CN118014164A - Energy storage capacity configuration double-layer optimization method and system considering flexibility requirements - Google Patents
Energy storage capacity configuration double-layer optimization method and system considering flexibility requirements Download PDFInfo
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Abstract
The invention discloses a double-layer optimization method and a double-layer optimization system for energy storage capacity configuration in consideration of flexibility requirements, wherein the method comprises the following steps: under a preset first constraint condition, constructing an energy storage capacity configuration inner layer model by taking the minimum running cost of flexible resource output of a short time scale as a first objective function; calculating the flexible resource adequacy under a short time scale, and determining a second constraint condition according to the flexible resource adequacy; under the second constraint condition, constructing an energy storage capacity configuration outer layer model by using the minimum running cost of the energy storage system as a second objective function; and carrying out iterative solution on the energy storage capacity configuration inner layer model and the energy storage capacity configuration outer layer model according to an improved quantum particle swarm algorithm to obtain a final energy storage capacity configuration scheme. The method realizes the interactive decision of the double-layer model and rapidly obtains the optimal scheme of flexible resource allocation in the region.
Description
Technical Field
The invention belongs to the technical field of energy storage capacity optimization, and particularly relates to an energy storage capacity configuration double-layer optimization method and system considering flexibility requirements.
Background
The flexible resources comprise distributed energy storage, demand response load, adjustable power supply and the like, and have the characteristics of wide distribution and dispersion. In the electricity peak period, the flexible resource can achieve the effect of peak regulation and frequency modulation by reducing the load or increasing the output, thereby improving the safety and stability of the power grid and reducing the redundant investment. However, the response degrees of various flexible resources are different, so how to quantify the response degrees of the flexibility according to the characteristics of various flexible resources, optimize the allocation and the scheduling of the flexible resources in the area and improve the economy of the power grid is a problem to be solved. In the current research, different kinds of indexes are often used for different kinds of flexibility demands, a supply degree quantization index capable of systematically describing flexibility is lacking for different kinds of resources, and an index system capable of rapidly calculating the flexibility resource configuration is lacking.
The configuration optimization of the flexible resources needs to consider the scheduling cost, the running cost and the construction cost supply degree of each flexible resource, and meanwhile, the investment running cost of the system needs to be considered, the capacity configuration schemes of different scheduling schemes are different, and the flexibility adjustment cost of the system is different. Therefore, achieving flexible and stable operation of regional power grids is a current urgent need.
Disclosure of Invention
The invention provides a double-layer optimization method and a double-layer optimization system for energy storage capacity configuration, which take the flexibility requirement into consideration, and are used for solving the technical problem that flexible and stable operation of a regional power grid cannot be realized.
In a first aspect, the present invention provides a method for energy storage capacity configuration double-layer optimization in consideration of flexibility requirements, including:
under a preset first constraint condition, constructing an energy storage capacity configuration inner layer model for a first objective function with the minimum running cost of flexible resource output of a short time scale, wherein the expression of the first objective function is as follows:
，
In the method, in the process of the invention, Cost is adjusted for flexible resources,/>For the unit start-stop cost of the adjustable hydropower station i,/>To adjust the start-stop capacity of the hydropower station i at the time t,/>Load cost per cut for demand response load unit i,Cut load quantity at time t for demand response load i,/>For the increased output cost of the demand response load unit i,For the increased output of the demand response load unit i,/>For the unit power generation cost of the adjustable hydropower station i,/>To adjust the actual output of the hydropower station i at the time t,/>Respectively an adjustable hydropower station and a demand response load,/>Is the number of time periods;
calculating the flexible resource adequacy under a short time scale, and determining a second constraint condition according to the flexible resource adequacy;
under the second constraint condition, constructing an energy storage capacity configuration outer layer model by using a second objective function with the minimum running cost of the energy storage system, wherein the expression of the second objective function is as follows:
，
In the method, in the process of the invention, For the investment of the system and the operation cost,/>To adjust the investment cost of the hydroelectric generating set,/>Investment cost for electrochemical energy storage,/>For the running cost of the system,/>Risk loss of the system due to insufficient flexible resources;
the expression of risk loss of the computing system due to insufficient flexible resources is as follows:
，
In the method, in the process of the invention, 、/>Are all auxiliary variables for calculating CvaR values,/>For confidence level,/>、/>Lost cost due to insufficient up and down flexibility resources per unit,/>For the power generated at the t moment of all the generator sets,/>Is the probability of occurrence of a typical scene n,/>For a typical scenario,/>Providing aggregate for downregulation of flexible resources,/>Supplying aggregate for up-regulation of flexible resources,/>For the load at time t,/>，/>For/>Or/>，/>Indicating an insufficient degree of flexibility;
and carrying out iterative solution on the energy storage capacity configuration inner layer model and the energy storage capacity configuration outer layer model according to an improved quantum particle swarm algorithm to obtain a final energy storage capacity configuration scheme.
In a second aspect, the present invention provides an energy storage capacity configuration dual-layer optimization system taking into account flexibility requirements, comprising:
The first construction module is configured to construct an energy storage capacity configuration inner layer model for a first objective function with the minimum running cost of flexible resource output of a short time scale under a preset first constraint condition, wherein the expression of the first objective function is as follows:
，
In the method, in the process of the invention, Cost is adjusted for flexible resources,/>For the unit start-stop cost of the adjustable hydropower station i,/>To adjust the start-stop capacity of the hydropower station i at the time t,/>Load cost per cut for demand response load unit i,Cut load quantity at time t for demand response load i,/>For the increased output cost of the demand response load unit i,For the increased output of the demand response load unit i,/>For the unit power generation cost of the adjustable hydropower station i,/>To adjust the actual output of the hydropower station i at the time t,/>Respectively an adjustable hydropower station and a demand response load,/>Is the number of time periods;
the computing module is configured to compute the flexible resource adequacy under a short time scale and determine a second constraint condition according to the flexible resource adequacy;
the second construction module is configured to construct an energy storage capacity configuration outer layer model for a second objective function with the minimum energy storage system operation cost under the second constraint condition, wherein the expression of the second objective function is as follows:
，
In the method, in the process of the invention, For the investment of the system and the operation cost,/>To adjust the investment cost of the hydroelectric generating set,/>Investment cost for electrochemical energy storage,/>For the running cost of the system,/>Risk loss of the system due to insufficient flexible resources;
the expression of risk loss of the computing system due to insufficient flexible resources is as follows:
，
In the method, in the process of the invention, 、/>Are all auxiliary variables for calculating CvaR values,/>For confidence level,/>、/>Lost cost due to insufficient up and down flexibility resources per unit,/>For the power generated at the t moment of all the generator sets,/>Is the probability of occurrence of a typical scene n,/>For a typical scenario,/>Providing aggregate for downregulation of flexible resources,/>Supplying aggregate for up-regulation of flexible resources,/>For the load at time t,/>，/>For/>Or/>，/>Indicating an insufficient degree of flexibility;
And the solving module is configured to carry out iterative solving on the energy storage capacity configuration inner layer model and the energy storage capacity configuration outer layer model according to an improved quantum particle swarm algorithm to obtain a final energy storage capacity configuration scheme.
In a third aspect, there is provided an electronic device, comprising: the system comprises at least one processor and a memory communicatively connected with the at least one processor, wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the steps of the energy storage capacity configuration double-layer optimization method taking into account flexibility requirements of any of the embodiments of the invention.
In a fourth aspect, the present invention also provides a computer readable storage medium having stored thereon a computer program, which when executed by a processor, causes the processor to perform the steps of the energy storage capacity configuration double-layer optimization method according to any of the embodiments of the present invention, taking into account the flexibility requirements.
According to the energy storage capacity configuration double-layer optimization method and system considering the flexibility requirement, the flexibility resource adequacy under a short time scale is calculated, the second constraint condition is determined according to the flexibility resource adequacy, and the two aspects of the system investment operation cost and the output operation cost of the flexibility resource are considered to construct a double-layer capacity configuration model, so that the double-layer model interaction decision is realized, and the optimal scheme of the regional flexibility resource configuration is obtained rapidly.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a dual-layer optimization method for energy storage capacity configuration in consideration of flexibility requirements according to an embodiment of the present invention;
FIG. 2 is a block diagram illustrating a dual-layer optimization system with energy storage capacity configuration in consideration of flexibility requirements according to an embodiment of the present invention;
Fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, a flow chart of a dual-layer optimization method for energy storage capacity configuration in consideration of flexibility requirements according to the present application is shown.
As shown in fig. 1, the energy storage capacity configuration double-layer optimization method considering the flexibility requirement specifically includes the following steps:
step S101, constructing an energy storage capacity configuration inner layer model for a first objective function according to a preset first constraint condition and with the minimum operation cost of flexible resource output of a short time scale.
In this step, the expression of the first objective function is:
，
In the method, in the process of the invention, Cost is adjusted for flexible resources,/>For the unit start-stop cost of the adjustable hydropower station i,/>To adjust the start-stop capacity of the hydropower station i at the time t,/>Load cost per cut for demand response load unit i,Cut load quantity at time t for demand response load i,/>For the increased output cost of the demand response load unit i,For the increased output of the demand response load unit i,/>For the unit power generation cost of the adjustable hydropower station i,/>To adjust the actual output of the hydropower station i at the time t,/>Respectively an adjustable hydropower station and a demand response load,/>Is the number of time periods.
It should be noted that, the first constraint condition includes a power generation-load balance constraint, an electrochemical energy storage charge-discharge constraint, and an electric quantity constraint;
The power generation-load balancing constraint expression is:
，
In the method, in the process of the invention, For the power generated at the t moment of all the generator sets,/>For the discharge power of the electrochemical energy storage i at time t,/>For the load at time t,/>For the charging power of the electrochemical energy storage i at time t,/>The number of the electrochemical energy storage;
the expression of the electrochemical energy storage charge-discharge constraint is:
，
，
，
In the method, in the process of the invention, For the minimum discharge power of the electrochemical energy storage i,/>For the discharge power of the electrochemical energy storage i at the time t,/>For the shutdown state of the electrochemical energy storage i at time t,/>For maximum discharge power of electrochemical energy storage i,/>For the minimum charge power of the electrochemical energy storage i,/>Charging power at time t for electrochemical energy storage i,/>For the starting state of the electrochemical energy storage i at time t,/>Maximum charging power for electrochemical energy storage i;
The expression of the electric quantity constraint is:
，
，
，
In the method, in the process of the invention, For the minimum output of the adjustable hydropower i,/>For the maximum output of the adjustable hydropower i,/>To adjust the lower gradient rate upper limit of the hydropowerTo adjust the upper limit of the ascending climbing rate of the hydropowerTo adjust the actual output of the hydropower station i at the time t-1,/>Is the number of month period,/>The monthly maximum power generation amount of the adjustable hydropower station i.
Step S102, calculating the flexible resource adequacy under a short time scale, and determining a second constraint condition according to the flexible resource adequacy.
In this step, the expression for calculating the flexible resource adequacy at a short time scale is:
，
，
，
In the method, in the process of the invention, Is the probability of occurrence of a typical scene n,/>For a typical scenario,/>Providing aggregate for downregulation of flexible resources,/>Supplying aggregate for up-regulation of flexible resources,/>For the load at time t,/>For the power generated at the t moment of all the generator sets,/>For the flexible supply of adjustable hydropower t-time upregulation,/>Flexible supply level for up-regulation of electrochemical energy storage t-time,/>Flexible supply level up-regulated for demand response load t moment,/>For the flexible supply of adjustable hydropower t-time downregulation,/>For a flexible supply of electrochemical energy storage t-time down-regulation,The flexible supply level is adjusted down for demand response load t.
It should be noted that the expression of the flexible supply degree of the adjustable hydropower is:
，
In the method, in the process of the invention, To adjust the upper limit of the ascending climbing rate of the hydropowerFor time scale,/>To adjust the maximum output power of water and electricity-To adjust the lower gradient rate upper limit of the hydropowerMinimum output power for adjustable hydropower;
the flexible supply of electrochemical energy storage is expressed as:
，
In the method, in the process of the invention, Is the maximum discharge power,/>For the rated capacity of the electrochemical energy storage i,/>Discharge efficiency for electrochemical energy storage,/>For the real-time value of the charge state of electrochemical energy storage,/>Is the minimum value of the charge state of electrochemical energy storage,/>For maximum charging power,/>For maximum value of electrochemical energy storage charge state,/>Charging efficiency for electrochemical energy storage;
The expression of the flexible supply degree of the demand response load is:
，
In the method, in the process of the invention, Power usage for demand responsive load increase,/>Power usage for demand responsive load removal,/>The amount of load is responded to for demand.
And step S103, constructing an energy storage capacity configuration outer layer model for the second objective function with the minimum energy storage system operation cost under the second constraint condition.
In this step, the expression of the second objective function is:
，
In the method, in the process of the invention, For the investment of the system and the operation cost,/>To adjust the investment cost of the hydroelectric generating set,/>Investment cost for electrochemical energy storage,/>For the running cost of the system,/>Risk loss of the system due to insufficient flexible resources;
It should be noted that, the expression of risk loss of the computing system due to insufficient flexible resources is:
，
In the method, in the process of the invention, 、/>Are all auxiliary variables for calculating CvaR values,/>For confidence level,/>、/>Lost cost due to insufficient up and down flexibility resources per unit,/>For the power generated at the t moment of all the generator sets,/>Is the probability of occurrence of a typical scene n,/>For a typical scenario,/>Providing aggregate for downregulation of flexible resources,/>Supplying aggregate for up-regulation of flexible resources,/>For the load at time t,/>，/>For/>Or/>，/>Indicating an insufficient degree of flexibility;
the expression for calculating the investment cost of the adjustable hydroelectric generating set is as follows:
，
In the method, in the process of the invention, For the total number of the adjustable hydroelectric generating sets,/>For the unit investment cost of the adjustable hydroelectric generating set i,/>The capacity of the hydroelectric generating set i can be adjusted;
the expression for calculating the investment cost of electrochemical energy storage is:
，
In the method, in the process of the invention, For the total number of electrochemical energy storage,/>Per unit investment cost for electrochemical energy storage i,/>Is the capacity of the electrochemical energy storage i.
Further, the second constraint condition is to consider a flexible resource adequacy constraint, and the expression to consider the flexible resource adequacy constraint is: in the above, the ratio of/> And the margin value is reserved for flexible resources.
Step S104, carrying out iterative solution on the energy storage capacity configuration inner layer model and the energy storage capacity configuration outer layer model according to an improved quantum particle swarm algorithm to obtain a final energy storage capacity configuration scheme.
In the step, setting random initial positions and speeds of particle swarms, namely positions and capacities of flexible resources;
inputting a first objective function and a first constraint condition of an energy storage capacity configuration inner layer model, and optimizing flexible resource output in each period;
Feeding back an optimization result of the flexible resource output to an energy storage capacity configuration outer layer model, calculating a second objective function and a second constraint condition of the energy storage capacity configuration outer layer model, and optimizing the position and capacity of the flexible resource;
calculating an fitness function and updating a particle swarm;
the excellent particle chaos search of 20% of the adaptability in the particle swarm is carried out, and the historical optimal speed and position are updated;
performing chaotic search on the rest particles, and updating the historical optimal speed and position of the rest particles;
Calculating local suction points of particles and population average optimal positions, and updating the positions of the particles;
and if the convergence condition is met, obtaining a final energy storage capacity configuration scheme.
In summary, the method calculates the flexibility resource adequacy under a short time scale, determines the second constraint condition according to the flexibility resource adequacy, and constructs a double-layer capacity configuration model by considering two aspects of the system investment operation cost and the output operation cost of the flexibility resource, thereby realizing the interactive decision of the double-layer model and rapidly obtaining the optimal scheme of the flexibility resource configuration in the area.
Referring to fig. 2, a block diagram of a dual-layer optimization system with energy storage capacity configuration in consideration of flexibility requirements according to the present application is shown.
As shown in fig. 2, the energy storage capacity configuration dual-layer optimization system 200 includes a first construction module 210, a calculation module 220, a second construction module 230, and a solution module 240.
The first construction module 210 is configured to construct an energy storage capacity configuration inner layer model for a first objective function with a minimum running cost of flexible resource output of a short time scale under a preset first constraint condition, where an expression of the first objective function is:
，
In the method, in the process of the invention, Cost is adjusted for flexible resources,/>For the unit start-stop cost of the adjustable hydropower station i,/>To adjust the start-stop capacity of the hydropower station i at the time t,/>Load cost per cut for demand response load unit i,Cut load quantity at time t for demand response load i,/>For the increased output cost of the demand response load unit i,For the increased output of the demand response load unit i,/>For the unit power generation cost of the adjustable hydropower station i,/>To adjust the actual output of the hydropower station i at the time t,/>Respectively an adjustable hydropower station and a demand response load,/>Is the number of time periods;
a calculation module 220 configured to calculate a flexible resource adequacy at a short time scale and determine a second constraint condition according to the flexible resource adequacy;
a second construction module 230 configured to construct an energy storage capacity configuration outer layer model for a second objective function with a minimum energy storage system operation cost under the second constraint condition, wherein an expression of the second objective function is:
，
In the method, in the process of the invention, For the investment of the system and the operation cost,/>To adjust the investment cost of the hydroelectric generating set,/>Investment cost for electrochemical energy storage,/>For the running cost of the system,/>Risk loss of the system due to insufficient flexible resources;
the expression of risk loss of the computing system due to insufficient flexible resources is as follows:
，
In the method, in the process of the invention, 、/>Are all auxiliary variables for calculating CvaR values,/>For confidence level,/>、/>Lost cost due to insufficient up and down flexibility resources per unit,/>For the power generated at the t moment of all the generator sets,/>Is the probability of occurrence of a typical scene n,/>For a typical scenario,/>Providing aggregate for downregulation of flexible resources,/>Supplying aggregate for up-regulation of flexible resources,/>For the load at time t,/>，/>For/>Or/>，/>Indicating an insufficient degree of flexibility;
And the solving module 240 is configured to perform iterative solving on the energy storage capacity configuration inner layer model and the energy storage capacity configuration outer layer model according to the improved quantum particle swarm algorithm, so as to obtain a final energy storage capacity configuration scheme.
It should be understood that the modules depicted in fig. 2 correspond to the various steps in the method described with reference to fig. 1. Thus, the operations and features described above for the method and the corresponding technical effects are equally applicable to the modules in fig. 2, and are not described here again.
In other embodiments, embodiments of the present invention further provide a computer readable storage medium having stored thereon a computer program, where the program instructions, when executed by a processor, cause the processor to perform the energy storage capacity configuration double-layer optimization method taking into account the flexibility requirements in any of the above-described method embodiments;
as one embodiment, the computer-readable storage medium of the present invention stores computer-executable instructions configured to:
under a preset first constraint condition, constructing an energy storage capacity configuration inner layer model for a first objective function with the minimum running cost of flexible resource output of a short time scale, wherein the expression of the first objective function is as follows:
，
In the method, in the process of the invention, Cost is adjusted for flexible resources,/>To adjust the unit start-stop cost of the hydropower station i,To adjust the start-stop capacity of the hydropower station i at the time t,/>Load cost per unit cut for demand response load unit i,/>Cut load quantity at time t for demand response load i,/>For the increased output cost of the demand response load unit i,/>For the increased output of the demand response load unit i,/>To adjust the unit power generation cost of the hydropower station i,To adjust the actual output of the hydropower station i at the time t,/>Respectively an adjustable hydropower station and a demand response load,/>Is the number of time periods;
calculating the flexible resource adequacy under a short time scale, and determining a second constraint condition according to the flexible resource adequacy;
under the second constraint condition, constructing an energy storage capacity configuration outer layer model by using a second objective function with the minimum running cost of the energy storage system, wherein the expression of the second objective function is as follows:
，
In the method, in the process of the invention, For the investment of the system and the operation cost,/>To adjust the investment cost of the hydroelectric generating set,/>Investment cost for electrochemical energy storage,/>For the running cost of the system,/>Risk loss of the system due to insufficient flexible resources;
the expression of risk loss of the computing system due to insufficient flexible resources is as follows:
，
In the method, in the process of the invention, 、/>Are all auxiliary variables for calculating CvaR values,/>For confidence level,/>、/>Lost cost due to insufficient up and down flexibility resources per unit,/>For the power generated at the t moment of all the generator sets,/>Is the probability of occurrence of a typical scene n,/>For a typical scenario,/>Providing aggregate for downregulation of flexible resources,/>Supplying aggregate for up-regulation of flexible resources,/>For the load at time t,/>，/>For/>Or/>，/>Indicating an insufficient degree of flexibility;
and carrying out iterative solution on the energy storage capacity configuration inner layer model and the energy storage capacity configuration outer layer model according to an improved quantum particle swarm algorithm to obtain a final energy storage capacity configuration scheme.
The computer readable storage medium may include a storage program area and a storage data area, wherein the storage program area may store an operating system, at least one application program required for a function; the storage data area may store data created according to the use of the energy storage capacity configuration double-layer optimization system considering flexibility requirements, and the like. In addition, the computer-readable storage medium may include high-speed random access memory, and may also include memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid-state storage device. In some embodiments, the computer readable storage medium optionally includes memory remotely located with respect to the processor, which may be connected via a network to a storage capacity configuration dual-layer optimization system that takes into account flexibility requirements. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
Fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present invention, as shown in fig. 3, where the device includes: a processor 310 and a memory 320. The electronic device may further include: an input device 330 and an output device 340. The processor 310, memory 320, input device 330, and output device 340 may be connected by a bus or other means, for example in fig. 3. Memory 320 is the computer-readable storage medium described above. The processor 310 executes various functional applications of the server and data processing by running non-volatile software programs, instructions and modules stored in the memory 320, i.e. implementing the energy storage capacity configuration double-layer optimization method of the above-described method embodiments taking into account flexibility requirements. The input device 330 may receive input numeric or character information and generate key signal inputs related to user settings and function control of the energy storage capacity configuration dual-layer optimization system taking into account flexibility requirements. The output device 340 may include a display device such as a display screen.
The electronic equipment can execute the method provided by the embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method. Technical details not described in detail in this embodiment may be found in the methods provided in the embodiments of the present invention.
As an embodiment, the electronic device is applied to a dual-layer optimization system for energy storage capacity configuration considering flexibility requirement, and is used for a client, and includes: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executable by the at least one processor to enable the at least one processor to:
under a preset first constraint condition, constructing an energy storage capacity configuration inner layer model for a first objective function with the minimum running cost of flexible resource output of a short time scale, wherein the expression of the first objective function is as follows:
，
In the method, in the process of the invention, Cost is adjusted for flexible resources,/>For the unit start-stop cost of the adjustable hydropower station i,/>To adjust the start-stop capacity of the hydropower station i at the time t,/>Load cost per cut for demand response load unit i,Cut load quantity at time t for demand response load i,/>For the increased output cost of the demand response load unit i,For the increased output of the demand response load unit i,/>For the unit power generation cost of the adjustable hydropower station i,/>To adjust the actual output of the hydropower station i at the time t,/>Respectively an adjustable hydropower station and a demand response load,/>Is the number of time periods;
calculating the flexible resource adequacy under a short time scale, and determining a second constraint condition according to the flexible resource adequacy;
under the second constraint condition, constructing an energy storage capacity configuration outer layer model by using a second objective function with the minimum running cost of the energy storage system, wherein the expression of the second objective function is as follows:
，
In the method, in the process of the invention, For the investment of the system and the operation cost,/>To adjust the investment cost of the hydroelectric generating set,/>Investment cost for electrochemical energy storage,/>For the running cost of the system,/>Risk loss of the system due to insufficient flexible resources;
the expression of risk loss of the computing system due to insufficient flexible resources is as follows:
，
In the method, in the process of the invention, 、/>Are all auxiliary variables for calculating CvaR values,/>For confidence level,/>、/>Lost cost due to insufficient up and down flexibility resources per unit,/>For the power generated at the t moment of all the generator sets,/>Is the probability of occurrence of a typical scene n,/>For a typical scenario,/>Providing aggregate for downregulation of flexible resources,/>Supplying aggregate for up-regulation of flexible resources,/>For the load at time t,/>，/>For/>Or/>，/>Indicating an insufficient degree of flexibility;
and carrying out iterative solution on the energy storage capacity configuration inner layer model and the energy storage capacity configuration outer layer model according to an improved quantum particle swarm algorithm to obtain a final energy storage capacity configuration scheme.
From the above description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course may be implemented by means of hardware. Based on such understanding, the foregoing technical solutions may be embodied essentially or in part in the form of a software product, which may be stored in a computer-readable storage medium, such as a ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to perform the various embodiments or methods of some parts of the embodiments.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (8)
1. The energy storage capacity configuration double-layer optimization method taking the flexibility requirement into consideration is characterized by comprising the following steps of:
under a preset first constraint condition, constructing an energy storage capacity configuration inner layer model for a first objective function with the minimum running cost of flexible resource output of a short time scale, wherein the expression of the first objective function is as follows:
，
In the method, in the process of the invention, Cost is adjusted for flexible resources,/>For the unit start-stop cost of the adjustable hydropower station i,/>To adjust the start-stop capacity of the hydropower station i at the time t,/>Load cost per unit cut for demand response load unit i,/>Cut load quantity at time t for demand response load i,/>For the increased output cost of the demand response load unit i,/>For the increased output of the demand response load unit i,/>For the unit power generation cost of the adjustable hydropower station i,/>To adjust the actual output of the hydropower station i at the time t,/>Respectively an adjustable hydropower station and a demand response load,/>Is the number of time periods;
calculating the flexible resource adequacy under a short time scale, and determining a second constraint condition according to the flexible resource adequacy;
under the second constraint condition, constructing an energy storage capacity configuration outer layer model by using a second objective function with the minimum running cost of the energy storage system, wherein the expression of the second objective function is as follows:
，
In the method, in the process of the invention, For the investment of the system and the operation cost,/>To adjust the investment cost of the hydroelectric generating set,/>Investment cost for electrochemical energy storage,/>For the running cost of the system,/>Risk loss of the system due to insufficient flexible resources;
the expression of risk loss of the computing system due to insufficient flexible resources is as follows:
，
In the method, in the process of the invention, 、/>Are all auxiliary variables for calculating CvaR values,/>For confidence level,/>、/>Lost cost due to insufficient up and down flexibility resources per unit,/>For the power generated at the t moment of all the generator sets,/>Is the probability of occurrence of a typical scene n,/>For a typical scenario,/>Providing aggregate for downregulation of flexible resources,/>Supplying aggregate for up-regulation of flexible resources,/>For the load at time t,/>，/>For/>Or (b)，/>Indicating an insufficient degree of flexibility;
and carrying out iterative solution on the energy storage capacity configuration inner layer model and the energy storage capacity configuration outer layer model according to an improved quantum particle swarm algorithm to obtain a final energy storage capacity configuration scheme.
2. The energy storage capacity configuration double-layer optimization method considering flexibility requirements according to claim 1, wherein the first constraint conditions comprise power generation-load balance constraint, electrochemical energy storage charge-discharge constraint and electric quantity constraint;
The power generation-load balancing constraint expression is:
，
In the method, in the process of the invention, For the power generated at the t moment of all the generator sets,/>For the discharge power of the electrochemical energy storage i at time t,/>For the load at time t,/>For the charging power of the electrochemical energy storage i at time t,/>The number of the electrochemical energy storage;
The expression of the electrochemical energy storage charge-discharge constraint is as follows:
，
，
，
In the method, in the process of the invention, For the minimum discharge power of the electrochemical energy storage i,/>For the discharge power of the electrochemical energy storage i at the time t,/>For the shutdown state of the electrochemical energy storage i at time t,/>For maximum discharge power of electrochemical energy storage i,/>For the minimum charge power of the electrochemical energy storage i,/>For the electrochemical energy storage i at the charging power at time t,For the starting state of the electrochemical energy storage i at time t,/>Maximum charging power for electrochemical energy storage i;
The expression of the electric quantity constraint is as follows:
，
，
，
In the method, in the process of the invention, For the minimum output of the adjustable hydropower i,/>For the maximum output of the adjustable hydropower i,/>To adjust the lower gradient rate upper limit of the hydropowerTo adjust the upper limit of the ascending climbing rate of the hydropowerTo adjust the actual output of the hydropower station i at the time t-1,/>Is the number of month period,/>The monthly maximum power generation amount of the adjustable hydropower station i.
3. The energy storage capacity configuration double-layer optimization method considering flexibility requirements according to claim 1, wherein the expression for calculating the flexibility resource adequacy under a short time scale is:
，
，
，
In the method, in the process of the invention, Is the probability of occurrence of a typical scene n,/>For a typical scenario,/>Providing aggregate for downregulation of flexible resources,/>Supplying aggregate for up-regulation of flexible resources,/>For the load at time t,/>For the power generated at the t moment of all the generator sets,/>For the flexible supply of adjustable hydropower t-time upregulation,/>Flexible supply level for up-regulation of electrochemical energy storage t-time,/>Flexible supply level up-regulated for demand response load t moment,/>For the flexible supply of adjustable hydropower t-time downregulation,/>Flexible supply level for down-regulation of electrochemical energy storage t-time,/>The flexible supply level is adjusted down for demand response load t.
4. The energy storage capacity configuration double-layer optimization method considering flexibility requirements according to claim 1, wherein the expression for calculating the investment cost of the adjustable hydroelectric generating set is:
，
In the method, in the process of the invention, For the total number of the adjustable hydroelectric generating sets,/>For the unit investment cost of the adjustable hydroelectric generating set i,/>The capacity of the hydroelectric generating set i can be adjusted;
the expression for calculating the investment cost of electrochemical energy storage is:
，
In the method, in the process of the invention, For the total number of electrochemical energy storage,/>Per unit investment cost for electrochemical energy storage i,/>Is the capacity of the electrochemical energy storage i.
5. The method for energy storage capacity configuration double-layer optimization considering flexibility requirements according to claim 1, wherein the performing iterative solution on the energy storage capacity configuration inner layer model and the energy storage capacity configuration outer layer model according to the improved quantum particle swarm algorithm to obtain a final energy storage capacity configuration scheme comprises:
Setting random initial positions and speeds of particle swarms, namely positions and capacities of flexible resources;
inputting a first objective function and a first constraint condition of an energy storage capacity configuration inner layer model, and optimizing flexible resource output in each period;
Feeding back an optimization result of the flexible resource output to an energy storage capacity configuration outer layer model, calculating a second objective function and a second constraint condition of the energy storage capacity configuration outer layer model, and optimizing the position and capacity of the flexible resource;
calculating an fitness function and updating a particle swarm;
the excellent particle chaos search of 20% of the adaptability in the particle swarm is carried out, and the historical optimal speed and position are updated;
performing chaotic search on the rest particles, and updating the historical optimal speed and position of the rest particles;
Calculating local suction points of particles and population average optimal positions, and updating the positions of the particles;
and if the convergence condition is met, obtaining a final energy storage capacity configuration scheme.
6. A dual-layer optimization system for energy storage capacity configuration taking into account flexibility requirements, comprising:
The first construction module is configured to construct an energy storage capacity configuration inner layer model for a first objective function with the minimum running cost of flexible resource output of a short time scale under a preset first constraint condition, wherein the expression of the first objective function is as follows:
，
In the method, in the process of the invention, Cost is adjusted for flexible resources,/>For the unit start-stop cost of the adjustable hydropower station i,/>To adjust the start-stop capacity of the hydropower station i at the time t,/>Load cost per unit cut for demand response load unit i,/>Cut load quantity at time t for demand response load i,/>For the increased output cost of the demand response load unit i,/>For the increased output of the demand response load unit i,/>For the unit power generation cost of the adjustable hydropower station i,/>To adjust the actual output of the hydropower station i at the time t,/>Respectively an adjustable hydropower station and a demand response load,/>Is the number of time periods;
the computing module is configured to compute the flexible resource adequacy under a short time scale and determine a second constraint condition according to the flexible resource adequacy;
the second construction module is configured to construct an energy storage capacity configuration outer layer model for a second objective function with the minimum energy storage system operation cost under the second constraint condition, wherein the expression of the second objective function is as follows:
，
In the method, in the process of the invention, For the investment of the system and the operation cost,/>To adjust the investment cost of the hydroelectric generating set,/>Investment cost for electrochemical energy storage,/>For the running cost of the system,/>Risk loss of the system due to insufficient flexible resources;
the expression of risk loss of the computing system due to insufficient flexible resources is as follows:
，
In the method, in the process of the invention, 、/>Are all auxiliary variables for calculating CvaR values,/>For confidence level,/>、/>Lost cost due to insufficient up and down flexibility resources per unit,/>For the power generated at the t moment of all the generator sets,/>Is the probability of occurrence of a typical scene n,/>For a typical scenario,/>Providing aggregate for downregulation of flexible resources,/>Supplying aggregate for up-regulation of flexible resources,/>For the load at time t,/>，/>For/>Or (b)，/>Indicating an insufficient degree of flexibility;
And the solving module is configured to carry out iterative solving on the energy storage capacity configuration inner layer model and the energy storage capacity configuration outer layer model according to an improved quantum particle swarm algorithm to obtain a final energy storage capacity configuration scheme.
7. An electronic device, comprising: at least one processor, and a memory communicatively coupled to the at least one processor, wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1 to 5.
8. A computer readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the method of any one of claims 1 to 5.
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Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104779611A (en) * | 2015-03-23 | 2015-07-15 | 南京邮电大学 | Economic dispatch method for micro grid based on centralized and distributed double-layer optimization strategy |
| CN107154407A (en) * | 2017-05-17 | 2017-09-12 | 厦门天马微电子有限公司 | Laminated film transistor device and its manufacture method, display panel and display device |
| CN109728001A (en) * | 2019-01-02 | 2019-05-07 | 京东方科技集团股份有限公司 | A kind of array substrate and preparation method thereof, display panel |
| WO2019196375A1 (en) * | 2018-04-13 | 2019-10-17 | 华南理工大学 | Demand side response-based microgrid optimal unit and time-of-use electricity price optimization method |
| CN112016747A (en) * | 2020-08-26 | 2020-12-01 | 国网四川省电力公司经济技术研究院 | Optimization method suitable for source-load-storage flexible resource overall planning and operation |
| CN113224800A (en) * | 2021-04-29 | 2021-08-06 | 广东电网有限责任公司 | Service life constraint considered and flexible energy storage optimized scheduling method |
| CN115001035A (en) * | 2022-05-31 | 2022-09-02 | 三峡大学 | Power system multi-time scale optimization method considering flexibility requirement |
| CN115313519A (en) * | 2022-09-15 | 2022-11-08 | 广东电网有限责任公司 | Power distribution network energy storage optimal configuration method, device, equipment and storage medium |
| CN115345350A (en) * | 2022-07-14 | 2022-11-15 | 国网宁夏电力有限公司经济技术研究院 | Alternating current-direct current hybrid power distribution network planning method, medium and system |
| CN115411777A (en) * | 2022-10-08 | 2022-11-29 | 国网四川省电力公司经济技术研究院 | Power distribution network flexibility evaluation and resource allocation method and system |
| CN116050576A (en) * | 2022-12-02 | 2023-05-02 | 国网山东省电力公司营销服务中心(计量中心) | Flexible resource coordination optimization method and system for active power distribution network |
| CN116911695A (en) * | 2023-09-12 | 2023-10-20 | 北京工业大学 | Flexible resource adequacy evaluation method and device |
| WO2023240864A1 (en) * | 2022-06-16 | 2023-12-21 | 国网江苏省电力有限公司经济技术研究院 | Improved particle swarm algorithm-based stepped carbon emission trading mechanism parameter optimization method |
| CN117526335A (en) * | 2023-10-17 | 2024-02-06 | 云南电网有限责任公司 | Power system source network charge storage planning method considering multi-element flexible resource cooperation |
| CN117691602A (en) * | 2023-10-25 | 2024-03-12 | 国家电网有限公司华中分部 | Source network load storage collaborative planning method and system considering flexibility and economy |
-
2024
- 2024-04-08 CN CN202410411890.9A patent/CN118014164B/en active Active
Patent Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104779611A (en) * | 2015-03-23 | 2015-07-15 | 南京邮电大学 | Economic dispatch method for micro grid based on centralized and distributed double-layer optimization strategy |
| CN107154407A (en) * | 2017-05-17 | 2017-09-12 | 厦门天马微电子有限公司 | Laminated film transistor device and its manufacture method, display panel and display device |
| WO2019196375A1 (en) * | 2018-04-13 | 2019-10-17 | 华南理工大学 | Demand side response-based microgrid optimal unit and time-of-use electricity price optimization method |
| CN109728001A (en) * | 2019-01-02 | 2019-05-07 | 京东方科技集团股份有限公司 | A kind of array substrate and preparation method thereof, display panel |
| CN112016747A (en) * | 2020-08-26 | 2020-12-01 | 国网四川省电力公司经济技术研究院 | Optimization method suitable for source-load-storage flexible resource overall planning and operation |
| CN113224800A (en) * | 2021-04-29 | 2021-08-06 | 广东电网有限责任公司 | Service life constraint considered and flexible energy storage optimized scheduling method |
| CN115001035A (en) * | 2022-05-31 | 2022-09-02 | 三峡大学 | Power system multi-time scale optimization method considering flexibility requirement |
| WO2023240864A1 (en) * | 2022-06-16 | 2023-12-21 | 国网江苏省电力有限公司经济技术研究院 | Improved particle swarm algorithm-based stepped carbon emission trading mechanism parameter optimization method |
| CN115345350A (en) * | 2022-07-14 | 2022-11-15 | 国网宁夏电力有限公司经济技术研究院 | Alternating current-direct current hybrid power distribution network planning method, medium and system |
| CN115313519A (en) * | 2022-09-15 | 2022-11-08 | 广东电网有限责任公司 | Power distribution network energy storage optimal configuration method, device, equipment and storage medium |
| CN115411777A (en) * | 2022-10-08 | 2022-11-29 | 国网四川省电力公司经济技术研究院 | Power distribution network flexibility evaluation and resource allocation method and system |
| CN116050576A (en) * | 2022-12-02 | 2023-05-02 | 国网山东省电力公司营销服务中心(计量中心) | Flexible resource coordination optimization method and system for active power distribution network |
| CN116911695A (en) * | 2023-09-12 | 2023-10-20 | 北京工业大学 | Flexible resource adequacy evaluation method and device |
| CN117526335A (en) * | 2023-10-17 | 2024-02-06 | 云南电网有限责任公司 | Power system source network charge storage planning method considering multi-element flexible resource cooperation |
| CN117691602A (en) * | 2023-10-25 | 2024-03-12 | 国家电网有限公司华中分部 | Source network load storage collaborative planning method and system considering flexibility and economy |
Non-Patent Citations (1)
| Title |
|---|
| 李海波;鲁宗相;乔颖;: "源荷储一体化的广义灵活电源双层统筹规划", 电力系统自动化, no. 21, 10 November 2017 (2017-11-10) * |
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