the established energy storage life model and the target integrated cost, the full life cycle cost corresponding to the energy storage system can be determined.
According to the method and the device, the energy storage life model is built according to the equivalent cycle times of the energy storage system, influences of other factors on the energy storage life are ignored, further, the total life cycle cost of the energy storage system is determined by combining the target comprehensive cost of the energy storage system, and accuracy of the total life cycle cost of the energy storage system is improved.
In some optional embodiments, step S3011 includes:
step a1, acquiring investment cost, replacement cost, operation and maintenance cost, processing cost, recovery cost and energy storage discharge capacity of the energy storage system.
Step a2, determining a target comprehensive cost based on the investment cost, the replacement cost, the operation and maintenance cost, the treatment cost and the recovery cost.
And a step a3, determining the equivalent cycle times based on the energy storage and discharge quantity.
In particular, according to the international standard IEC60300-3-3, the full life cycle cost should generally comprise 6 parts: product design cost, manufacturing cost and purchasing cost Cost, use cost, maintenance cost, and disposal cost. Full lifecycle cost C considered in this embodiment cost Including investment cost C 1 Cost of replacement C 2 Cost of operation and maintenance C 3 Cost of treatment C 4 And recovery cost C 5 。
Wherein the ESS is composed of storage cells, power conversion system PCS (power conversionsystem) and auxiliary equipment 3, thus investment cost C 1 The following relation (4) shows:
C 1 ＝c E E ess +c P P ess +c B E ess (4)
wherein: c E Representing the price per unit capacity of the energy storage system, and the unit/(kW.h); c P Representing the price per unit power of the energy storage system, the/kW; c B Representing the price of auxiliary equipment per kilowatt-hour, and the unit/(kW.h); e (E) ess Representing the rated capacity of the ESS of the energy storage system; p (P) ess Indicating the power rating of the ESS of the energy storage system.
Preferably, the life cycle of the ESS and PCS is not sufficient to meet the full project cycle requirements if not replaced, thus replacing cost C 2 The following relation (5) shows:
wherein: k represents the total number of battery replacements in the energy storage system (rounded up, k=γ/n-1); epsilon represents the replacement order; beta represents the average annual decline rate of the investment cost of the ESS of the energy storage system; sigma represents the discount rate.
Preferably, the operation and maintenance cost C 3 Consists of labor cost and management cost, and is related to initial investment as shown in the following relation (6):
C 3 ＝c f C 1 (6)
wherein: c f Representing the energy storage operation and maintenance cost coefficient.
Preferably, the energy storage system ESS is over its lifetimeShutdown at the end of the period, thus, processing cost C 4 The following relation (7) shows:
wherein: c d Representing one particular processing cost per unit power of the ESS of the energy storage system, meta/kW.
Preferably, the ESS is recyclable, and therefore, the cost of recycling C 5 The following relation (8) shows:
C 5 ＝c res (C 1 +C 2 ) (8)
wherein: c res Represents recovery rate, and is usually 3 to 5%.
Finally, the target integrated cost of the energy storage system is represented by the following relation (9):
cost＝C 1 +C 2 +C 3 +C 4 -C 5 (9)
further, the full life cycle cost C of the energy storage system can be obtained by combining the energy storage life model cost The following relation (10) shows:
C cost ＝η(C 1 +C 2 +C 3 +C 4 -C 5 ) (10)
wherein: η represents an equi-series funds recovery coefficient.
Wherein, considering the time value of funds, the energy storage equivalent life is used for converting the adult investment cost, and the equiquantity series funds recovery coefficient eta is shown in the following relational expression (11):
wherein: n is n ess The age when the energy storage reaches the end of the service life of the battery, namely the equivalent service life of the energy storage, can be obtained according to an energy storage service life model.
Preferably, the energy storage and discharge quantity Q of the energy storage system cyc (t i ) The following relation (12)The following is shown:
Q cyc (t i )＝(1-SOC(t i -1))S e (t i ) (12)
wherein: q (Q) cyc (t i ) The actual discharge capacity of the stored energy in the period t of the season i is represented; SOC represents the state of charge of the battery in the energy storage system; s is S e Represents a 0-1 variable introduced by charge-discharge cycle, S e =1 indicates that a charge-discharge cycle occurs at time t and the depth of discharge is calculated, otherwise, indicates that no charge-discharge cycle occurs and the depth of discharge is 0.
Further, based on the energy storage and discharge quantity Q cyc (t i ) And determining the equivalent cycle times.
Firstly, the cycle times of each battery in the energy storage system at different depths are converted into equivalent full cycle times at 100% discharge depth. Equivalent full cycle number n corresponding to each charge-discharge cycle eq The expression (13) below is used to obtain:
wherein: n is n eq (t i ) The equivalent full cycle times corresponding to each charge-discharge cycle of the period t of the season i are represented; k (k) p The fitting constant is represented and may be provided by the manufacturer corresponding to the battery in the energy storage system.
Further, the equivalent cycle number N of the energy storage system in the whole life cycle can be obtained eq The following relation (14) shows:
the target comprehensive cost provided by the embodiment considers the investment cost, the replacement cost, the operation and maintenance cost, the treatment cost and the recovery cost of the energy storage system, and meets the international standard IEC 60300-3-3.
In some optional embodiments, step S3012 includes:
step b1, obtaining a first energy storage cycle number of the energy storage system.
And b2, determining a second energy storage cycle number based on the energy storage and discharge amount and the first energy storage cycle number.
And b3, establishing an energy storage life model based on the second energy storage cycle times and the equivalent cycle times.
Wherein the first energy storage cycle number N e The cycle number indicating when the battery energy storage reaches the end of life can be provided by the manufacturer corresponding to the battery in the energy storage system.
Number of second energy storage cycles N s The number of cycles when the battery reached the end of life was represented by the charge and discharge at 100% depth of discharge.
Specifically, the second energy storage cycle number N of the energy storage system can be obtained by the following relation (15) s ：
Further, the equivalent cycle number N shown in the above relation (14) is combined eq And (3) establishing an energy storage life model, wherein the energy storage life model is shown in the following relation (16):
wherein: n is n ess Indicating the age of the battery at which the stored energy reaches the end of its life.
According to the embodiment, the cycle times under different discharge depths are calculated to be 100% of the cycle times under the discharge depths, the energy storage life model is built, the discharge depths are only used as core factors influencing the life loss of the battery, and the influence of other factors on the energy storage life is ignored.
Step S302, energy storage benefits and wind and light utilization rates of the energy storage system in a preset time period are obtained, and an objective function is determined based on the energy storage benefits and the wind and light utilization rates. Please refer to step S202 in the embodiment shown in fig. 2, which is not described herein.
Step S303, an energy storage capacity planning model is established based on the full life cycle cost. Please refer to step S203 in the embodiment shown in fig. 2 in detail, which is not described herein.
And step S304, solving the energy storage capacity planning model based on the objective function and a preset constraint condition set to obtain an energy storage configuration result of the target micro-grid. Please refer to step S204 in the embodiment shown in fig. 2 in detail, which is not described herein.
According to the micro-grid energy storage configuration method provided by the invention, the circulation times under different discharge depths are calculated to be 100% of the circulation times under the discharge depths, an energy storage life model is built, the discharge depths are only used as core factors influencing the life loss of the battery, and the influence of other factors on the energy storage life is ignored; the full life cycle cost of the energy storage system is considered, the energy storage income and the wind and light utilization rate are taken as target functions, the energy utilization rate of the energy storage system in a preset time period is optimized to obtain a target micro-grid energy storage configuration result, the energy storage economy is guaranteed, and further, the energy storage utilization rate of the micro-grid can be improved under the configuration of the target micro-grid energy storage configuration result.
In this embodiment, a method for storing energy in a micro-grid is provided, which may be used in the above-mentioned dc bus independent micro-grid shown in fig. 1, and fig. 4 is a flowchart of a method for storing energy in a micro-grid according to an embodiment of the present invention, as shown in fig. 4, where the flowchart includes the following steps:
step S401, acquiring the full life cycle cost of the energy storage system in the micro-grid. Please refer to step S301 in the embodiment shown in fig. 3 in detail, which is not described herein.
Step S402, obtaining energy storage benefits and wind and light utilization rates of the energy storage system in a preset time period, and determining an objective function based on the energy storage benefits and the wind and light utilization rates. Please refer to step S202 in the embodiment shown in fig. 2, which is not described herein.
Step S403, an energy storage capacity planning model is established based on the full life cycle cost. Please refer to step S203 in the embodiment shown in fig. 2 in detail, which is not described herein.
And step S404, solving the energy storage capacity planning model based on the objective function and the preset constraint condition set to obtain an energy storage configuration result of the target micro-grid.
Specifically, the step S404 includes:
step S4041, a preset constraint condition set is acquired.
The preset constraint condition set may include a generator set operation constraint, an energy storage system constraint, and a power system constraint.
Preferably, the genset operating constraints may include gas turbine operating constraints and renewable energy operating constraints:
(1) Gas turbine operating constraints: the gas turbine can not change the output and the start-stop state at will during operation, so that the upper and lower limits of the output, the start-stop time and the climbing rate are restrained, and the following relational expression (17) is shown:
wherein:gas turbine output representing season i period t; q (Q) gas,min Representing a lower limit of gas turbine output; q (Q) gas ,max Representing an upper limit of gas turbine output; />A gas turbine duration representing a season i period t; />Gas turbine downtime, representing season i period t; r is R on Indicating minimum continuous operation of the gas turbine; r is R off Representing a minimum downtime of the gas turbine; u (u) i,t A gas turbine start-stop 0-1 variable representing a season i period t, wherein 0 and 1 represent a shutdown and an operational state, respectively; />A gas turbine ramp rate representing a season i period t; q (Q) th,v Indicating the upper limit of the gas turbine ramp rate.
(2) Renewable energy operation constraints: under the influence of natural environment, wind power and photovoltaic have upper and lower output limits in actual operation, and the following relational expression (18) shows:
wherein:fan output for period t of season i; />A lower limit of fan output for a period t of season i; The upper limit of the fan output of the season i in the period t is represented; />Photovoltaic output representing season i period t; />A lower photovoltaic output limit representing a season i period t; />The upper limit of the photovoltaic output for the period t of season i is indicated.
Preferably, the energy storage system constraints may include energy storage operation constraints, energy storage power constraints, and duration constraints:
(1) Energy storage operation constraint: the running condition of the energy storage system is generally represented by the state of charge, in order to ensure the stable running of the energy storage system, the state of charge is limited in a certain range, and the state of charge at the beginning and the end of one scheduling period is equal, so that the energy storage capacity in the next period is ensured, and the following relation (19) is shown:
wherein:representing the stored energy state of charge of the season i during period t; e (E) soc,min Representing a lower energy storage state of charge limit; e (E) soc ,max Representing an upper energy storage state of charge limit; zeta type c Representing energy storage charging efficiency; zeta type d Representing energy storage discharge efficiency; />Representing the state of charge at the beginning of the scheduling period for season i; />Indicating the state of charge at the end of the scheduling period for season i.
(2) Energy storage power constraint: the energy storage system cannot be in a charging and discharging state at the same time, and the maximum charging and discharging power cannot exceed the rated power when planning, as shown in the following relation (20):
Wherein:representing the actual charge of the stored energy in the season i during period t; />Representing the actual discharge of stored energy in season i and period t
(3) Duration discharge time constraint: when the energy storage capacity is configured, the capacity selection is too small, the regulating effect on the regulating system is limited, and the investment and operation and maintenance cost can be obviously increased when the capacity selection is too large. The energy storage continuous discharge time refers to the time when the energy storage continuously works at rated power, and is directly related to the capacity and power of the energy storage, so that the expected continuous discharge time is restrained, and the energy storage cost is further controlled, wherein the energy storage cost is shown in the following relation (21):
wherein: h represents the expected duration of energy storage, and can be reasonably selected according to different energy storage configuration requirements; h min Representing the minimum duration of the stored energy; h max Indicating the maximum duration of the stored energy.
Preferably, the power system constraints may include power balance constraints, reserve capacity constraints, and renewable energy permeability constraints:
(1) The power balance constraint is represented by the following relation (22):
wherein: g i,t Indicating the load demand of the season i for the period t.
(2) The reserve capacity constraint is represented by the following relationship (23):
wherein: alpha represents the gas turbine backup capacity coefficient.
(3) Renewable energy permeability constraints: the power fluctuation and randomness of the renewable energy source can bring impact to the power grid, and the maximum grid-connected proportion of the renewable energy source is constrained on the premise of ensuring continuous and stable power supply, as shown in the following relational expression (24):
wherein: beta represents the maximum permeability of renewable energy.
Step S4042, solving the energy storage capacity planning model by using an improved simulated annealing algorithm and a main target method based on the target function and the preset constraint condition set to obtain a target micro-grid energy storage configuration result.
Conventional particle swarm optimization algorithms (Particle Swarm Optimization, PSO) tend to trap local extrema, and therefore, the simulated annealing [13] concept (Simulated Annealing, SA) and Metropolis criteria are introduced into the particle swarm optimization algorithm, resulting in an improved simulated annealing algorithm.
Furthermore, the commonly used multi-objective solving method mainly comprises an intelligent algorithm and a linear weighting method, the methods need to normalize the objective before conversion, and the normalization process can mask the change characteristics of renewable energy sources in different seasons. Therefore, the model solution is performed by using the main objective method in this embodiment.
Specifically, the energy storage capacity planning model is solved by using an improved simulated annealing algorithm and a main target method, so that a target micro-grid energy storage configuration result meeting an objective function and a preset constraint condition set can be obtained.
In some alternative embodiments, step S4042 includes:
step c1, determining a first target, a second target and a third target of the energy storage system based on the objective function.
And c2, solving an energy storage capacity planning model by utilizing an improved simulated annealing algorithm based on a preset constraint condition set to obtain an initial micro-grid energy storage configuration result meeting a first target.
And c3, solving the energy storage capacity planning model by using a main target method based on the second target, the preset constraint condition set and the initial micro-grid energy storage configuration result to obtain a target micro-grid energy storage configuration result meeting the third target.
Wherein the first target represents a maximum energy storage gain; the second target represents the maximum wind and light utilization rate; the third goal represents the minimum wind and light utilization.
Firstly, taking the maximum energy storage income as a target, and based on a preset constraint condition set, utilizing an improved simulated annealing algorithm to carry out constraint solution on an energy storage capacity planning model, and obtaining a global optimal solution when the energy storage income is maximum, namely an initial micro-grid energy storage configuration result.
And secondly, taking the minimum wind and light utilization rate as a target, and based on a second target, a preset constraint condition set and an initial micro-grid energy storage configuration result, performing constraint solving on an energy storage capacity planning model by using a main target method to obtain the corresponding energy storage optimal capacity and power when the wind and light utilization rate is minimum, namely, the target micro-grid energy storage configuration result.
In some alternative embodiments, step c2 includes:
step c21, determining the target energy storage output of the energy storage system meeting the preset constraint condition set by utilizing the improved simulated annealing algorithm.
And step c22, solving an energy storage capacity planning model based on the target energy storage output to obtain an initial micro-grid energy storage configuration result meeting the first target.
Firstly, the most economical energy storage output meeting a preset constraint condition set is obtained by utilizing an improved simulated annealing algorithm.
Specifically, when new particles generated by disturbance are x', the current optimal particles of the particle swarm are p best The fitness difference between the two can be expressed as the following relation (25):
Δf＝fitness(x′)-fitness(p best ) (25)
if Δf<0, namely the fitness value of the new particle is smaller than that of the current optimal particle, and receiving the new particle as the optimal particle; conversely, take e -ΔfT Is to receive this particle. This is the Metropolis criterion, where T is the current temperature during the simulated annealing.
Further, based on the target energy storage output, solving an energy storage capacity planning model to obtain a global optimal solution when the energy storage income is maximum, namely an initial micro-grid energy storage configuration result.
In some alternative embodiments, step c3 includes:
Step c31, obtaining the target scaling factor.
And c32, converting the initial micro-grid energy storage configuration result into a target constraint condition based on the target scaling factor, the second target and the preset constraint condition set.
And c33, solving the energy storage capacity planning model based on the target constraint condition to obtain a target micro-grid energy storage configuration result meeting a third target.
Specifically, setting the second target, namely the maximum wind and light utilization rate, as a main target, then introducing a target scaling factor, and converting an initial micro-grid energy storage configuration result when the total energy storage benefit is maximum into a target constraint condition, wherein the target constraint condition is shown in the following relational expression (26):
wherein:the optimal value of solving with the maximum total energy storage benefit as a single target is represented, namely an initial micro-grid energy storage configuration result; />Representing a target scaling factor, wherein ∈ ->A value of 0 indicates that the energy storage system is acceptable as long as the energy storage system is not deficient; />A maximum benefit can be achieved indicated by 1.
Further, the energy storage capacity planning model is solved by taking a third target, namely the minimum wind and light utilization rate, as a target, so that the energy storage optimal capacity and power, namely the energy storage configuration result of the target micro-grid, can be obtained.
According to the micro-grid energy storage configuration method, the problem that a traditional particle swarm optimization algorithm is easy to be trapped in a local extremum is solved by utilizing an improved simulated annealing algorithm, and further, an energy storage capacity planning model is solved by combining a main target method, so that the change characteristics of micro-grid energy storage in different time periods cannot be covered, the configuration accuracy of a target micro-grid energy storage configuration result is improved, and the energy storage utilization rate of the micro-grid can be further improved.
In an example, a micro-grid configured with an 800kW wind turbine unit, a 500kW photovoltaic unit and a 1000kW gas turbine unit is taken as an example for simulation analysis, and the micro-grid energy storage configuration method provided by the above embodiment is verified according to the simulation analysis result.
Specifically, according to the weather division method, 1 month and 7 months are selected as typical months in winter and summer. And predicting the output and the load of wind power and photovoltaic typical days by using the historical data of wind speed, temperature and sunlight intensity of each period every day in a typical month in a certain region of a certain year.
Wherein, wind power and photovoltaic online electricity prices are respectively 0.54 yuan/(kW.h) and 0.6 yuan/(kW.h).
The energy storage realizes peak clipping and valley filling of the electric load and efficient utilization of renewable energy sources through energy time shifting, so that longer discharge time is required. In this example, the lithium battery is adopted to configure the energy storage system at the power generation side:
setting H to 3-6H; c P 1000 yuan/kW; c E 1600 yuan/(kW.h); c f 5%; sigma is 4.9%; n is n 0 1500; zeta type c ，ζ d 90% of the total weight;0.5; e (E) soc,max ，E soc,min 0.9 and 0.1, respectively. Due to the regulation of the stored energy, alpha is reduced to 10% and beta is increased to 80%.
Further, the configuration scale of energy storage and the operation effect of the power system are analyzed according to 3 situations:
Scenario one: carrying out energy storage configuration by taking the maximum total income of the energy storage system as a single target;
scenario two: based on the first scene, adopting a main target method to perform energy storage configuration with the minimum wind and light rejection rate as a target;
scenario three: and (3) specifically selecting a typical day, taking a certain typical day of the whole year as an example to carry out energy storage configuration, and analyzing the influence of the energy storage scale on the operation effect of the power system in other seasons.
Further, the energy storage configuration results and the power system operation effects under the above 3 scenarios are shown in the following table 1:
energy storage configuration and power system operation effect under table 1, 3 scenarios
Parameters (parameters) Scenario one Scene two Scene three
E ess /(kW·h) 121.42 193.07 307.95
P ess /kW 36.16 46.13 58.38
H/h 3.36 4.18 5.27
n ess /a 6.79 6.25 5.13
C ess Meta/Yuan 52234.07 85070.83 151646.52
New year-increasing wind power grid-connected income/yuan 53925.54 80599.98 118037.57
New year-old photovoltaic grid-connected income/element 11702.60 16880.53 30329.62
Annual energy storage benefit/unit 13394.07 12409.68 -3279.33
According to table 1, the energy storage cost is high at present, and when the optimal configuration is carried out with the maximum total energy storage benefit as a single target, the capacity and the power of the energy storage can be reduced as much as possible under the condition of ensuring the safe operation of the power system, the discharge amount of the energy storage can be reduced, the service life of the energy storage can be prolonged, and the energy storage cost can be reduced. The lowest renewable energy utilization rate of a typical day in two seasons after energy storage is improved from 94.22% to 96.29%, and the wind and light discarding rate in two seasons is reduced by 2.05% on average. In order to cope with the impact of the high permeability of the renewable energy sources on the power system, the energy storage scale can be greatly increased, the original abandoned electric quantity is stored when the generated energy of the renewable energy sources is large, and the energy time shifting effect of the energy storage system is better exerted by prolonging the continuous discharge time. When the energy storage income is sacrificed and the higher renewable energy utilization rate is pursued, the lowest renewable energy utilization rate is further improved to 97.11%, and the wind and light abandoning rate is reduced by 2.995% in two seasons. However, the large-scale consumption of renewable energy sources makes it possible to maintain the stability of the power system only by frequent charging and discharging, which causes a great impact on the life of the stored energy. But renewable energy sources brought by newly increased power generation of wind power and photovoltaic are in grid connection income, partial cost of energy storage construction and operation and maintenance can be offset, and the influence on the total income of energy storage is not obvious. And in the third scenario, when the energy storage configuration is carried out only on a typical day in summer, high-efficiency consumption of renewable energy sources is realized by needing larger energy storage capacity and power, and the situation that the energy storage scale is excessive in other seasons is caused. Under the dual effects of the increase of the energy storage configuration scale and the reduction of the equivalent service life, the energy storage annual assembly cost is increased by nearly 2 times compared with the situation one, so that negative benefits are generated.
Further, the renewable energy utilization rates in each season in 3 scenarios are shown in table 2 below:
table 2, renewable energy utilization in winter and summer under 3 scenarios
According to the table 2, with the increase of the energy storage scale, the wind power and the photovoltaic utilization rate are improved to a certain extent, and the 100% absorption of the photovoltaic is realized in the third scenario. Besides the energy storage scale, wind power, photovoltaic actual output and load requirements are also 2 important factors affecting the utilization rate of renewable energy sources. The load demands in summer and winter are significantly higher from a seasonal point of view. Wind power output is mainly concentrated in winter, so that the output exceeds 97% in winter in all 3 scenes, but the wind power output needs to be high in cost. From the power supply type, the effect of improving the photovoltaic absorption by the energy storage configuration is better than that of wind power, and the photovoltaic total energy storage benefit can be improved by preferentially absorbing the photovoltaic due to high electricity price of the photovoltaic on-line.
In summary, the following conclusions are drawn in combination with the energy storage configuration and the power system operation conditions under the above 3 scenarios:
(1) When the income is simply pursued to be maximum, the energy storage depth of discharge is small, the service life is more friendly, and the renewable energy utilization rate is reduced.
(2) The renewable energy utilization rate can be improved by increasing the energy storage scale and prolonging the continuous discharge time, and the energy storage income is reduced but the amplitude is not large.
(3) When the comprehensive utilization rate of the selected single typical sunlight is low, the configured energy storage can be in excessive scale. In contrast, when energy storage configuration is performed only according to seasons with high comprehensive utilization rate of wind and light, the scale of the energy storage configuration may not be enough to be adjusted according to wind power and photovoltaic output in other seasons, so that a large amount of renewable energy sources are wasted.
Therefore, when energy storage planning is carried out, annual change conditions of renewable energy sources should be comprehensively considered, and the energy storage scale and the continuous discharge time are properly increased, so that energy storage income and renewable energy source utilization rate are both considered. In addition, the hybrid energy storage configuration can be selected or an energy storage system can be introduced into an auxiliary service market, and frequency modulation, voltage regulation, black start service and the like are provided through energy storage low-storage high-emission arbitrage, so that the energy storage economic benefit is further improved.
The embodiment also provides a micro-grid energy storage configuration device, which is used for realizing the above embodiment and the preferred implementation manner, and the description is omitted. As used below, the term "module" may be a combination of software and/or hardware that implements a predetermined function. While the means described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware, is also possible and contemplated.
The embodiment provides a micro-grid energy storage configuration device, as shown in fig. 5, including:
the acquiring module 501 is configured to acquire a full life cycle cost of an energy storage system in the micro-grid.
The acquiring and determining module 502 is configured to acquire energy storage gain and wind-light utilization rate of the energy storage system in a preset time period, and determine an objective function based on the energy storage gain and the wind-light utilization rate.
A building module 503 is configured to build an energy storage capacity planning model based on the full life cycle cost.
And the solving module 504 is configured to solve the energy storage capacity planning model based on the objective function and the preset constraint condition set, so as to obtain an energy storage configuration result of the target micro-grid.
In some alternative embodiments, the acquisition module 501 includes:
the first acquisition sub-module is used for acquiring the target comprehensive cost and the equivalent cycle number of the energy storage system in the micro-grid.
And the building sub-module is used for building an energy storage life model based on the equivalent cycle times.
And the determining submodule is used for determining the full life cycle cost of the energy storage system based on the energy storage life model and the target comprehensive cost.
In some alternative embodiments, the first acquisition submodule includes:
the first acquisition unit is used for acquiring investment cost, replacement cost, operation and maintenance cost, processing cost, recovery cost and energy storage discharge capacity of the energy storage system.
A first determination unit for determining a target composite cost based on the investment cost, the replacement cost, the operation and maintenance cost, the processing cost, and the recovery cost.
And the second determining unit is used for determining the equivalent cycle times based on the energy storage discharge quantity.
In some alternative embodiments, establishing the sub-module includes:
the second acquisition unit is used for acquiring the first energy storage cycle times of the energy storage system.
And the third determining unit is used for determining the second energy storage cycle times based on the energy storage discharge quantity and the first energy storage cycle times.
The building unit is used for building an energy storage life model based on the second energy storage cycle times and the equivalent cycle times.
In some alternative embodiments, the solution module 504 includes:
and the second acquisition sub-module is used for acquiring a preset constraint condition set.
And the solving sub-module is used for solving the energy storage capacity planning model by utilizing the improved simulated annealing algorithm and the main target method based on the target function and the preset constraint condition set to obtain the energy storage configuration result of the target micro-grid.
In some alternative embodiments, the solving sub-module includes:
and a fourth determining unit for determining the first, second and third targets of the energy storage system based on the objective function.
The first solving unit is used for solving the energy storage capacity planning model by utilizing an improved simulated annealing algorithm based on a preset constraint condition set to obtain an initial micro-grid energy storage configuration result meeting a first target.
The second solving unit is used for solving the energy storage capacity planning model by utilizing a main target method based on a second target, a preset constraint condition set and an initial micro-grid energy storage configuration result to obtain a target micro-grid energy storage configuration result meeting a third target.
In some alternative embodiments, the first solving unit includes:
and the determining subunit is used for determining the target energy storage output of the energy storage system meeting the preset constraint condition set by utilizing the improved simulated annealing algorithm.
The first solving subunit is used for solving the energy storage capacity planning model based on the target energy storage output to obtain an initial micro-grid energy storage configuration result meeting the first target.
In some alternative embodiments, the second solving unit includes:
and the acquisition subunit is used for acquiring the target scaling coefficient.
And the conversion subunit is used for converting the initial micro-grid energy storage configuration result into a target constraint condition based on the target scaling coefficient, the second target and the preset constraint condition set.
And the second solving subunit is used for solving the energy storage capacity planning model based on the target constraint condition to obtain a target micro-grid energy storage configuration result meeting the third target.
In some alternative embodiments, the set of preset constraints includes: genset operation constraints, energy storage system constraints, and power system constraints.
In some alternative embodiments, the genset operating constraints include: gas turbine operating constraints and renewable energy operating constraints.
In some alternative embodiments, the power system constraints include: power balance constraints, reserve capacity constraints, and renewable energy permeability constraints.
Further functional descriptions of the above respective modules and units are the same as those of the above corresponding embodiments, and are not repeated here.
The micro-grid energy storage configuration device in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit ) circuit, a processor and a memory executing one or more software or fixed programs, and/or other devices that can provide the above functions.
The embodiment of the invention also provides computer equipment, which is provided with the micro-grid energy storage configuration device shown in the figure 5.
Referring to fig. 6, fig. 6 is a schematic structural diagram of a computer device according to an alternative embodiment of the present invention, as shown in fig. 6, the computer device includes: one or more processors 10, memory 20, and interfaces for connecting the various components, including high-speed interfaces and low-speed interfaces. The various components are communicatively coupled to each other using different buses and may be mounted on a common motherboard or in other manners as desired. The processor may process instructions executing within the computer device, including instructions stored in or on memory to display graphical information of the GUI on an external input/output device, such as a display device coupled to the interface. In some alternative embodiments, multiple processors and/or multiple buses may be used, if desired, along with multiple memories and multiple memories. Also, multiple computer devices may be connected, each providing a portion of the necessary operations (e.g., as a server array, a set of blade servers, or a multiprocessor system). One processor 10 is illustrated in fig. 6.
The processor 10 may be a central processor, a network processor, or a combination thereof. The processor 10 may further include a hardware chip, among others. The hardware chip may be an application specific integrated circuit, a programmable logic device, or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general-purpose array logic, or any combination thereof.
Wherein the memory 20 stores instructions executable by the at least one processor 10 to cause the at least one processor 10 to perform a method for implementing the embodiments described above.
The memory 20 may include a storage program area that may store an operating system, at least one application program required for functions, and a storage data area; the storage data area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, memory 20 may optionally include memory located remotely from processor 10, which may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
Memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk, or solid state disk; the memory 20 may also comprise a combination of the above types of memories.
The computer device also includes a communication interface 30 for the computer device to communicate with other devices or communication networks.
The embodiments of the present invention also provide a computer readable storage medium, and the method according to the embodiments of the present invention described above may be implemented in hardware, firmware, or as a computer code which may be recorded on a storage medium, or as original stored in a remote storage medium or a non-transitory machine readable storage medium downloaded through a network and to be stored in a local storage medium, so that the method described herein may be stored on such software process on a storage medium using a general purpose computer, a special purpose processor, or programmable or special purpose hardware. The storage medium can be a magnetic disk, an optical disk, a read-only memory, a random access memory, a flash memory, a hard disk, a solid state disk or the like; further, the storage medium may also comprise a combination of memories of the kind described above. It will be appreciated that a computer, processor, microprocessor controller or programmable hardware includes a storage element that can store or receive software or computer code that, when accessed and executed by the computer, processor or hardware, implements the methods illustrated by the above embodiments.
Although embodiments of the present invention have been described in connection with the accompanying drawings, various modifications and variations may be made by those skilled in the art without departing from the spirit and scope of the invention, and such modifications and variations fall within the scope of the invention as defined by the appended claims.

Claims (15)

1. A method for micro-grid energy storage configuration, the method comprising:
acquiring the full life cycle cost of an energy storage system in a micro-grid;
acquiring energy storage benefits and wind and light utilization rate of the energy storage system in a preset time period, and determining an objective function based on the energy storage benefits and the wind and light utilization rate;
establishing an energy storage capacity planning model based on the full life cycle cost;
and solving the energy storage capacity planning model based on the objective function and a preset constraint condition set to obtain an energy storage configuration result of the target micro-grid.
2. The method of claim 1, wherein obtaining a full lifecycle cost of the energy storage system in the micro-grid comprises:
acquiring target comprehensive cost and equivalent cycle times of an energy storage system in a micro-grid;
establishing an energy storage life model based on the equivalent cycle times;
and determining the full life cycle cost of the energy storage system based on the energy storage life model and the target comprehensive cost.
3. The method of claim 2, wherein obtaining the target integrated cost and equivalent cycle number for the energy storage system in the micro-grid comprises:
acquiring investment cost, replacement cost, operation and maintenance cost, processing cost, recovery cost and energy storage discharge capacity of the energy storage system;
determining the target composite cost based on the investment cost, the replacement cost, the operation cost, the processing cost, and the recovery cost;
and determining the equivalent cycle times based on the energy storage and discharge quantity.
4. A method according to claim 3, wherein establishing an energy storage life model based on the equivalent number of cycles comprises:
acquiring a first energy storage cycle number of the energy storage system;
determining a second energy storage cycle number based on the energy storage discharge amount and the first energy storage cycle number;
and establishing the energy storage life model based on the second energy storage cycle times and the equivalent cycle times.
5. The method of claim 1, wherein solving the energy storage capacity planning model based on the objective function and a set of preset constraints to obtain a target microgrid energy storage configuration result comprises:
Acquiring the preset constraint condition set;
and solving the energy storage capacity planning model by utilizing an improved simulated annealing algorithm and a main target method based on the objective function and the preset constraint condition set to obtain an energy storage configuration result of the target micro-grid.
6. The method of claim 5, wherein solving the energy storage capacity planning model using a modified simulated annealing algorithm and a primary objective method based on the objective function and the set of preset constraints to obtain the objective microgrid energy storage configuration results comprises:
determining a first target, a second target, and a third target of the energy storage system based on the objective function;
solving the energy storage capacity planning model by utilizing the improved simulated annealing algorithm based on the preset constraint condition set to obtain an initial micro-grid energy storage configuration result meeting the first target;
and solving the energy storage capacity planning model by using the main target method based on the second target, the preset constraint condition set and the initial micro-grid energy storage configuration result to obtain the target micro-grid energy storage configuration result meeting the third target.
7. The method of claim 6, wherein solving the energy storage capacity planning model using the modified simulated annealing algorithm based on the set of preset constraints results in an initial microgrid energy storage configuration that meets the first objective, comprising:
determining a target stored energy output of the energy storage system meeting the set of preset constraints using the modified simulated annealing algorithm;
and solving the energy storage capacity planning model based on the target energy storage output to obtain the initial micro-grid energy storage configuration result meeting the first target.
8. The method of claim 6, wherein solving the energy storage capacity planning model using the primary objective method based on the second objective, the set of preset constraints, and the initial microgrid energy storage configuration result to obtain the microgrid energy storage configuration result satisfying the third objective comprises:
obtaining a target scaling factor;
converting the initial micro-grid energy storage configuration result into a target constraint condition based on the target scaling factor, the second target and the preset constraint condition set;
and solving the energy storage capacity planning model based on the target constraint condition to obtain the target micro-grid energy storage configuration result meeting the third target.
9. The method of claim 5, wherein the set of preset constraints comprises: genset operation constraints, energy storage system constraints, and power system constraints.
10. The method of claim 9, wherein the genset operation constraint comprises: gas turbine operating constraints and renewable energy operating constraints.
11. The method of claim 9, wherein the energy storage system constraints comprise: energy storage operation constraints, energy storage power constraints, and sustained discharge time constraints.
12. The method of claim 9, wherein the power system constraints comprise: power balance constraints, reserve capacity constraints, and renewable energy permeability constraints.
13. A microgrid energy storage configuration device, the device comprising:
the acquisition module is used for acquiring the full life cycle cost of the energy storage system in the micro-grid;
the acquisition and determination module is used for acquiring energy storage benefits and wind and light utilization rate of the energy storage system in a preset time period and determining an objective function based on the energy storage benefits and the wind and light utilization rate;
the building module is used for building an energy storage capacity planning model based on the full life cycle cost;
And the solving module is used for solving the energy storage capacity planning model based on the objective function and a preset constraint condition set to obtain an energy storage configuration result of the target micro-grid.
14. A computer device, comprising:
a memory and a processor, the memory and the processor being communicatively connected to each other, the memory having stored therein computer instructions, the processor executing the computer instructions to perform the microgrid storage configuration method of any one of claims 1 to 12.
15. A computer-readable storage medium, having stored thereon computer instructions for causing a computer to perform the microgrid storage configuration method of any one of claims 1 to 12.
CN202310955042.XA 2023-07-31 2023-07-31 Micro-grid energy storage configuration method and device, computer equipment and storage medium Pending CN116979619A (en)

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