CN116316557A - Energy storage cluster aggregation method and device suitable for spot market - Google Patents
Energy storage cluster aggregation method and device suitable for spot market 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
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
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- G—PHYSICS
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- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/06—Electricity, gas or water supply
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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
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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
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Abstract
The invention belongs to the technical field of operation control of power systems, and relates to an energy storage cluster aggregation method and device suitable for spot markets. The method comprises the steps of firstly establishing an objective function of an energy storage cluster aggregation optimization model: under the spot market condition, the constraint of an energy storage cluster aggregation optimization model is established: linearizing an optimization model formed by an objective function and constraint conditions to obtain a mixed integer linear stochastic programming model: and solving the model by using a branch-and-bound method to obtain an energy storage cluster segmented power-quotation curve, namely an aggregation result of the energy storage cluster. The invention considers the influence brought by the randomness and the fluctuation of the clear price of the spot market, realizes the mathematical expectation value of minimizing the energy supply cost while meeting the operation safety constraint of the energy storage station, and effectively aims at the randomness and the fluctuation of the spot market.
Description
Technical Field
The invention belongs to the technical field of operation control of power systems, and relates to an energy storage cluster aggregation method and device suitable for spot markets.
Background
Under the low-carbon background, the permeability of new energy in the electric power system is improved year by year, and the requirement for the flexibility adjusting capability is greatly improved due to the randomness, intermittence and fluctuation of new energy power generation. In recent years, the user side mostly adopts electrochemical energy storage as a standby battery, such as a 5G communication base station, but the energy storage idle rate of the load side is too high, and the reliability of a power supply system of a base station communication power supply is higher, so that the standby battery is in an idle or floating charge state for a long time, and the standby battery has the potential of providing flexibility for a power system.
In the prior art, the application number is 202210499759.3, the patent entitled "optimization operation method, device, equipment and medium of energy storage quick charging station group" adopts a deterministic optimization model, and the defect is that the influence of random factors such as spot market price and load on the operation of the energy storage group cannot be considered.
Disclosure of Invention
The invention aims to provide an energy storage cluster aggregation method, an energy storage cluster aggregation device, electronic equipment and a storage medium which are suitable for the spot market, so that the energy storage cluster is aggregated to be flexible, the adjustment capability is provided for a power system, and meanwhile, extra benefits are obtained under the condition of the spot market.
According to a first aspect of the present invention, there is provided an energy storage cluster aggregation method adapted to spot markets, including:
step 2, under the spot market condition, establishing constraint of an energy storage cluster aggregation optimization model:
step 3, linearizing an energy storage cluster aggregation optimization model under spot market conditions formed by the objective function of the step 1 and the constraint condition of the step 2 to obtain a mixed integer linear random programming model:
and 4, solving the mixed integer linear stochastic programming model in the step 3 by using a branch-and-bound method to obtain an energy storage cluster segmented power-quotation curve, namely an aggregation result of the energy storage cluster.
The energy storage cluster aggregation method suitable for the spot market can better consider the economy and the time variability of the energy storage adjustable capacity so as to maximize the application of the energy storage clusters, enable the energy storage cluster aggregate to reach the admission rule of the power system, provide flexibility for the power system, obtain extra income and demand response subsidy under the condition of the spot market, improve the flexibility of power grid operation and increase the extra income of an energy storage cluster operator. Compared with the existing aggregation method, the method considers the influence caused by the randomness and the fluctuation of the spot market price, achieves the minimum mathematical expected value of the energy supply cost while meeting the operation safety constraint of the energy storage station, and can effectively influence the influence of the randomness and the fluctuation of the spot market on the operation of the energy storage clusters.
According to a second aspect of the present invention, there is provided an aggregation device of energy storage clusters adapted to spot markets, comprising:
the first model building module is used for building an objective function of the energy storage cluster aggregation optimization model under the spot market condition:
the second model building module is used for building the constraint of the energy storage cluster aggregation optimization model under the spot market condition:
the third model building module is used for linearizing the energy storage cluster aggregation optimization model under the spot market condition formed by the objective function obtained by the first model building module and the constraint condition obtained by the second model building module to obtain a mixed integer linear random programming model:
and the model solving module is used for solving the mixed integer linear stochastic programming model established by the third model establishing module by using a branch-and-bound method to obtain an energy storage cluster segmented power-quotation curve, namely an aggregation result of the energy storage cluster.
According to a third aspect of the present invention, there is provided an electronic device comprising:
a memory for storing computer-executable instructions;
a processor configured to sequentially perform:
under the spot market condition, establishing an objective function of an energy storage cluster aggregation optimization model:
under the spot market condition, the constraint of an energy storage cluster aggregation optimization model is established:
the method comprises the steps of linearizing an energy storage cluster aggregation optimization model under spot market conditions formed by an objective function and constraint conditions to obtain a mixed integer linear random programming model:
and solving the mixed integer linear stochastic programming model by using a branch-and-bound method to obtain an energy storage cluster segmentation power-quotation curve, namely an aggregation result of the energy storage cluster.
According to a fourth aspect of the present invention, there is provided a computer-readable storage medium having stored thereon a computer program for causing the computer to sequentially execute:
under the spot market condition, establishing an objective function of an energy storage cluster aggregation optimization model:
under the spot market condition, the constraint of an energy storage cluster aggregation optimization model is established:
the method comprises the steps of linearizing an energy storage cluster aggregation optimization model under spot market conditions formed by an objective function and constraint conditions to obtain a mixed integer linear random programming model:
and solving the mixed integer linear stochastic programming model by using a branch-and-bound method to obtain an energy storage cluster segmentation power-quotation curve, namely an aggregation result of the energy storage cluster.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are required to be used in the description of the embodiments will be briefly described below. It is evident that the drawings in the following description are only some embodiments of the present invention and that other drawings may be obtained from the drawings without inventive work for a person of ordinary skill in the art.
Fig. 1 is a schematic diagram of an aggregation process involved in an aggregation method of energy storage clusters adapted to spot markets according to an embodiment of the present invention.
Fig. 2 is a schematic structural diagram of an aggregation device adapted to an energy storage cluster of a spot market according to an embodiment of the present invention.
Detailed Description
The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only one embodiment of the present invention, not all embodiments. 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 present invention will be described in detail below with reference to the drawings attached to the specification.
Fig. 1 is a schematic diagram of an aggregation process related to an aggregation method suitable for spot market energy storage clusters according to an embodiment of the present invention.
In one embodiment of the invention, the aggregation method of the energy storage clusters suitable for spot market obtains the aggregation result of a plurality of energy storage stations in the energy storage clusters, and the aggregation result can be used for the purposes of power system dispatching, spot market clearing and the like, and comprises the following steps:
step one, establishing an objective function of an energy storage cluster aggregation optimization model under spot market conditions:
the method aims at minimizing the overall operation cost under the spot market condition, and for randomly fluctuating day-ahead electricity prices and real-time electricity prices, the expected value of the minimized cost is as follows:
wherein ,
in order to obtain the mathematical expectation, gamma is the set formed by all scheduling moments t, N is the total number of segments of power in the spot market, the power of the energy storage cluster is segmented to obtain the total number of segments of power, N=1, 2, …, N, and the total number of segments of power is defined by the spot marketIt is determined that in an exemplary embodiment of the present invention, n=10; />
To determine whether the variable is cleared before the day, the value is 0 or-1, if the power of the ith section is cleared at the price of the day before the day, the value is recorded +.>
If the power of the ith section is not paid out by the daily price, the power of the ith section is paid out by the real-time price, and +.>
For the price of the spot market to be cleared before day at the scheduling time t, the subscript DA indicates before day,/-before day>
For the real-time price of the spot market at the scheduling instant t, the subscript RT indicates real-time,/-for the spot market>
For scheduling the power of the power segment i at time t, N E For the number of energy storage stations in the energy storage cluster, < >>
Is the ith in the energy storage cluster E The unit adjustment costs of the individual energy storage stations are derived from the energy storage station instructions, < >>
Clear power in real-time market for non-metered parts, +.>
Meaning the difference between the actual power used at the scheduling instant t and the sum of all power segments,/-, for>
The expression of (2) is:
step two: establishing constraint of an energy storage cluster aggregation optimization model under spot market conditions:
(1) Establishing operation constraint of each energy storage station in the energy storage cluster:
wherein ,STR For a set of all energy storage stations in an energy storage cluster,
is the ith in the energy storage cluster E Response status of the individual energy storage stations->
The discharge quantity of the corresponding energy storage station at the scheduling time t is calculated; />
Is the ith in the energy storage cluster E Starting variable, z, of each energy storage station i,OUT,t =1 means that the energy storage station starts to discharge at the scheduled time t; />
Representing the ith in the cluster E Whether or not the energy storage resources are charged->
As a variable of 0-1, when the energy storage resource is charged at the scheduling time t, record
Is the ith in the energy storage cluster E The set of all dischargeable times of the individual energy storage stations, < >>
Is the ith in the cluster E The set of all chargeable moments of the individual energy storage stations, < >>
and />
Given by the operator; />
Representing the ith in the energy storage cluster E Minimum duration of a single discharge of the individual energy storage stations; />
Respectively representing the ith in the cluster E The minimum discharge time and the maximum discharge time of the energy storage resources in one day are obtained from the energy storage equipment instruction book of the energy storage station and the operation rule of the energy storage station; />
Respectively represent the ith in the energy storage cluster E The maximum discharge capacity and the maximum charge capacity of the energy storage stations are obtained by subtracting the current charge state of the energy storage stations from the maximum capacity in the specifications of the energy storage equipment; />
Respectively the ith in the energy storage clusters E The power change quantity of each energy storage station in a discharging/charging state is given by an instruction book of the energy storage equipment, and T represents a scheduling time interval, and is 15 minutes in one embodiment of the invention; />
Is the ith in the energy storage cluster E The actual power of the individual energy storage stations,
is the ith in the energy storage cluster E The power baselines of the energy storage stations are predicted and obtained by the loads of the energy storage stations;
(2) Establishing auxiliary variable constraints:
wherein ,
for the scheduling time t, the energy storage cluster offers the ith power, if the variable for determining whether to clear before the day +.>
Make->
I.e. the quote is greater than the daily price of the spot market, the power of this segment is at the daily price +.>
Realize clearing, if->
Make->
I.e. the quotation is smaller than the daily clearing price of the spot market, and the ith section of power is cleared in real time;
(3) Establishing quotation curve constraint:
the constraint indicates that the quotation curve of the energy storage cluster for each section of power is decreasing;
(4) The power balance of the energy storage cluster and the upper limit constraint and the lower limit constraint of the power:
wherein ,
the upper limit and the lower limit of each power are respectively given by spot market rules.
Linearizing an energy storage cluster aggregation optimization model under spot market conditions formed by the objective function of the first step and the constraint condition of the second step to obtain a mixed integer linear random programming model, wherein the method comprises the following steps of:
(1) Introducing auxiliary variables
The meaning is the variable +.>
Power variable +.>
Product of (i.e.)>
Linearizing bilinear terms in the objective function of the energy storage cluster aggregation optimization model under the spot market condition in the step one, namely enabling +.>
The objective function is rewritten as:
(3-2) supplementing the additional constraints introduced by linearization, such that
The establishment is as follows:
wherein M is an arbitrarily large integer, in one embodiment of the present invention, M may be 10000.
Obtaining a mixed integer linear stochastic programming model, wherein the objective function of the model is the objective function obtained in the step (1), and the constraint conditions of the model are the constraint conditions in the step (1), (2), (3), (4) and the step (2);
(4) And (3) solving the mixed integer linear stochastic programming model in the step (3) by using a branch-and-bound method to obtain an energy storage cluster segmented power-quotation curve, namely an aggregation result of the energy storage cluster. The aggregation result can be used for dispatching of power systems, clearing of spot markets and the like. The mixed integer linear random programming model in the method can be converted into the mixed integer linear programming model based on scene generation and scene reduction technology, and the branch-and-bound method is a common method for solving the mixed integer linear programming.
Correspondingly, the embodiment of the invention provides an energy storage cluster aggregation device adapting to the spot market, which has a structure shown in fig. 2 and comprises:
the first model building module is used for building an objective function of the energy storage cluster aggregation optimization model under the spot market condition:
the second model building module is used for building the constraint of the energy storage cluster aggregation optimization model under the spot market condition:
the third model building module is used for linearizing the energy storage cluster aggregation optimization model under the spot market condition formed by the objective function obtained by the first model building module and the constraint condition obtained by the second model building module to obtain a mixed integer linear random programming model:
and the model solving module is used for solving the mixed integer linear stochastic programming model established by the third model establishing module by using a branch-and-bound method to obtain an energy storage cluster segmented power-quotation curve, namely an aggregation result of the energy storage cluster.
In an embodiment of the present invention, an electronic device is provided, including:
a memory for storing computer-executable instructions;
a processor configured to sequentially perform:
under the spot market condition, establishing an objective function of an energy storage cluster aggregation optimization model:
under the spot market condition, the constraint of an energy storage cluster aggregation optimization model is established:
the method comprises the steps of linearizing an energy storage cluster aggregation optimization model under spot market conditions formed by an objective function and constraint conditions to obtain a mixed integer linear random programming model:
and solving the mixed integer linear stochastic programming model by using a branch-and-bound method to obtain an energy storage cluster segmentation power-quotation curve, namely an aggregation result of the energy storage cluster.
A computer readable storage medium according to an embodiment of the present invention stores a computer program thereon, where the computer program is configured to cause the computer to sequentially execute:
under the spot market condition, establishing an objective function of an energy storage cluster aggregation optimization model:
under the spot market condition, the constraint of an energy storage cluster aggregation optimization model is established:
the method comprises the steps of linearizing an energy storage cluster aggregation optimization model under spot market conditions formed by an objective function and constraint conditions to obtain a mixed integer linear random programming model:
and solving the mixed integer linear stochastic programming model by using a branch-and-bound method to obtain an energy storage cluster segmentation power-quotation curve, namely an aggregation result of the energy storage cluster.
It should be noted that, in the embodiment of the present invention, the processor may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field programmable gate arrays (Field Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor, or the processor may be any conventional processor or the like, and the memory may be used to store the computer program and/or modules, and the processor may implement the various functions of the spot-market-adapted energy storage cluster aggregation method by executing or executing the computer program and/or modules stored in the memory, and invoking data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program (such as a sound playing function, an image playing function, etc.) required for at least one function, and the like; the storage data area may store data created by the operating system during the running of an application program, etc. In addition, the memory may include a high-speed random access memory, and may further include a nonvolatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), a memory device of at least one magnetic disk, or a Flash memory device.
Based on such understanding, the present disclosure may implement all or part of the flow of the method of the above embodiments, or may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of each of the method embodiments described above. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth. It should be noted that the above-described apparatus embodiments are merely illustrative, and the units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. In addition, in the device embodiment drawings provided by the disclosure, the connection relation between the modules represents that the modules have communication connection therebetween, and may be specifically implemented as one or more communication buses or signal lines. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
While the foregoing is directed to the preferred embodiments of the present disclosure, it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present disclosure, and such modifications and adaptations are intended to be comprehended within the scope of the present disclosure.
Claims (7)
1. A method of aggregating energy storage clusters adapted to the spot market, comprising:
step 1, establishing an objective function of an energy storage cluster aggregation optimization model under spot market conditions:
step 2, under the spot market condition, establishing constraint of an energy storage cluster aggregation optimization model:
step 3, linearizing an energy storage cluster aggregation optimization model under spot market conditions formed by the objective function of the step 1 and the constraint condition of the step 2 to obtain a mixed integer linear random programming model:
and 4, solving the mixed integer linear stochastic programming model in the step 3 by using a branch-and-bound method to obtain an energy storage cluster segmented power-quotation curve, namely an aggregation result of the energy storage cluster.
2. The aggregation method of claim 1, wherein the objective function of the energy storage cluster aggregation optimization model is:
under the spot market condition, establishing an objective function of an energy storage cluster aggregation optimization model:
the method aims at minimizing the overall operation cost under the spot market condition, and for randomly fluctuating day-ahead electricity prices and real-time electricity prices, the expected value of the minimized cost is as follows:
wherein ,
in order to obtain the mathematical expectation, gamma is a set formed by all scheduling moments t, and N is the total number of segments of power in the spot market; />
In order to determine whether the variable is cleared before the day, if the i-th power is cleared at the day before the price, record +.>
If the power of the ith section is not paid out by the daily price, the power of the ith section is paid out by the real-time price, and +.>
For the price of the spot market to be cleared before day at the scheduling time t, the subscript DA indicates before day,/-before day>
For the real-time price of the spot market at the scheduling instant t, the subscript RT indicates real-time,/-for the spot market>
For scheduling the power of the power segment i at time t, N E For the number of energy storage stations in the energy storage cluster,
is the ith in the energy storage cluster E Unit adjustment cost of individual energy storage stations, +.>
Clear power in real-time market for non-metered parts, +.>
Meaning the difference between the actual power used at the scheduling instant t and the sum of all power segments,/-, for>
The expression of (2) is:
3. the aggregation method of claim 1, wherein (2) constraints of the energy storage cluster aggregation optimization model under spot market conditions are established:
(1) Establishing operation constraint of each energy storage station in the energy storage cluster:
wherein ,STR For a set of all energy storage stations in an energy storage cluster,
is the ith in the energy storage cluster E Response status of the individual energy storage stations->
The discharge quantity of the corresponding energy storage station at the scheduling time t is calculated; />
Is the ith in the energy storage cluster E Starting variable, z, of each energy storage station i,OUT,t =1 means that the energy storage station starts to discharge at the scheduled time t; />
Representing the ith in the cluster E Whether or not the energy storage resources are charged->
As a variable of 0-1, when the energy storage resource is charged at the scheduling time t, record
Is the ith in the energy storage cluster E The set of all dischargeable times of the individual energy storage stations, < >>
Is the ith in the cluster E The set of all chargeable moments of the individual energy storage stations, < >>
and />
Given by the operator;/>
representing the ith in the energy storage cluster E Minimum duration of a single discharge of the individual energy storage stations; />
Respectively representing the ith in the cluster E The minimum discharge time and the maximum discharge time of the energy storage resources in one day are obtained from the energy storage equipment instruction book of the energy storage station and the operation rule of the energy storage station; />
Respectively represent the ith in the energy storage cluster E The maximum discharge capacity and the maximum charge capacity of the energy storage stations are obtained by subtracting the current charge state of the energy storage stations from the maximum capacity in the specifications of the energy storage equipment; />
Respectively the ith in the energy storage clusters E The power variation of each energy storage station in a discharging/charging state is given by an energy storage device instruction book, and T represents a scheduling time interval; />
Is the ith in the energy storage cluster E The actual power of the individual energy storage stations, +.>
Is the ith in the energy storage cluster E The power baselines of the energy storage stations are predicted and obtained by the loads of the energy storage stations;
(2) Establishing auxiliary variable constraints:
wherein ,
for the scheduling time t, the energy storage cluster offers the ith power, if the variable for determining whether to clear before the day is determined
Make->
I.e. the quote is greater than the daily price of the spot market, the power of this segment is at the daily price +.>
Realize clearing, if->
Make->
I.e. the quotation is smaller than the daily clearing price of the spot market, and the ith section of power is cleared in real time;
(3) Establishing quotation curve constraint:
the constraint indicates that the quotation curve of the energy storage cluster for each section of power is decreasing;
(4) The power balance of the energy storage cluster and the upper limit constraint and the lower limit constraint of the power:
wherein ,
the upper limit and the lower limit of each power are respectively given by spot market rules.
4. The aggregation method of claim 1, wherein linearizing the energy storage cluster aggregation optimization model under spot market conditions consisting of the objective function of step 1 and the constraint condition of step 2 to obtain a mixed integer linear stochastic programming model comprises:
(1) Introducing auxiliary variables
The meaning is the variable +.>
Power variable +.>
Product of (i.e.)>
Linearizing bilinear terms in the objective function of the energy storage cluster aggregation optimization model under the spot market condition in the step 1, namely enabling +.>
The objective function is rewritten as:
(2) Supplementing the additional constraints introduced by linearization to make
The establishment is as follows:
wherein M is an arbitrarily large integer;
obtaining a mixed integer linear stochastic programming model, wherein the objective function of the model is the objective function obtained in the step 1, and the constraint conditions of the model comprise the constraint conditions of (1), (2), (3), (4) in the step 2 and (2) in the step 3.
5. An aggregation device for an energy storage cluster adapted for spot markets, comprising:
the first model building module is used for building an objective function of the energy storage cluster aggregation optimization model under the spot market condition:
the second model building module is used for building the constraint of the energy storage cluster aggregation optimization model under the spot market condition:
the third model building module is used for linearizing the energy storage cluster aggregation optimization model under the spot market condition formed by the objective function obtained by the first model building module and the constraint condition obtained by the second model building module to obtain a mixed integer linear random programming model:
and the model solving module is used for solving the mixed integer linear stochastic programming model established by the third model establishing module by using a branch-and-bound method to obtain an energy storage cluster segmented power-quotation curve, namely an aggregation result of the energy storage cluster.
6. An electronic device, comprising:
a memory for storing computer-executable instructions;
a processor configured to sequentially perform:
under the spot market condition, establishing an objective function of an energy storage cluster aggregation optimization model:
under the spot market condition, the constraint of an energy storage cluster aggregation optimization model is established:
the method comprises the steps of linearizing an energy storage cluster aggregation optimization model under spot market conditions formed by an objective function and constraint conditions to obtain a mixed integer linear random programming model:
and solving the mixed integer linear stochastic programming model by using a branch-and-bound method to obtain an energy storage cluster segmentation power-quotation curve, namely an aggregation result of the energy storage cluster.
7. A computer-readable storage medium having stored thereon a computer program for causing the computer to sequentially perform:
under the spot market condition, establishing an objective function of an energy storage cluster aggregation optimization model:
under the spot market condition, the constraint of an energy storage cluster aggregation optimization model is established:
the method comprises the steps of linearizing an energy storage cluster aggregation optimization model under spot market conditions formed by an objective function and constraint conditions to obtain a mixed integer linear random programming model:
and solving the mixed integer linear stochastic programming model by using a branch-and-bound method to obtain an energy storage cluster segmentation power-quotation curve, namely an aggregation result of the energy storage cluster.
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