CN115133607A - Method, system, equipment and medium for configuring energy storage capacity of retired battery at user side - Google Patents
Method, system, equipment and medium for configuring energy storage capacity of retired battery at user side Download PDFInfo
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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
- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J7/0047—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries with monitoring or indicating devices or circuits
- H02J7/0048—Detection of remaining charge capacity or state of charge [SOC]
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
- H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
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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
- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J7/0013—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries acting upon several batteries simultaneously or sequentially
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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
- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J7/0029—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries with safety or protection devices or circuits
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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
- H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
- H02J7/007—Regulation of charging or discharging current or voltage
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Abstract
The invention relates to a method, a system, equipment and a medium for configuring the energy storage capacity of a retired battery at a user side, wherein the method comprises the following steps: establishing a double-layer optimization model of the energy storage capacity optimization configuration of the retired battery at the user side considering the degradation characteristics, and determining a target function and constraint conditions of the double-layer optimization model; and solving the established double-layer optimization model considering the degradation characteristic and optimally configured the energy storage capacity of the retired battery at the user side aiming at the given typical operation scene of the power distribution network and the pre-acquired relevant parameters of the power distribution system and the retired battery, and obtaining a configuration scheme of the energy storage capacity of the retired battery at the user side based on a solving result. The invention fully considers the battery degradation characteristic to improve the energy storage economy of the user side, and can be widely applied to the field of optimization configuration of the energy storage capacity of the retired battery of the user side considering the degradation characteristic.
Description
Technical Field
The invention relates to a method, a system, equipment and a medium for configuring the energy storage capacity of a user-side retired battery, in particular to a method, a system, equipment and a medium for optimally configuring the energy storage capacity of the user-side retired battery, which are used for solving a configuration problem and considering the degradation characteristic based on a double-layer optimization framework, and belongs to the technical field of new energy.
Background
At present, the new energy automobile industry develops rapidly. In the expected future, a large number of power batteries will be decommissioned because they do not meet automotive requirements. However, after the power battery is retired, the residual capacity is still more than 80% of the initial capacity. If the power battery is directly scrapped and disassembled, on one hand, the value of the whole life cycle of the power battery is wasted; on the other hand, the environmental pollution is aggravated by improper recycling and disassembling treatment. These problems can be avoided well by the echelon utilization of retired power cells. However, in the using process of the battery, factors such as working temperature, charge-discharge rate and charge-discharge depth have great influence on the capacity and cycle life of the battery, and the difficulty in capacity allocation and economic analysis during the gradient utilization of the retired power battery is increased. How to consider the degradation characteristic of the retired battery and accurately estimate the residual available cycle number and the residual capacity of the retired battery has important significance for the gradient utilization of the retired battery.
In the aspects of battery recycling and battery energy storage capacity optimization configuration, a large number of scholars make intensive research, but documents for utilizing battery energy storage on a user side in a gradient manner and considering battery degradation characteristics are rare. The recovery method of the waste power battery is concluded; the value evaluation of each link is performed by analyzing the ex-service battery echelon utilization technology and the disassembly and recovery technology; the method comprises the steps of providing a micro-grid energy storage capacity double-layer optimization model considering system economic operation and battery life; … that a capacity optimization model considering energy storage cost and service life is established with the aim of minimum cost, there are documents in general that focus on battery recycling technology or do not consider the condition of a retired battery in the research of an energy storage capacity optimization configuration method.
Disclosure of Invention
In view of the above problems, an object of the present invention is to provide a method, a system, a device, and a medium for configuring the energy storage capacity of a retired battery on a user side, wherein the configured capacity and the configured power of the energy storage capacity of the retired battery on the user side are planned based on the degradation characteristic of the retired battery, so as to obtain an optimal scheme for optimizing the energy storage capacity of the retired battery on the user side considering the degradation characteristic.
In order to achieve the purpose, the invention adopts the following technical scheme:
in a first aspect, the present invention provides a method for configuring energy storage capacity of a retired battery on a user side, which includes the following steps:
establishing a double-layer optimization model of the energy storage capacity optimization configuration of the retired battery at the user side in consideration of the degradation characteristic, and determining a target function and constraint conditions of the double-layer optimization model;
and solving the established double-layer optimization model of the energy storage capacity optimization configuration of the retired battery at the user side considering the degradation characteristics according to a given typical operation scene of the power distribution network and the pre-acquired relevant parameters of the power distribution system and the retired battery, and obtaining a configuration scheme of the energy storage capacity of the retired battery at the user side based on a solving result.
Further, the pre-acquired relevant parameters of the power distribution system include:
wherein, the power distribution system parameter includes: unit investment cost of power grid upgrading, annual interest rate, project discount rate, project operation year limit, energy storage working time per year, energy storage system recovery cost, processing recombination cost, energy conversion part cost, unit cost of retired battery processing recombination, rated capacity and maximum discharge power of energy storage installed at each node, upper and lower limits of capacity and maximum discharge power of energy storage installed at each node, annual operation maintenance cost of unit power energy storage, total number of power grid nodes, real-time electricity price, energy storage working efficiency, operating voltage of each node, maximum and minimum operating voltage values of each node, current of each branch, maximum allowable current of each branch, resistance and reactance of each branch, active power and reactive power flowing through each branch at each moment, active power and reactive power injected into each node at each moment, active power and reactive power injected into distributed power supply at each node at each moment, Active power and reactive power injected by the energy storage system on each node at each moment, active power and reactive power consumed by loads on each node at each moment, a charge state at the initial time of energy storage, upper and lower limits of the charge state of the energy storage, a simulation step length, backward transmission electric power of a user transformer and an upper limit initial value of the backward transmission electric power;
the retired battery parameters include: the method comprises the following steps of activating energy of a battery at the current temperature, working temperature of the battery, charging and discharging multiplying power, charging and discharging depth, capacity of a single battery, used cycle times of the battery, used charging and discharging times of a retired battery as a power battery, and working voltage of battery packs of all nodes after series connection.
Further, the method for establishing a double-layer optimization model of the energy storage capacity optimization configuration of the retired battery at the user side considering the degradation characteristics and determining the objective function and the constraint conditions of the double-layer optimization model comprises the following steps:
determining a user side retired battery energy storage capacity optimization configuration double-layer optimization model considering the degradation characteristics, wherein the user side retired battery energy storage capacity optimization configuration double-layer optimization model comprises an upper-layer planning optimization model and a lower-layer operation optimization model; the upper-layer planning optimization model is used for making an energy storage configuration scheme, the lower-layer operation optimization model is used for making an optimal operation control strategy and feeding back an operation result to the upper-layer planning optimization model;
determining an objective function of an upper-layer planning optimization model and a lower-layer operation optimization model in a double-layer optimization model;
and determining constraint conditions of objective functions of the upper-layer planning optimization model and the lower-layer operation optimization model.
Further, the objective functions of the upper-layer planning optimization model and the lower-layer operation optimization model are respectively as follows:
in the formula, F is an objective function of an upper-layer planning optimization model; f is an objective function of the lower-layer operation optimization model; n is ES Representing energy storage annual operating time;
the capacity decline coefficient of the retired battery in the y year; y is the project operation age; theta is the item discount rate; v is the energy storage of the user side to delay the investment income;
for the total cost of the energy storage system of the retired battery, the energy storage system is recombined according to recovery and processingThe way of (2) is calculated; m is the total number of the nodes of the power grid; c dd (t) real-time electricity prices; eta is the energy storage working efficiency;
active power injected into the energy storage system at a node i at the time t;
the constraint conditions of the objective functions of the upper-layer planning optimization model and the lower-layer operation optimization model comprise: the method comprises the steps of energy storage installation capacity, power constraint, power distribution network safety constraint, power distribution network power flow constraint, energy storage operation constraint and reverse power supply constraint.
Further, when the established user-side retired battery energy storage capacity optimization configuration model considering the degradation characteristics is solved according to a given power distribution network typical operation scene and pre-acquired relevant parameters of a power distribution system, a mixed algorithm combining second-order cone planning and simulated annealing is adopted.
Further, the method for solving the established user side retired battery energy storage capacity optimization configuration model considering the degradation characteristics by adopting a hybrid algorithm combining second-order cone programming and simulated annealing comprises the following steps of:
solving a user side retired battery energy storage capacity optimization configuration model considering the degradation characteristics into an upper-layer planning optimization model and a lower-layer operation optimization model;
solving an upper-layer planning optimization model by adopting a simulated annealing algorithm, and setting initial temperature, temperature reduction coefficients and iteration limit value parameters;
carrying out integer coding on the energy storage capacity of the retired power battery at the user side, giving an initial energy storage capacity optimization configuration scheme, wherein the scheme comprises the rated capacity of the energy storage installed at a node i
Maximum discharge power of stored energy installed at node i
And setting the iteration times to zero;
changing the rated capacity configuration and the maximum discharge power configuration of the installed battery energy storage capacity to obtain a new configuration scheme and transmitting the new configuration scheme to the lower-layer operation optimization model;
the lower-layer operation optimization model is used for optimizing and configuring the energy storage capacity of a user-side retired battery obtained from the upper layer according to a given typical operation scene of the power distribution network and by combining a scheme for optimizing and configuring the energy storage capacity of the user-side retired battery with the current consideration of the degradation characteristics, a second-order cone planning algorithm is adopted for carrying out optimization calculation to obtain an energy storage system profit index and an energy storage system operation control strategy, and the corresponding index and the control strategy are returned to the upper-layer planning optimization model to form a target function;
setting the iteration times plus one, judging whether the maximum iteration times at the current temperature is reached, if so, jumping out of the iteration process, and selecting a configuration scheme at the current temperature as a final solution; otherwise, returning to the step IV to regenerate the configuration scheme until the final optimized configuration scheme of the energy storage capacity of the retired battery at the user side considering the degradation characteristic is output.
In a second aspect, the present invention provides a system for configuring energy storage capacity of a retired battery on a user side, where the system includes:
the model building module is used for building a double-layer optimization model of the energy storage capacity optimization configuration of the retired battery at the user side considering the degradation characteristic and determining a target function and a constraint condition of the model;
and the model calculation module is used for solving the established double-layer optimization model considering the degradation characteristic and optimally configured the energy storage capacity of the retired battery at the user side according to a given typical operation scene of the power distribution network and the pre-acquired related parameters of the power distribution system and the retired battery, and obtaining a configuration scheme of the energy storage capacity of the retired battery at the user side based on a solving result.
Further, the model building module comprises: the double-layer optimization model determination module is used for determining a user side retired battery energy storage capacity optimization configuration double-layer optimization model considering the degeneration characteristics, and comprises an upper-layer planning optimization model and a lower-layer operation optimization model; the upper-layer planning optimization model is used for making an energy storage configuration scheme, the lower-layer operation optimization model is used for making an optimal operation control strategy and feeding back an operation result to the upper-layer planning optimization model; the target function determining module is used for determining target functions of an upper-layer planning optimization model and a lower-layer operation optimization model in the double-layer optimization model; and the constraint condition determining module is used for determining the constraint conditions of the objective functions of the upper-layer planning optimization model and the lower-layer operation optimization model.
In a third aspect, the present invention provides a processing device, where the processing device at least includes a processor and a memory, where the memory stores a computer program, and the processor executes the computer program when executing the computer program to implement the steps of the method for configuring the energy storage capacity of the retired battery on the user side.
In a fourth aspect, the present invention provides a computer storage medium having computer readable instructions stored thereon, the computer readable instructions being executable by a processor to implement the steps of the user-side retired battery energy storage capacity configuration method.
Due to the adoption of the technical scheme, the invention has the following advantages:
1. according to the user side retired battery energy storage capacity optimization configuration model considering the degradation characteristics, the degradation characteristics and the flexible control strategy of energy storage are considered in the retired battery energy storage capacity optimization configuration stage, so that the user side retired battery energy storage capacity optimization configuration scheme considering the degradation characteristics is obtained, the operation strategy of high charging and low discharging of energy storage is fully utilized, the electricity purchasing requirement of a superior power grid is transferred in time, and therefore the overall consumption of power users is reduced;
2. when the optimization configuration model of the energy storage capacity of the retired battery at the user side considering the degradation characteristics is solved, a mixed algorithm combining second-order cone programming and simulated annealing is adopted, so that the solution is quicker and higher in accuracy.
Therefore, the method can be widely applied to the technical field of new energy, in particular to the field of optimal configuration of the energy storage capacity of the retired battery on the user side considering the degradation characteristic.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Like reference numerals refer to like parts throughout the drawings. In the drawings:
fig. 1 is a flowchart of a method for optimally configuring energy storage capacity of a retired battery on a user side in consideration of a degradation characteristic according to an embodiment of the present invention;
FIG. 2 is a diagram of a two-tier optimization framework in an embodiment of the present invention;
FIG. 3 is a diagram of a typical case consumer grid architecture in an embodiment of the present invention;
FIG. 4 is a typical daily distributed power and load power plot (units/kW) in an embodiment of the present invention;
FIGS. 5a and 5b are diagrams of model verification in an embodiment of the present invention;
FIG. 6 is a graph of the change in energy storage capacity of a battery in an embodiment of the invention;
FIG. 7 is a graph of capacity utilization for various aspects in an embodiment of the invention;
FIGS. 8a to 8d are graphs illustrating the variation of the energy storage SOC under different configurations in the embodiment of the present invention; wherein, FIG. 8a is a graph of the change of SOC according to the method of the present invention; FIG. 8b is a graph showing the SOC variation of comparative scheme 1; FIG. 8c is a SOC variation graph of comparative scheme 2; FIG. 8d is a graph showing the SOC variation of comparative scheme 3;
figure 9 is a cost-effective diagram of various aspects in an embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the drawings of the embodiments of the present invention. It is to be understood that the embodiments described are only a few embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the description of the embodiments of the invention given above, are within the scope of protection of the invention.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
Some embodiments of the present invention provide a method for configuring energy storage capacity of a retired battery on a user side, including: establishing a double-layer optimization model of the energy storage capacity optimization configuration of the retired battery at the user side in consideration of the degradation characteristic, and determining a target function and constraint conditions of the double-layer optimization model; and solving the established double-layer optimization model considering the degradation characteristic and optimally configured the energy storage capacity of the retired battery at the user side aiming at the given typical operation scene of the power distribution network and the pre-acquired relevant parameters of the power distribution system and the retired battery, and obtaining a configuration scheme of the energy storage capacity of the retired battery at the user side based on a solving result. According to the energy storage capacity optimization configuration model of the retired battery at the user side, which is established by considering the degradation characteristic, the degradation characteristic of the battery and the flexible control strategy of energy storage are considered in the energy storage capacity optimization configuration stage of the retired battery, so that the energy storage capacity optimization configuration scheme of the retired battery at the user side, which is considered in the degradation characteristic, is obtained, the operation strategy of high charging and low discharging of the energy storage is fully utilized, the electricity purchasing requirement of a superior power grid is transferred in time, and the overall consumption of power users is reduced.
In accordance with other embodiments of the present invention, there are provided a system, apparatus and medium for configuring the energy storage capacity of a user-side retired battery.
Example 1
As shown in fig. 1, the method for configuring the energy storage capacity of a retired battery on a user side according to this embodiment includes the following steps:
1) and collecting relevant parameters of the power distribution system and the retired battery, and calculating the energy storage capacity optimization configuration scheme of the retired battery at the user side by considering the degradation characteristic.
Wherein, the distribution system parameter of gathering includes: unit investment cost of power grid upgrading, annual interest rate, project discount rate, project operation year limit, energy storage working time per year, energy storage system recovery cost, processing recombination cost, energy conversion part cost, unit cost of retired battery processing recombination, rated capacity and maximum discharge power of energy storage installed at each node, upper and lower limits of capacity and maximum discharge power of energy storage installed at each node, annual operation maintenance cost of unit power energy storage, total number of power grid nodes, real-time electricity price, energy storage working efficiency, operating voltage of each node, maximum and minimum operating voltage values of each node, current of each branch, maximum allowable current of each branch, resistance and reactance of each branch, active power and reactive power flowing through each branch at each moment, active power and reactive power injected into each node at each moment, active power and reactive power injected into distributed power supply at each node at each moment, Active power and reactive power injected by the energy storage system on each node at each moment, active power and reactive power consumed by a load on each node at each moment, a charge state at the initial time of energy storage, an upper limit and a lower limit of the charge state of the energy storage, a simulation step length, backward electric power transmission of a user transformer and an upper limit initial value of the backward electric power transmission;
the retired battery parameters include: the activation energy of the battery at the current temperature, the working temperature of the battery, the charge-discharge rate, the charge-discharge depth, the capacity of the single battery, the used cycle times of the battery, the used charge-discharge times of the retired battery as a power battery, the working voltage of the battery pack of each node after series connection and the like.
2) Establishing a double-layer optimization model of the energy storage capacity optimization configuration of the retired battery at the user side considering the degradation characteristic, and determining an objective function and a constraint condition of the double-layer optimization model.
Specifically, the step 2) may be implemented by:
2.1) determining a user side retired battery energy storage capacity optimization configuration double-layer optimization model considering the degradation characteristic.
As shown in fig. 2, in this embodiment, the user-side retired battery energy storage capacity optimization configuration double-layer optimization model considering the degradation characteristic includes an upper-layer planning optimization model and a lower-layer operation optimization model. The upper-layer planning optimization model is used for making an energy storage configuration scheme (decision variable), namely configuration capacity (battery capacity and maximum charge and discharge power), and the lower-layer operation optimization model is used for making an optimal operation control strategy (decision variable) and feeding back an operation result to the upper-layer planning optimization model. And calculating to obtain a user comprehensive benefit optimization index based on the capacity fading characteristic by combining the determined optimization configuration scheme of the energy storage capacity of the retired battery at the user side considering the fading characteristic.
Specifically, for the present embodiment, the user-side retired battery energy storage capacity optimization configuration double-layer optimization model considering the degradation characteristic may be represented as:
in the formula, F is an objective function of an upper-layer planning optimization model; G. h is a constraint condition of the upper-layer planning optimization model;
the method is not only a decision variable of an upper-layer planning optimization model, but also a boundary condition of a lower-layer operation optimization model; delta is not only a boundary condition of the upper-layer planning optimization model, but also an objective function of the lower-layer operation optimization model; f is an objective function of the lower-layer operation optimization model and represents the daily operation income of energy storage of the retired battery; g. h is a constraint condition of a lower-layer operation optimization model;
is a decision variable of the lower run optimization model.
2.2) determining the objective functions of an upper-layer planning optimization model and a lower-layer operation optimization model in the double-layer optimization model.
Firstly, the battery capacity decline rate index epsilon loss Expressed as:
in the formula, epsilon loss The battery capacity degradation rate under the influence of various factors; e battery Is the capacity of the single battery; epsilon Rate Is the charge-discharge multiplying power; r is a molar gas coefficient; t is the working temperature of the battery; DOD is the charge-discharge depth; n is a radical of EV The number of used cycles of the battery; alpha, beta, gamma, k 1 、k 2 And z is a undetermined constant.
Therefore, the objective function F of the upper-level planning optimization model is represented as:
in the embodiment, the residual capacity of the battery in the retired state is used as the rated capacity of the battery after the battery is rebuilt in a gradient manner. According to equation (3), the coefficient of the fading rate is a function of the number of uses. Considering that a certain capacity attenuation exists when the battery is retired and the energy storage charging and discharging strategy of a user is stable, the using times of the battery and the running time (year) are approximately in a linear relation. Therefore, the coefficient of the fading rate can be expressed as a function of the running time, and is obtained by correcting the formula (3):
in the formula, V is the energy storage of the user side to delay the investment income;
calculating the total cost of the retired battery energy storage system according to the modes of recovery, processing and recombination;
the capacity fading coefficient of the retired battery in the y year is used for correcting the capacity of the battery used in the echelon; r rest Unit investment cost for power grid upgrade; ρ is the annual percentage, and in this embodiment, ρ is 15%; θ is the item discount rate, which is 8% in this example; y is the project operation age; n is ES Representing energy storage annual operating time; c hs Recovering cost for the energy storage system; c JG Processing and recombining cost for the energy storage system; c PCS Part of the cost is converted for energy of the energy storage system;
recovering unit cost for the energy storage system;
the unit cost of the processing and recombination of the retired battery;
part of the cost is converted for energy of the energy storage system;
annual operating maintenance costs for unit power energy storage; n is a radical of EV The number of cycles used for the battery;
rated capacity for stored energy installed at node i;
maximum discharge power for stored energy installed at node i；
Initial investment cost for the energy storage system; c om The annual operating and maintaining cost of the energy storage system is saved;
to cycle N EV Capacity fade coefficient of secondary back-service battery.
The objective function f of the lower run optimization model is expressed as:
in the formula, M is the total number of the nodes of the power grid; c dd (t) real-time electricity prices; eta is the energy storage working efficiency;
and injecting active power into the energy storage system at the node i at the moment t.
2.3) determining the constraint conditions of the objective functions of the upper-layer planning optimization model and the lower-layer operation optimization model.
For this embodiment, the constraint conditions include energy storage installation capacity, power constraint, power distribution network safety constraint, power distribution network power flow constraint, energy storage operation constraint, and reverse power transmission power constraint:
2.3.1) energy storage installation Capacity, Power constraints
The energy storage installation capacity, power constraint can be expressed as:
in the formula (I), the compound is shown in the specification,
rated capacity and maximum discharge power of the stored energy installed at node i, respectively; e max,i 、P max,i 、E min,i 、P min,i Respectively, the rated capacity of the stored energy installed at node i and the upper and lower limits of the maximum discharge power.
2.3.2) Power distribution network safety constraints
In the formula of U i Is the operating voltage of node i;
the maximum and minimum operating voltage values of the node i are respectively; i is ij Current for branch ij;
the maximum allowed current for branch ij.
2.3.3) Power flow constraints for distribution networks
In the formula, P t,ik The active power flowing to the node k for the node i on the branch at the moment t; q t,ik The reactive power of the node i on the branch at the time t flowing to the node k; r is ji 、x ji Resistance and reactance for branch ij; p is t,ji 、Q t,ji Respectively the active power and the reactive power flowing through the branch ij at the moment t; p t,i 、Q t,i Respectively the active power and the reactive power injected into the node i at the time t;
respectively the active power and the reactive power injected by the distributed power supply on the node i at the time t;
respectively the active power and the reactive power injected by the energy storage system at the node i at the time t;
respectively the active power and the reactive power consumed by the load on the node i at the moment t; omega b The method comprises the steps of (1) collecting all branches of a power distribution system; i is t,ij Is the current of branch ij at time t; u shape t,i The voltage amplitude of the node i at time t; u shape t,j The voltage magnitude at node j at time t.
2.3.4) energy storage operation constraints
In the formula (I), the compound is shown in the specification,
for the maximum discharge power of the stored energy installed at node i,
active power injected into the energy storage system at a node i at the time t;
is the state of charge at the beginning of energy storage;
the upper limit and the lower limit of the energy storage charge state; delta t is a simulation step length; v battery,i The working voltage of the battery pack which is the node i after being connected in series;
the capacity fading coefficient of the retired battery after recombination is used for correcting the capacity of the battery used in the echelon; i is t,i The current at the node i at the time t, k is the charge-discharge rate,
The capacity of the retired battery capacity degradation is taken into account for the energy storage installed at node i.
2.3.5) reverse supply Power constraints
In the formula (I), the compound is shown in the specification,
the power is fed back to the user transformer,
is the upper limit of the reverse electric power.
3) And solving the established double-layer optimization model considering the degradation characteristic for optimizing and configuring the energy storage capacity of the retired battery at the user side according to the given typical operation scene of the power distribution network and the related parameters of the power distribution system, and obtaining a configuration scheme of the energy storage capacity of the retired battery at the user side based on a solving result, wherein the configuration scheme comprises the battery capacity degradation rate, the capacity configuration and power configuration scheme of the energy storage battery, the capacity utilization rate, the annual average profit index of the energy storage and the comprehensive benefit index of the user.
For this embodiment, when solving the established optimal configuration model of the energy storage capacity of the retired battery on the user side considering the degradation characteristics, a hybrid algorithm combining second-order cone planning and simulated annealing is adopted, which specifically includes the following steps:
3.1) solving the optimal configuration model of the energy storage capacity of the retired battery at the user side considering the degradation characteristic into two stages, namely an upper-layer planning optimization model and a lower-layer operation optimization model;
3.2) solving an upper-layer planning optimization model by adopting a simulated annealing algorithm, and setting algorithm parameters such as initial temperature, temperature drop coefficient, iteration time limit value and the like;
3.3) integer coding the energy storage capacity of the retired battery at the user side, and giving an initial energy storage capacity optimization configuration scheme, namely the rated capacity of the energy storage installed at the node i
Maximum discharge power of stored energy installed at node i
And setting the iteration times to zero;
3.4) updating the rated capacity configuration and the maximum discharge power configuration of the energy storage capacity of the retired battery at the user side to obtain a new configuration scheme and transmitting the new configuration scheme to a lower-layer operation optimization model;
3.5) the lower-layer operation optimization model combines the energy storage capacity optimization configuration scheme of the currently-considered-decline-characteristic user-side retired battery obtained from the upper-layer planning optimization model aiming at the given typical operation scene of the power distribution network, adopts a second-order cone planning algorithm to perform optimization calculation to obtain an energy storage system profit index and an energy storage system operation control strategy, and returns the corresponding index and the control strategy to the upper-layer planning optimization model to form a target function;
3.6) setting the iteration times plus one, judging whether the maximum iteration times at the current temperature is reached, if so, jumping out of the iteration process, and selecting the configuration scheme at the current temperature as a final solution; otherwise, returning to the step 3.4) to regenerate the configuration scheme until the final optimized configuration scheme of the energy storage capacity of the retired battery at the user side considering the degradation characteristic is output.
Example 2
In this embodiment, a load user equipped with a small distributed power supply is selected. The user power grid is simple in structure, user loads and the distributed power sources are connected to the same bus, and the reverse power supply is allowed to be 100 kW. In this context, users want to install a certain amount of energy storage to obtain the maximum economic benefit. In the example, a power grid structure diagram, a typical daily distributed power supply and load power are shown in fig. 3-4; the transformer parameters, typical daily electricity rate tables, user requirements and related information are shown in tables 1-3.
For the present embodiment, the decay characteristic of the model is first fitted and verified in combination with the typical capacity change of the lithium iron phosphate battery. The usage of a battery of a certain type is shown in table 4, and the corresponding capacity deterioration is shown in table 5. According to the above conditions, the fitting result of the undetermined coefficient of the battery capacity decline is shown in table 6. And (4) verifying and analyzing the retired battery which is the same as the lithium iron phosphate battery. The battery capacity degradation under different test conditions is shown in table 7, and the model verification results are shown in fig. 5a and 5 b. The battery energy storage capacity variation graph is shown in fig. 6.
For the embodiment, it is assumed that all 5000 cycles of the battery are used for power energy storage, and the energy storage operation age is calculated to be 15 years by combining the energy storage working time bar; the residual using times of the battery when the capacity of the battery is used to 80 percent of the retired battery is 2709 times, and the energy storage operating life is calculated to be 8 years in the same way. The energy storage adopts a use strategy of complete charge-discharge cycle for 1 time (from a full state of the energy storage, charged discharge to no power, and full charge again) every day, and the energy storage is set to operate within the range of SOC (state of charge) of more than or equal to 0.3 and less than or equal to 0.9. The configuration model and the method of the embodiment are adopted for calculation to obtain a configuration scheme. Meanwhile, a configuration scheme is given in which the same type of new battery is used without considering the degradation characteristics, as shown in table 8.
For the present embodiment, in order to analyze the optimality of the configuration scheme, the energy storage configuration schemes of different capacities of the battery are given as shown in table 9, and the analysis is completed from the aspects of capacity utilization rate, economic benefit, and the like. The capacity utilization rate of different schemes is shown in fig. 7, the energy storage SOC variation curves of different configuration schemes are shown in fig. 8a to 8d, and the cost effectiveness of different schemes is shown in fig. 9.
The computer hardware environment for executing the optimized calculation is Intel (R) Xeon (R) CPU E5-2609, the dominant frequency is 2.50GHz, and the memory is 16 GB; the software environment is a Windows 10 operating system.
The result shows that the theoretical model obtained by fitting the battery degradation characteristic and the actual use data have better fitting degree; when the energy is stored in the retired battery or the new battery is configured, the degradation characteristic has no influence on the configuration scheme. However, when the fading characteristic is considered, the annual average benefit of the energy storage of the retired battery is 4.9 ten thousand yuan more than that of the new battery, which shows that the economic performance of the energy storage of the retired battery is better than that of the new battery, namely, the fading characteristic has non-negligible influence on the economic performance of energy storage analysis when the user configures the energy storage; compared with the scheme 1, the energy storage operation strategy is complex; compared with the scheme 2, the charging and discharging are still required for multiple times, and the use frequency and the utilization rate are not as good as those of the scheme in the embodiment; in contrast to scheme 3, a part of additional batteries are added on the basis of the configuration scheme of the embodiment, but the batteries cannot be utilized in the charging and discharging process, so that investment is wasted, and the energy storage utilization rate is reduced. Therefore, the energy storage capacity in the configuration scheme of the embodiment can meet the peak-valley arbitrage requirement; although the initial investment costs of energy storage of the comparison scheme 1 and the comparison scheme 2 are respectively 2.2 ten thousand yuan and 5.3 ten thousand yuan less than that of the configuration scheme of the embodiment, the profit-and-profit-loss between peaks and valleys is respectively 17.2 ten thousand yuan and 18.3 ten thousand yuan less, so that the comprehensive cost benefits are respectively 15 ten thousand yuan and 13 ten thousand yuan less. The peak-valley arbitrage benefits of the comparison scheme 3 and the configuration scheme of the embodiment are 15 ten thousand yuan, but the energy storage capacity of the comparison scheme 3 is partially configured, so that the energy storage investment total cost of the comparison scheme 3 is 5.3 ten thousand yuan more than that of the configuration scheme of the embodiment, and the total benefit of a user is 5.3 ten thousand yuan less; in summary, the configuration scheme of the embodiment obtained by the optimization model of the method achieves the optimal energy storage capacity utilization rate while the operation strategy is simple, and achieves the operation target of the maximum economic benefit of the user under the user requirement and relevant constraints, thereby completing the optimal energy storage configuration. Therefore, the configuration model and the method provided by the embodiment can provide guidance for installing energy storage at the user side.
TABLE 1 Transformer and connecting line parameters
TABLE 2 typical daily electricity rate table
TABLE 3 user side demand and retired Battery cost sheet
| Multiplying factor of charge and discharge | 0.25C |
| Depth of charge and discharge | 90％ |
| Working time per year | Charging and discharging for 4h every day for 330 days |
| Charge and discharge efficiency | 90％ |
| Capacity of monomer | 10Ah |
| Recovery cost | 0.1 yuan/Wh |
| Process recombination cost | 0.24 yuan/Wh |
| Cost of acquisition of new battery | 0.95 yuan/Wh |
| PCS cost | 1 yuan/W |
| Construction and maintenance costs | 1.3 yuan/W |
TABLE 4 Battery usage and parameters
TABLE 5 Battery degradation
| Number of cycles | Rate of capacity fade | Number of cycles | Rate of capacity fade |
| 20 times of | 2.35％ | 100 times (twice) | 5.1％ |
| 40 times (twice) | 3.1％ | 120 times of | 6.01％ |
| 60 times | 4.22％ | 140 times (one time) | 6.55％ |
| 80 times (twice) | 4.55％ | 160 times (times) | 7.1％ |
TABLE 6 fitting results of coefficient to be determined
| Parameter(s) | Numerical value | Parameter(s) | Numerical value |
| α | 0.4575 | z | 0.5556 |
| β | -2.96 | k 1 | 9.257 |
| γ | -2.881 | k 2 | -0.8797 |
TABLE 7 degradation of capacity of certain lithium iron phosphate power battery
(a)55℃-75％DOD-2C
(b)55℃-55％DOD-3C
TABLE 8 energy storage configuration scheme
TABLE 9 energy storage scheme
| Type of scenario | Configuration scheme | Annual average benefit |
| Scheme of the embodiment | 1258kWh/188.7kW | 8 ten thousand yuan |
| |
629kWh/188.7kW | 6.1 ten thousand yuan |
| |
1101kWh/188.7kW | 6.4 ten thousand yuan |
| |
1416kWh/188.7kW | 7.3 ten thousand yuan |
Example 3
The foregoing embodiment 1 provides a method for configuring energy storage capacity of a retired battery on a user side, and correspondingly, this embodiment provides a system for configuring energy storage capacity of a retired battery on a user side. The configuration system provided in this embodiment may implement the energy storage capacity optimization configuration method for the retired battery on the user side in embodiment 1, and the configuration system may be implemented by software, hardware, or a combination of software and hardware. For example, the system may comprise integrated or separate functional modules or functional units to perform the corresponding steps in the methods of embodiment 1. Since the configuration system of the present embodiment is basically similar to the method embodiment, the description process of the present embodiment is relatively simple, and reference may be made to part of the description of embodiment 1 for relevant points.
The present embodiment provides a system for configuring energy storage capacity of a retired battery on a user side in consideration of degradation characteristics, which includes: the parameter acquisition module is used for acquiring relevant parameters of the power distribution system and the retired battery; the model building module is used for building a double-layer optimization model of the energy storage capacity optimization configuration of the retired battery at the user side considering the degradation characteristic and determining a target function and a constraint condition of the model; and the model calculation module is used for solving the established double-layer optimization model considering the optimal configuration of the energy storage capacity of the retired battery at the user side according to the given typical operation scene of the power distribution network and the related parameters of the power distribution system, and obtaining a configuration scheme of the energy storage capacity of the retired battery at the user side based on a solving result.
Further, the optimal configuration model of the energy storage capacity of the retired battery at the user side considering the degradation characteristics is an objective function with the maximum comprehensive optimization index of the user benefit, and the constraint conditions of the objective function include: the method comprises the steps of energy storage installation capacity, power constraint, power distribution network safety constraint, power distribution network power flow constraint, energy storage operation constraint and reverse power supply power constraint.
Further, the model calculation module includes: the problem dividing module is used for dividing the solution of a user side retired battery energy storage capacity optimization configuration model considering the degradation characteristics into two stages, namely an upper-layer planning optimization model and a lower-layer operation optimization model, wherein the upper-layer planning optimization model is used for determining a user side retired battery energy storage capacity optimization configuration scheme considering the degradation characteristics, and the lower-layer operation optimization model is used for calculating a user comprehensive benefit optimization index based on the capacity degradation characteristics according to a given power distribution network typical operation scene and in combination with the determined user side retired battery energy storage capacity optimization configuration scheme considering the degradation characteristics; a parameter setting module for solving the upper layer by using simulated annealing algorithmPlanning an optimization model, and setting algorithm parameters of initial temperature, temperature drop coefficient and iteration limit value; an initial configuration scheme determining module, configured to perform integer coding on the energy storage capacity of the power battery retired at the user side, and give an initial energy storage capacity optimal configuration scheme, that is, the rated capacity of the energy storage installed at the node i
Maximum discharge power of stored energy installed at node i
And setting the iteration times to zero; the configuration scheme updating module is used for changing the rated capacity configuration and the discharge power configuration of the installed energy storage to obtain a new configuration scheme and transmitting the new configuration scheme to the lower-layer operation optimization model; the lower-layer operation problem solving module is used for combining a user-side retired battery energy storage capacity optimization configuration scheme which is obtained from an upper layer and currently considers the degeneration characteristics to obtain an energy storage system profit index and an energy storage system operation control strategy by adopting a second-order cone programming algorithm for optimization calculation aiming at a given power distribution network typical operation scene, and returning the corresponding index and the control strategy to the upper layer to form a main objective function; the configuration scheme output module is used for setting the iteration times plus one, judging whether the maximum iteration times at the current temperature is reached, jumping out of the iteration process if the maximum iteration times at the current temperature is reached, and selecting the configuration scheme at the current temperature as a final solution; and otherwise, regenerating the configuration scheme until the final user-side retired battery energy storage capacity optimization configuration scheme considering the degradation characteristic is output.
Example 4
This embodiment provides a processing device corresponding to the method for configuring energy storage capacity of a retired battery on a user side provided in embodiment 1, where the processing device may be a processing device for a client, such as a mobile phone, a notebook computer, a tablet computer, a desktop computer, and the like, to execute the method of embodiment 1.
The processing equipment comprises a processor, a memory, a communication interface and a bus, wherein the processor, the memory and the communication interface are connected through the bus so as to complete mutual communication. The memory stores a computer program that can be executed on the processor, and the processor executes the method for optimally configuring the energy storage capacity of the retired battery on the user side provided in this embodiment 1 when executing the computer program.
In some implementations, the Memory may be a high-speed Random Access Memory (RAM), and may also include a non-volatile Memory, such as at least one disk Memory.
In other implementations, the processor may be various general-purpose processors such as a Central Processing Unit (CPU), a Digital Signal Processor (DSP), and the like, and is not limited herein.
Example 5
The method for configuring the energy storage capacity of the retired battery on the user side according to this embodiment 1 may be embodied as a computer program product, and the computer program product may include a computer readable storage medium on which computer readable program instructions for executing the method for configuring the energy storage capacity of the retired battery on the user side according to this embodiment 1 are loaded.
The computer readable storage medium may be a tangible device that retains and stores instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any combination of the foregoing.
It should be noted that the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. Each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s).
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention has been described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: although the present invention has been described in detail with reference to the above embodiments, it should be understood by those skilled in the art that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
The above embodiments are only used for illustrating the present invention, and the structure, connection mode, manufacturing process, etc. of the components may be changed, and all equivalent changes and modifications performed on the basis of the technical solution of the present invention should not be excluded from the protection scope of the present invention.
Claims (10)
1. A method for configuring the energy storage capacity of a retired battery at a user side is characterized by comprising the following steps:
establishing a double-layer optimization model of the energy storage capacity optimization configuration of the retired battery at the user side in consideration of the degradation characteristic, and determining a target function and constraint conditions of the double-layer optimization model;
and solving the established double-layer optimization model considering the degradation characteristic and optimally configured the energy storage capacity of the retired battery at the user side aiming at the given typical operation scene of the power distribution network and the pre-acquired relevant parameters of the power distribution system and the retired battery, and obtaining a configuration scheme of the energy storage capacity of the retired battery at the user side based on a solving result.
2. The method according to claim 1, wherein the method comprises the following steps: the pre-acquired relevant parameters of the power distribution system comprise:
wherein, the power distribution system parameter includes: unit investment cost of power grid upgrading, annual interest rate, project discount rate, project operation year limit, energy storage working time per year, energy storage system recovery cost, processing recombination cost, energy conversion part cost, unit cost of retired battery processing recombination, rated capacity and maximum discharge power of energy storage installed at each node, upper and lower limits of capacity and maximum discharge power of energy storage installed at each node, annual operation maintenance cost of unit power energy storage, total number of power grid nodes, real-time electricity price, energy storage working efficiency, operating voltage of each node, maximum and minimum operating voltage values of each node, current of each branch, maximum allowable current of each branch, resistance and reactance of each branch, active power and reactive power flowing through each branch at each moment, active power and reactive power injected into each node at each moment, active power and reactive power injected into distributed power supply at each node at each moment, Active power and reactive power injected by the energy storage system on each node at each moment, active power and reactive power consumed by loads on each node at each moment, a charge state at the initial time of energy storage, an upper limit and a lower limit of the charge state of the energy storage, a simulation step length, backward electric power transmission of a user transformer and an upper limit initial value of the backward electric power transmission;
the retired battery parameters include: the method comprises the following steps of activation energy of a battery at the current temperature, working temperature of the battery, charging and discharging multiplying power, charging and discharging depth, single battery capacity, used cycle times of the battery, used charging and discharging times of a retired battery as a power battery, and working voltage of battery packs of all nodes after series connection.
3. The method according to claim 1, wherein the method comprises the following steps: the method for establishing the double-layer optimization model of the energy storage capacity optimization configuration of the retired battery at the user side considering the degradation characteristic and determining the objective function and the constraint condition of the double-layer optimization model comprises the following steps:
determining a user side retired battery energy storage capacity optimization configuration double-layer optimization model considering the degradation characteristics, wherein the user side retired battery energy storage capacity optimization configuration double-layer optimization model comprises an upper-layer planning optimization model and a lower-layer operation optimization model; the upper-layer planning optimization model is used for making an energy storage configuration scheme, the lower-layer operation optimization model is used for making an optimal operation control strategy and feeding back an operation result to the upper-layer planning optimization model;
determining an objective function of an upper-layer planning optimization model and a lower-layer operation optimization model in a double-layer optimization model;
and determining constraint conditions of objective functions of the upper-layer planning optimization model and the lower-layer operation optimization model.
4. The method according to claim 3, wherein the method comprises the following steps: the target functions of the upper-layer planning optimization model and the lower-layer operation optimization model are respectively as follows:
in the formula, F is an objective function of an upper-layer planning optimization model; f is an objective function of the lower-layer operation optimization model; n is a radical of an alkyl radical ES Representing energy storage annual operating time;
the capacity decline coefficient of the retired battery in the y year; y is the project operation age; theta is the item discount rate; v is the energy storage of the user side to delay the investment income;
calculating the total cost of the retired battery energy storage system according to the modes of recovery, processing and recombination; m is the total number of the nodes of the power grid; c dd (t) real-time electricity prices; eta is the energy storage working efficiency;
active power injected into the energy storage system at a node i at the time t;
the constraint conditions of the objective functions of the upper-layer planning optimization model and the lower-layer operation optimization model comprise: the method comprises the steps of energy storage installation capacity, power constraint, power distribution network safety constraint, power distribution network power flow constraint, energy storage operation constraint and reverse power supply power constraint.
5. The method for configuring energy storage capacity of a retired battery on a user side according to claim 1, wherein: and when solving the established user side retired battery energy storage capacity optimization configuration model considering the degradation characteristics aiming at the given typical operation scene of the power distribution network and the pre-acquired relevant parameters of the power distribution system, adopting a hybrid algorithm combining second-order cone planning and simulated annealing.
6. The method according to claim 5, wherein the method comprises the following steps: the method for solving the established user side retired battery energy storage capacity optimization configuration model considering the degradation characteristics by adopting a hybrid algorithm combining second-order cone programming and simulated annealing comprises the following steps of:
solving a user side retired battery energy storage capacity optimization configuration model considering the degradation characteristics into an upper-layer planning optimization model and a lower-layer operation optimization model;
solving an upper-layer planning optimization model by adopting a simulated annealing algorithm, and setting initial temperature, temperature reduction coefficients and iteration limit value parameters;
carrying out integer coding on the energy storage capacity of the retired power battery at the user side, giving an initial energy storage capacity optimization configuration scheme, wherein the scheme comprises the rated capacity of the energy storage installed at a node i
Maximum discharge power of stored energy installed at node i
And setting the iteration times to zero;
changing the rated capacity configuration and the maximum discharge power configuration of the installed battery energy storage capacity to obtain a new configuration scheme and transmitting the new configuration scheme to a lower-layer operation optimization model;
the lower-layer operation optimization model is used for optimizing and configuring the energy storage capacity of a user-side retired battery obtained from the upper layer according to a given typical operation scene of the power distribution network and by combining a scheme for optimizing and configuring the energy storage capacity of the user-side retired battery with the current consideration of the degradation characteristics, a second-order cone planning algorithm is adopted for carrying out optimization calculation to obtain an energy storage system profit index and an energy storage system operation control strategy, and the corresponding index and the control strategy are returned to the upper-layer planning optimization model to form a target function;
setting the iteration times plus one, judging whether the maximum iteration times at the current temperature is reached, if so, jumping out of the iteration process, and selecting a configuration scheme at the current temperature as a final solution; otherwise, returning to the step IV to regenerate the configuration scheme until the final optimized configuration scheme of the energy storage capacity of the retired battery at the user side considering the degradation characteristic is output.
7. A system for configuring energy storage capacity of a customer-side retired battery, the system comprising:
the model building module is used for building a double-layer optimization model of the energy storage capacity optimization configuration of the retired battery at the user side considering the degradation characteristics and determining a target function and constraint conditions of the model;
and the model calculation module is used for solving the established double-layer optimization model of the energy storage capacity optimization configuration of the retired battery at the user side considering the degradation characteristics according to a given typical operation scene of the power distribution network and the pre-acquired relevant parameters of the power distribution system and the retired battery, and obtaining an energy storage capacity configuration scheme of the retired battery at the user side based on a solving result.
8. The system of claim 7, wherein the model building module comprises:
the double-layer optimization model determination module is used for determining a user side retired battery energy storage capacity optimization configuration double-layer optimization model considering the degeneration characteristics, and comprises an upper-layer planning optimization model and a lower-layer operation optimization model; the upper-layer planning optimization model is used for making an energy storage configuration scheme, the lower-layer operation optimization model is used for making an optimal operation control strategy and feeding back an operation result to the upper-layer planning optimization model;
the target function determining module is used for determining target functions of an upper-layer planning optimization model and a lower-layer operation optimization model in the double-layer optimization model;
and the constraint condition determining module is used for determining the constraint conditions of the objective functions of the upper-layer planning optimization model and the lower-layer operation optimization model.
9. A processing device comprising at least a processor and a memory, the memory having stored thereon a computer program, characterized in that the processor, when executing the computer program, executes to carry out the steps of the user-side retired battery energy storage capacity configuration method according to any of claims 1 to 6.
10. A computer storage medium having computer readable instructions stored thereon which are executable by a processor to perform the steps of the user-side retired battery energy storage capacity configuration method according to any of claims 1 to 6.
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