Nothing Special   »   [go: up one dir, main page]

CN113705863B - Method and device for determining capacity commissioning decision scheme and computer equipment - Google Patents

Method and device for determining capacity commissioning decision scheme and computer equipment Download PDF

Info

Publication number
CN113705863B
CN113705863B CN202110923802.XA CN202110923802A CN113705863B CN 113705863 B CN113705863 B CN 113705863B CN 202110923802 A CN202110923802 A CN 202110923802A CN 113705863 B CN113705863 B CN 113705863B
Authority
CN
China
Prior art keywords
capacity
commissioning
cost
resource
transmission line
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN202110923802.XA
Other languages
Chinese (zh)
Other versions
CN113705863A (en
Inventor
尚楠
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Energy Development Research Institute of China Southern Power Grid Co Ltd
Original Assignee
Energy Development Research Institute of China Southern Power Grid Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Energy Development Research Institute of China Southern Power Grid Co Ltd filed Critical Energy Development Research Institute of China Southern Power Grid Co Ltd
Priority to CN202110923802.XA priority Critical patent/CN113705863B/en
Publication of CN113705863A publication Critical patent/CN113705863A/en
Application granted granted Critical
Publication of CN113705863B publication Critical patent/CN113705863B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • 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"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/12—Computing arrangements based on biological models using genetic models
    • G06N3/126—Evolutionary algorithms, e.g. genetic algorithms or genetic programming
    • 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
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0637—Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals
    • 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
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Landscapes

  • Engineering & Computer Science (AREA)
  • Business, Economics & Management (AREA)
  • Physics & Mathematics (AREA)
  • Human Resources & Organizations (AREA)
  • Economics (AREA)
  • Health & Medical Sciences (AREA)
  • Theoretical Computer Science (AREA)
  • Strategic Management (AREA)
  • General Physics & Mathematics (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Marketing (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Tourism & Hospitality (AREA)
  • Biophysics (AREA)
  • General Business, Economics & Management (AREA)
  • General Health & Medical Sciences (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Operations Research (AREA)
  • Quality & Reliability (AREA)
  • Evolutionary Biology (AREA)
  • Game Theory and Decision Science (AREA)
  • Educational Administration (AREA)
  • Development Economics (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Genetics & Genomics (AREA)
  • Evolutionary Computation (AREA)
  • Water Supply & Treatment (AREA)
  • Physiology (AREA)
  • Public Health (AREA)
  • Artificial Intelligence (AREA)
  • Biomedical Technology (AREA)
  • Computational Linguistics (AREA)
  • Data Mining & Analysis (AREA)
  • Primary Health Care (AREA)
  • Molecular Biology (AREA)
  • Computing Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Mathematical Physics (AREA)
  • Software Systems (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The application relates to a capacity commissioning decision scheme determination method, a capacity commissioning decision scheme determination device and computer equipment. The method comprises the following steps: determining a capacity commissioning parameter; the capacity commissioning parameters comprise commissioning cost of a transmission line unit length, annual discount rate of capacity resources, operation age of the capacity resources and power generation output conversion coefficients of unit capacity of the corresponding capacity resources; based on the capacity commissioning parameters, the commissioning state of the transmission line and the commissioning capacity of the capacity resource are taken as variables, and the minimum total cost commissioning cost is taken as an optimization target to construct an optimization function; and carrying out optimization solution on the optimization function based on a preset iterative optimization mode to obtain an optimal commissioning combination of the capacity resource and the transmission line, and determining a capacity commissioning decision scheme based on the commissioning combination. The method can avoid the over compensation of capacity resources.

Description

Method and device for determining capacity commissioning decision scheme and computer equipment
Technical Field
The present application relates to the field of power supply planning technologies, and in particular, to a method and an apparatus for determining a capacity commissioning decision scheme, and a computer device.
Background
Coordination between energy conversion, stranded cost recovery, and stable power supply guarantees presents new challenges for traditional generation capacity deployment mechanisms. Currently, reliability pricing capacity market mechanisms have been adopted to address this challenge. However, although this method reduces the commissioning cost by taking into account both the potential revenue of the actual operation of the power generation capacity and the cost that may be incurred by the transmission line commissioning, this method fails to take into account the potential revenue of the capacity resource in actual operation during the capacity commissioning process, resulting in "overcompensation" of the capacity resource.
Disclosure of Invention
In view of the foregoing, there is a need to provide a capacity commissioning decision scheme determination method, apparatus and computer device that can avoid "overcompensation" for capacity resources.
A capacity commissioning decision scheme determination method, the method comprising:
determining a capacity commissioning parameter; the capacity commissioning parameters comprise commissioning cost of a transmission line unit length, annual discount rate of capacity resources, operation age of the capacity resources and power generation output conversion coefficients of unit capacity of the corresponding capacity resources;
based on the capacity commissioning parameters, building an optimization function by taking the commissioning state of the transmission line and the commissioning capacity of the capacity resource as variables and the minimum total cost commissioning cost as an optimization target;
and carrying out optimization solution on the optimization function based on a preset iterative optimization mode to obtain an optimal commissioning combination of the capacity resource and the transmission line, and determining a capacity commissioning decision scheme based on the optimal commissioning combination.
A capacity commissioning decision scheme determination apparatus, the apparatus comprising a determination module, a first processing module and a second processing module, wherein:
the determining module is used for determining a capacity commissioning parameter; the capacity commissioning parameters comprise commissioning cost of a transmission line unit length, annual discount rate of capacity resources, operation age of the capacity resources and power generation output conversion coefficients of unit capacity of the corresponding capacity resources;
the first processing module is used for constructing an optimization function by taking the commissioning state of the transmission line and the commissioning capacity of the capacity resource as variables and the minimum total cost commissioning cost as an optimization target on the basis of the capacity commissioning parameter;
and the second processing module is used for carrying out optimization solution on the optimization function based on a preset iterative optimization mode to obtain the optimal commissioning combination of the capacity resource and the transmission line, and determining a capacity commissioning decision scheme based on the optimal commissioning combination.
A computer device comprising a memory storing a computer program and a processor implementing the following steps when the computer program is executed:
determining a capacity commissioning parameter; the capacity commissioning parameters comprise commissioning cost of a transmission line unit length, annual discount rate of capacity resources, operation age of the capacity resources and power generation output conversion coefficients of unit capacity of the corresponding capacity resources;
based on the capacity commissioning parameters, building an optimization function by taking the commissioning state of the transmission line and the commissioning capacity of the capacity resource as variables and the minimum total cost commissioning cost as an optimization target;
and carrying out optimization solution on the optimization function based on a preset iterative optimization mode to obtain an optimal commissioning combination of the capacity resource and the transmission line, and determining a capacity commissioning decision scheme based on the optimal commissioning combination.
A computer-readable storage medium, on which a computer program is stored which, when executed by a processor, carries out the steps of:
determining a capacity commissioning parameter; the capacity commissioning parameters comprise commissioning cost of a transmission line unit length, annual discount rate of capacity resources, operation age of the capacity resources and power generation output conversion coefficients of unit capacity of the corresponding capacity resources;
based on the capacity commissioning parameters, building an optimization function by taking the commissioning state of the transmission line and the commissioning capacity of the capacity resource as variables and the minimum total cost commissioning cost as an optimization target;
and carrying out optimization solution on the optimization function based on a preset iterative optimization mode to obtain an optimal commissioning combination of the capacity resource and the transmission line, and determining a capacity commissioning decision scheme based on the optimal commissioning combination.
On the basis of considering energy structure constraint and system operation, the capacity commissioning decision scheme determining method, the capacity commissioning decision scheme determining device, the computer equipment and the storage medium construct an optimization function based on the determined capacity commissioning parameters, with commissioning states of transmission lines and commissioning capacity of capacity resources as variables and with minimized total cost commissioning cost as an optimization target, optimally solve the optimization function based on a preset iterative optimization mode, and determine the capacity commissioning decision scheme based on the obtained optimal commissioning combination. On one hand, the possible benefit of capacity resources in actual operation, the potential benefit of the actual operation of power generation capacity and the cost possibly generated by the transmission line investment are considered, so that the capacity decision investment and the system operation can be better connected. On the other hand, the minimum total cost investment cost is taken as an optimization target, so that the energy installation structure can avoid the overcompensation of capacity resources by using a low-carbon clean transformation regulation and control target, and the capacity investment decision efficiency is improved.
Drawings
FIG. 1 is a diagram of an application environment for a method for capacity projection decision-making scheme determination in one embodiment;
FIG. 2 is a schematic flow chart diagram illustrating a method for capacity commissioning decision scheme determination in one embodiment;
FIG. 3 is a schematic flow chart diagram illustrating an overall process for capacity commissioning decision scheme determination in one embodiment;
FIG. 4 is a block diagram showing the structure of a capacity planning decision making means in one embodiment;
FIG. 5 is a diagram illustrating an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of and not restrictive on the broad application.
The method for determining the capacity commissioning decision scheme can be applied to an application environment shown in fig. 1. Wherein the terminal 102 communicates with the server 104 via a network. When determining the capacity deployment decision scheme, firstly, determining capacity deployment parameters by the server 104 based on the typical cost standard of the power transmission and transformation project transmitted by the terminal 102; the capacity investment parameters comprise investment cost of a transmission line unit length, annual discount rate of capacity resources, operation years of the capacity resources and power generation output conversion coefficients of unit capacity of the corresponding capacity resources; secondly, the server 104 builds an optimization function based on the capacity commissioning parameters, with the commissioning state of the transmission line and the commissioning capacity of the capacity resources as variables and with the minimum total cost commissioning cost as an optimization target; finally, the server 104 performs optimization solution on the optimization function based on a preset iterative optimization mode to obtain an optimal commissioning combination of the capacity resource and the transmission line, and determines a capacity commissioning decision scheme based on the optimal commissioning combination.
It should be noted that the terminal 102 may be, but is not limited to, various personal computers, notebook computers, smart phones, tablet computers, and portable wearable devices, and the server 104 may be implemented by an independent server or a server cluster composed of a plurality of servers.
In one embodiment, as shown in fig. 2, a capacity commissioning decision scheme determining method is provided, which is described by taking the method as an example applied to the server in fig. 1, and includes the following steps:
step S202, determining a capacity setting parameter; the capacity investment parameters comprise investment cost of a transmission line unit length, annual discount rate of capacity resources, operation years of the capacity resources and power generation output conversion coefficients of the unit capacity of the corresponding capacity resources.
Specifically, determining a capacity commissioning parameter includes: determining corresponding physical environment parameters of the power system based on a preset typical construction cost standard of the power transmission and transformation project, and determining the construction cost of a transmission line in unit length according to the physical environment parameters of the power system; the power system physical environment parameters comprise at least one of voltage grade, wire type adopted by the power system, meteorological conditions and altitude; determining corresponding asset assessment discount rate based on typical cost standard of power transmission and transformation project, and determining annual discount rate of capacity resource according to the asset assessment discount rate; determining the service life of corresponding to-be-put-into-operation generating capacity resources based on the typical cost standard of the power transmission and transformation project, and determining the operation age limit of the capacity resources according to the service life of the to-be-put-into-operation generating capacity resources; based on the typical cost standard of the power transmission and transformation project, the ratio of the maximum output of the capacity resource in the historical operating year to the installed capacity is determined, and the power generation output conversion coefficient of the unit capacity of the corresponding capacity resource is determined according to the ratio of the maximum output of the capacity resource in the historical operating year to the installed capacity.
In one embodiment, (1) after determining the corresponding physical environmental parameters of the power system, the server calculates the construction cost per unit length of the transmission line based on a table look-up manner. It should be noted that the cost of the project is an average estimated value. (2) And the server approximately considers that the annual discount rate of the capacity resource is equal to the asset evaluation discount rate, and approximately considers that the operation age limit of the capacity resource is equal to the service life of the capacity resource to be built. It should be noted that, in general, the annual discount rate of the capacity resource is 8%, and of course, the annual discount rate may be different in different implementation scenarios, and this is not limited in the embodiment of the present application. (3) The generated output conversion coefficient is further calculated based on historical data, and in one embodiment, the server determines the annual maximum output average value of the capacity resource in nearly three years, and the ratio of the annual maximum output average value to the installed capacity of the capacity resource is the generated output conversion coefficient.
And step S204, based on the capacity commissioning parameters, building an optimization function by taking the commissioning state of the transmission line and the commissioning capacity of the capacity resource as variables and the minimized total cost commissioning cost as an optimization target.
Specifically, the total cost investment cost is determined according to the investment cost of the capacity resource, the investment cost of the transmission line and the operation cost of the capacity resource; the optimization function comprises an upper layer objective function taking the minimum full cost investment cost as an optimization target and a lower layer objective function taking the minimum running cost of the capacity resource as an optimization target.
And S206, carrying out optimization solution on the optimization function based on a preset iterative optimization mode to obtain the optimal commissioning combination of the capacity resource and the transmission line, and determining a capacity commissioning decision scheme based on the optimal commissioning combination.
Specifically, the optimization function is optimized and solved based on an iterative optimization mode to obtain an optimal commissioning combination of the capacity resource and the transmission line, and the method comprises the following steps: and carrying out optimization solution on the optimization function based on a genetic algorithm, and outputting the obtained optimal commissioning combination of the capacity resource and the transmission line as an optimal solution, wherein in the process of optimization solution, the method comprises the following steps: determining an initial population, and calculating the fitness of each individual in the initial population in an individual evaluation mode; based on the fitness of each individual in the initial population, selecting optimized individuals; based on preset crossover operators and mutation operators, carrying out pairing crossover and compiling operation on each selected optimized individual to obtain a next generation group; and when the fitness of the corresponding individual in the next generation group meets the preset condition, outputting the corresponding optimal solution, otherwise, when the next iteration is started, taking the next generation group as the initial group in the next iteration, returning to the step of calculating the fitness of each individual in the initial group by an individual evaluation mode, and continuously executing the step until the corresponding optimal solution is output, and ending the iteration cycle.
In the capacity commissioning decision scheme determining method, on the basis of considering energy structure constraint and system operation, an optimization function is constructed on the basis of the determined capacity commissioning parameters, the commissioning state of a transmission line and the commissioning capacity of a capacity resource are taken as variables, the minimum total cost commissioning cost is taken as an optimization target, the optimization function is optimized and solved on the basis of a preset iterative optimization mode, and the capacity commissioning decision scheme is determined on the basis of the obtained optimal commissioning combination. On one hand, the possible benefit of capacity resources in actual operation is considered, and the potential benefit of the actual operation of the power generation capacity and the cost possibly generated by the transmission line investment are considered, so that the capacity decision investment and the system operation can be better connected. On the other hand, the minimum total cost investment cost is taken as an optimization target, so that the energy installation structure can avoid the overcompensation of capacity resources by using a low-carbon clean transformation regulation and control target, and the capacity investment decision efficiency is improved.
In one embodiment, the upper layer objective function is represented by the following formula:
Figure GDA0003604021880000061
wherein, PψThe cost is put into operation for the whole cost,
Figure GDA0003604021880000062
in order to establish the cost of the capacity resource,
Figure GDA0003604021880000063
as a transmission lineThe cost of the construction of the building is reduced,
Figure GDA0003604021880000064
operating costs for capacity resources; t is the minimum clearing period of capacity resource market operation, and T is the full period of capacity resource construction; omegaKInstall resource sets, Ω, for incrementsMFor inventory of installed resource sets, ΩLIs a line node set;
Figure GDA0003604021880000065
for the unit installed capacity price of the ith incremental capacity resource,
Figure GDA0003604021880000066
a unit installed capacity price for a kth inventory capacity resource;
Figure GDA0003604021880000067
the installed capacity of the ith incremental capacity resource,
Figure GDA0003604021880000068
installed capacity that is the kth inventory capacity resource;
Figure GDA0003604021880000069
a conversion factor of the annual capacity investment cost,
Figure GDA00036040218800000610
a conversion factor for annual transmission line investment cost;
Figure GDA00036040218800000611
for the construction costs of the transmission line between line nodes i-j,
Figure GDA00036040218800000612
for commissioning of transmission lines between line nodes i-j, LijIs the line length, k, of the transmission line between nodes i-jinFor annual discount rate of capacity resources, TCFor the operational age, T, of a capacity resourceLFor transmission linesAnd (5) the operating life.
Specifically, the server adopts a genetic algorithm in the process of optimizing the upper-layer objective function, and the capacity g is put into use by the power generation resourcesc,iAnd the state of the transmission line between the line nodes i-j
Figure GDA00036040218800000613
To optimize variables (
Figure GDA00036040218800000614
The parameters are Boolean variables and are used as network planning factors, and the parameters are introduced into the upper-layer objective function for further optimization), and the optimal total cost construction cost is obtained through multiple iterative optimization by taking the minimized total cost construction cost as an optimization target. It should be noted that the genetic algorithm is a method for searching an optimal solution by simulating a natural evolution process. The algorithm converts the solving process of the problem into the processes of crossover, variation and the like of chromosome genes in the similar biological evolution by a mathematical mode and by utilizing computer simulation operation. When a complex combined optimization problem is solved, a better optimization result can be obtained faster compared with some conventional optimization algorithms.
In the embodiment, on the basis of considering energy structure constraint and system operation, a network planning factor is introduced, and possible benefits of capacity resources in actual operation, potential benefits of actual operation of power generation capacity and possible cost of transmission line construction are considered, so that capacity decision construction and system operation can be better connected, and capacity construction decision efficiency is improved.
In one embodiment, the underlying objective function is represented by the following formula:
Figure GDA0003604021880000071
Figure GDA0003604021880000072
Figure GDA0003604021880000073
wherein, PE,tThe operation cost of the capacity resource in the t period is obtained;
Figure GDA0003604021880000074
the running price of the ith incremental capacity resource during the t period,
Figure GDA0003604021880000075
operating price of the kth stock capacity resource in the t period;
Figure GDA0003604021880000076
the power generation amount of the ith incremental capacity resource in the t period,
Figure GDA0003604021880000077
generating capacity of the kth stock capacity resource in a t period; etai,tThe maximum power generation output conversion coefficient, eta, of the ith incremental capacity resource installation level in the t periodk,tAnd (4) obtaining a maximum power generation output conversion system of the installed level of the kth stock capacity resource in the t period.
Specifically, in the process of optimizing the lower-layer objective function, the server adopts a genetic algorithm to calculate the operating output g of the ith power generation resourcee,i,tIn order to optimize variables, the optimal capacity resource operation cost is obtained through multiple iterative optimization by taking the minimized capacity resource operation cost as an optimization target.
In the embodiment, the optimal capacity resource operation cost is calculated through a genetic algorithm, the objective function value can be used as search information, the algorithm only uses the fitness function value to measure the individual goodness, derivation of the objective function value is not involved in the calculation process to obtain the differential, the calculation complexity can be effectively reduced, and the optimization efficiency is improved.
It should be understood that although the steps in the flowcharts of fig. 2 and 3 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least some of the steps in fig. 2 and 3 may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed in turn or alternately with other steps or at least some of the other steps.
In one embodiment, as shown in fig. 4, there is provided a capacity commissioning decision scheme determining apparatus 400, the apparatus 400 comprising a determining module 401, a first processing module 402 and a second processing module 403, wherein:
a determining module 401, configured to determine a capacity commissioning parameter; the capacity commissioning parameters comprise commissioning cost of a transmission line unit length, annual discount rate of capacity resources, operation age of the capacity resources and power generation output conversion coefficient of the unit capacity of the corresponding capacity resources.
The first processing module 402 is configured to construct an optimization function based on the capacity commissioning parameter, with the commissioning state of the transmission line and the commissioning capacity of the capacity resource as variables, and with the minimum total cost commissioning cost as an optimization target.
The second processing module 403 is configured to perform optimization solution on the optimization function based on a preset iterative optimization manner, to obtain an optimal commissioning combination of the capacity resource and the transmission line, and determine a capacity commissioning decision scheme based on the optimal commissioning combination.
In one embodiment, the determining module 401 is further configured to determine a corresponding physical environment parameter of the power system based on a preset typical cost standard of the power transmission and transformation project, and determine a construction cost per unit length of the transmission line according to the physical environment parameter of the power system; the power system physical environment parameters comprise at least one of voltage grade, wire type adopted by the power system, meteorological conditions and altitude; determining corresponding asset assessment discount rate based on typical cost standard of power transmission and transformation projects, and determining annual discount rate of capacity resources according to the asset assessment discount rate; determining the service life of corresponding to-be-put-into-operation generating capacity resources based on the typical cost standard of the power transmission and transformation project, and determining the operation age limit of the capacity resources according to the service life of the to-be-put-into-operation generating capacity resources; based on the typical cost standard of the power transmission and transformation project, the ratio of the maximum output of the capacity resource in the historical operating year to the installed capacity is determined, and the power generation output conversion coefficient of the unit capacity of the corresponding capacity resource is determined according to the ratio of the maximum output of the capacity resource in the historical operating year to the installed capacity.
In one embodiment, the first processing module 402 is further configured to determine a full cost investment cost according to an investment cost of the capacity resource, an investment cost of the transmission line, and an operation cost of the capacity resource; and determining an optimization function by using the minimized full-cost investment cost as an upper-layer objective function of the optimization target and using the minimized running cost of the capacity resource as a lower-layer objective function of the optimization target.
In one embodiment, the first processing module 402 is further configured to determine an upper layer objective function according to the following formula:
Figure GDA0003604021880000091
wherein, PψThe cost is put into operation for the whole cost,
Figure GDA0003604021880000092
in order to establish the cost of the capacity resource,
Figure GDA0003604021880000093
the cost of the commissioning of the transmission line,
Figure GDA0003604021880000094
operating costs for capacity resources; t is the minimum clearing period of capacity resource market operation, and T is the full period of capacity resource construction; omegaKInstalling resource collections for increments，ΩMSet of installed resources for inventory, ΩLIs a line node set;
Figure GDA0003604021880000095
for the unit installed capacity price of the ith incremental capacity resource,
Figure GDA0003604021880000096
a unit installed capacity price for a kth stock capacity resource;
Figure GDA0003604021880000097
the installed capacity of the ith incremental capacity resource,
Figure GDA0003604021880000098
installed capacity that is the kth inventory capacity resource;
Figure GDA0003604021880000099
a conversion factor of the cost is built for the annual capacity,
Figure GDA00036040218800000910
a conversion factor for annual transmission line investment cost;
Figure GDA00036040218800000911
for the construction costs of the transmission line between line nodes i-j,
Figure GDA00036040218800000912
for commissioning of transmission lines between line nodes i-j, LijIs the line length, k, of the transmission line between nodes i-jinFor annual discount rate of capacity resources, TCFor the operational age of a capacity resource, TLThe operational age of the transmission line.
In one embodiment, the first processing module 402 is further configured to determine the lower layer objective function according to the following formula:
Figure GDA00036040218800000913
Figure GDA00036040218800000914
Figure GDA0003604021880000101
wherein, PE,tThe operation cost of the capacity resource in the t period is obtained;
Figure GDA0003604021880000102
the running price of the ith incremental capacity resource during the t period,
Figure GDA0003604021880000103
operating price of the kth stock capacity resource in the t period;
Figure GDA0003604021880000104
the power generation amount of the ith incremental capacity resource in the t period,
Figure GDA0003604021880000105
generating capacity for the kth inventory capacity resource in a t period; etai,tThe maximum power generation output conversion coefficient, eta, of the ith incremental capacity resource installation level in the t periodk,tAnd (4) obtaining a maximum power generation output conversion system of the installed level of the kth stock capacity resource in the t period.
In one embodiment, the second processing module 402 is further configured to perform optimization solution on the optimization function based on a genetic algorithm, and output an optimal commissioning combination of the obtained capacity resource and the transmission line as an optimal solution, where in the process of the optimization solution, the optimization solution includes: determining an initial population, and calculating the fitness of each individual in the initial population in an individual evaluation mode; based on the fitness of each individual in the initial population, selecting optimized individuals; based on preset crossover operators and mutation operators, carrying out pairing crossover and compiling operation on each selected optimized individual to obtain a next generation group; and when the fitness of the corresponding individual in the next generation group meets the preset condition, outputting the corresponding optimal solution, otherwise, when the next iteration is started, taking the next generation group as the initial group in the next iteration, returning to the step of calculating the fitness of each individual in the initial group by an individual evaluation mode, and continuously executing the step until the corresponding optimal solution is output, and ending the iteration cycle.
The capacity commissioning decision-making scheme determining device is used for constructing an optimization function based on the determined capacity commissioning parameters, the commissioning state of a transmission line and the commissioning capacity of a capacity resource as variables and the minimized total cost commissioning cost as an optimization target on the basis of considering energy structure constraints and system operation, performing optimization solution on the optimization function based on a preset iterative optimization mode, and determining the capacity commissioning decision-making scheme based on the obtained optimal commissioning combination. On one hand, the possible benefit of capacity resources in actual operation is considered, and the potential benefit of the actual operation of the power generation capacity and the cost possibly generated by the transmission line investment are considered, so that the capacity decision investment and the system operation can be better connected. On the other hand, the minimum total cost investment cost is taken as an optimization target, so that the energy installation structure can avoid the overcompensation of capacity resources by using a low-carbon clean transformation regulation and control target, and the capacity investment decision efficiency is improved.
For specific limitations of the capacity planning decision scheme determining apparatus, reference may be made to the above limitations of the capacity planning decision scheme determining method, which will not be described herein again. The modules in the capacity planning decision-making scheme determination apparatus may be implemented in whole or in part by software, hardware, and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 5. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer device is used for storing capacity commissioning decision scheme data. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a capacity commissioning decision scheme determination method.
Those skilled in the art will appreciate that the architecture shown in fig. 5 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided, comprising a memory having a computer program stored therein and a processor that when executing the computer program performs the steps of: determining a capacity commissioning parameter; the capacity commissioning parameters comprise commissioning cost of a transmission line unit length, annual discount rate of capacity resources, operation age of the capacity resources and power generation output conversion coefficients of unit capacity of the corresponding capacity resources; based on the capacity commissioning parameters, the commissioning state of the transmission line and the commissioning capacity of the capacity resource are taken as variables, and the minimum total cost commissioning cost is taken as an optimization target to construct an optimization function; and carrying out optimization solution on the optimization function based on a preset iterative optimization mode to obtain an optimal commissioning combination of the capacity resource and the transmission line, and determining a capacity commissioning decision scheme based on the optimal commissioning combination.
In one embodiment, the processor when executing the computer program further performs the steps of: determining corresponding physical environment parameters of the power system based on a preset typical construction cost standard of the power transmission and transformation project, and determining the construction cost of a transmission line in unit length according to the physical environment parameters of the power system; the power system physical environment parameters comprise at least one of voltage grade, wire type adopted by the power system, meteorological conditions and altitude; determining corresponding asset assessment discount rate based on typical cost standard of power transmission and transformation project, and determining annual discount rate of capacity resource according to the asset assessment discount rate; determining the service life of corresponding to-be-put-into-operation generating capacity resources based on the typical cost standard of the power transmission and transformation project, and determining the operation age limit of the capacity resources according to the service life of the to-be-put-into-operation generating capacity resources; based on the typical cost standard of the power transmission and transformation project, the ratio of the maximum output of the capacity resource in the historical operating year to the installed capacity is determined, and the power generation output conversion coefficient of the unit capacity of the corresponding capacity resource is determined according to the ratio of the maximum output of the capacity resource in the historical operating year to the installed capacity.
In one embodiment, the processor, when executing the computer program, further performs the steps of: determining the total cost investment cost according to the investment cost of the capacity resource, the investment cost of the transmission line and the operation cost of the capacity resource; and determining an optimization function by using the minimized full-cost investment cost as an upper-layer objective function of the optimization target and using the minimized running cost of the capacity resource as a lower-layer objective function of the optimization target.
In one embodiment, the processor, when executing the computer program, further performs the steps of: determining an upper layer objective function according to the following formula:
Figure GDA0003604021880000121
wherein, PψThe cost is put into operation for the whole cost,
Figure GDA0003604021880000122
in order to establish the cost of the capacity resource,
Figure GDA0003604021880000123
the cost of the construction of the transmission line is increased,
Figure GDA0003604021880000124
operating costs for capacity resources; t is the minimum clearing period of capacity resource market operation, and T is the full period of capacity resource construction; omegaKInstall resource sets, Ω, for incrementsMFor inventory of installed resource sets, ΩLIs a line node set;
Figure GDA0003604021880000125
for the unit installed capacity price of the ith incremental capacity resource,
Figure GDA0003604021880000126
a unit installed capacity price for a kth stock capacity resource;
Figure GDA0003604021880000127
for the installed capacity of the ith incremental capacity resource,
Figure GDA0003604021880000128
installed capacity that is the kth inventory capacity resource;
Figure GDA0003604021880000129
a conversion factor of the annual capacity investment cost,
Figure GDA00036040218800001210
a conversion factor for annual transmission line investment costs;
Figure GDA00036040218800001211
for the construction costs of the transmission line between line nodes i-j,
Figure GDA0003604021880000131
for commissioning of transmission lines between line nodes i-j, LijIs the line length, κ, of the transmission line between nodes i-jinFor annual discount rate of capacity resources, TCFor the operational age, T, of a capacity resourceLIs the operational age of the transmission line.
In one embodiment, the processor, when executing the computer program, further performs the steps of: the lower layer objective function is determined according to the following formula:
Figure GDA0003604021880000132
Figure GDA0003604021880000133
Figure GDA0003604021880000134
wherein, PE,tThe operation cost of the capacity resource in the t period is obtained;
Figure GDA0003604021880000135
the running price of the ith incremental capacity resource during the t period,
Figure GDA0003604021880000136
operating price of the kth stock capacity resource in the t period;
Figure GDA0003604021880000137
the power generation amount of the ith incremental capacity resource in the t period,
Figure GDA0003604021880000138
generating capacity for the kth inventory capacity resource in a t period; etai,tThe maximum power generation output conversion coefficient, eta, of the ith incremental capacity resource installation level in the t periodk,tAnd (4) converting the maximum power generation output coefficient of the installed level of the kth inventory capacity resource in the t period.
In one embodiment, the processor, when executing the computer program, further performs the steps of: and carrying out optimization solution on the optimization function based on a genetic algorithm, and outputting the obtained optimal put-in combination of the capacity resource and the transmission line as an optimal solution, wherein in the process of optimization solution, the method comprises the following steps: determining an initial population, and calculating the fitness of each individual in the initial population in an individual evaluation mode; based on the fitness of each individual in the initial population, selecting optimized individuals; based on preset crossover operators and mutation operators, carrying out pairing crossover and compiling operation on each selected optimized individual to obtain a next generation group; and when the fitness of the corresponding individual in the next generation group meets the preset condition, outputting the corresponding optimal solution, otherwise, when the next iteration is started, taking the next generation group as the initial group in the next iteration, returning to the step of calculating the fitness of each individual in the initial group by an individual evaluation mode, and continuously executing the step until the corresponding optimal solution is output, and ending the iteration cycle.
The computer device constructs an optimization function based on the determined capacity commissioning parameters, the commissioning state of the transmission line and the commissioning capacity of the capacity resource as variables, and the minimized total cost commissioning cost as an optimization target, on the basis of consideration of energy structure constraints and system operation, optimizes and solves the optimization function based on a preset iterative optimization mode, and determines a capacity commissioning decision scheme based on the obtained optimal commissioning combination. On one hand, the possible benefit of capacity resources in actual operation is considered, and the potential benefit of the actual operation of the power generation capacity and the cost possibly generated by the transmission line investment are considered, so that the capacity decision investment and the system operation can be better connected. On the other hand, the minimum total cost investment cost is taken as an optimization target, so that the energy installation structure can use a low-carbon clean transformation regulation and control target, the capacity resource is prevented from being overcompensated, and the capacity investment decision efficiency is improved.
In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of: determining a capacity commissioning parameter; the capacity commissioning parameters comprise commissioning cost of a transmission line unit length, annual discount rate of capacity resources, operation age of the capacity resources and power generation output conversion coefficients of unit capacity of the corresponding capacity resources; based on the capacity commissioning parameters, the commissioning state of the transmission line and the commissioning capacity of the capacity resource are taken as variables, and the minimum total cost commissioning cost is taken as an optimization target to construct an optimization function; and carrying out optimization solution on the optimization function based on a preset iterative optimization mode to obtain an optimal commissioning combination of the capacity resource and the transmission line, and determining a capacity commissioning decision scheme based on the optimal commissioning combination.
In one embodiment, the computer program when executed by the processor further performs the steps of: determining corresponding physical environment parameters of the power system based on a preset typical construction cost standard of the power transmission and transformation project, and determining the construction cost of a transmission line in unit length according to the physical environment parameters of the power system; the power system physical environment parameters comprise at least one of voltage grade, wire type adopted by the power system, meteorological conditions and altitude; determining corresponding asset assessment discount rate based on typical cost standard of power transmission and transformation project, and determining annual discount rate of capacity resource according to the asset assessment discount rate; determining the service life of corresponding to-be-put-into-operation generating capacity resources based on the typical cost standard of the power transmission and transformation project, and determining the operation age limit of the capacity resources according to the service life of the to-be-put-into-operation generating capacity resources; based on the typical cost standard of the power transmission and transformation project, the ratio of the maximum output of the capacity resource in the historical operating year to the installed capacity is determined, and the power generation output conversion coefficient of the unit capacity of the corresponding capacity resource is determined according to the ratio of the maximum output of the capacity resource in the historical operating year to the installed capacity.
In one embodiment, the computer program when executed by the processor further performs the steps of: determining the total cost investment cost according to the investment cost of the capacity resource, the investment cost of the transmission line and the operation cost of the capacity resource; and determining an optimization function by using the minimized full-cost investment cost as an upper-layer objective function of the optimization target and using the minimized running cost of the capacity resource as a lower-layer objective function of the optimization target.
In one embodiment, the computer program when executed by the processor further performs the steps of: determining an upper layer objective function according to the following formula:
Figure GDA0003604021880000151
wherein, PψThe cost is put into operation for the whole cost,
Figure GDA0003604021880000152
in order to establish the cost of the capacity resource,
Figure GDA0003604021880000153
the cost of the commissioning of the transmission line,
Figure GDA0003604021880000154
operating costs for capacity resources; t is the minimum clearing period of capacity resource market operation, and T is the full period of capacity resource construction; omegaKInstall resource sets, Ω, for incrementsMFor inventory of installed resource sets, ΩLIs a line node set;
Figure GDA0003604021880000155
for the unit installed capacity price of the ith incremental capacity resource,
Figure GDA0003604021880000156
a unit installed capacity price for a kth stock capacity resource;
Figure GDA0003604021880000157
the installed capacity of the ith incremental capacity resource,
Figure GDA0003604021880000158
installed capacity that is the kth inventory capacity resource;
Figure GDA0003604021880000159
a conversion factor of the annual capacity investment cost,
Figure GDA00036040218800001510
a conversion factor for annual transmission line investment cost;
Figure GDA00036040218800001511
for the construction costs of the transmission line between line nodes i-j,
Figure GDA00036040218800001512
for commissioning of transmission lines between line nodes i-j, LijIs the line length, k, of the transmission line between nodes i-jinFor annual discount rate, T, of capacity resourcesCFor the operational age of a capacity resource, TLThe operational age of the transmission line.
In one embodiment, the computer program when executed by the processor further performs the steps of: the lower layer objective function is determined according to the following formula:
Figure GDA00036040218800001513
Figure GDA00036040218800001514
Figure GDA00036040218800001515
wherein, PE,tThe operation cost of the capacity resource in the t period is obtained;
Figure GDA0003604021880000161
the running price of the ith incremental capacity resource during the t period,
Figure GDA0003604021880000162
operating price for the kth inventory capacity resource during the t period;
Figure GDA0003604021880000163
the power generation amount of the ith incremental capacity resource in the t period,
Figure GDA0003604021880000164
sending of the kth stock capacity resource in the t periodAn amount of electricity; etai,tA maximum power generation output conversion coefficient, eta, of the ith incremental capacity resource installation level in the t periodk,tAnd (4) converting the maximum power generation output coefficient of the installed level of the kth inventory capacity resource in the t period.
In one embodiment, the computer program when executed by the processor further performs the steps of: and carrying out optimization solution on the optimization function based on a genetic algorithm, and outputting the obtained optimal put-in combination of the capacity resource and the transmission line as an optimal solution, wherein in the process of optimization solution, the method comprises the following steps: determining an initial population, and calculating the fitness of each individual in the initial population in an individual evaluation mode; based on the fitness of each individual in the initial population, selecting optimized individuals; based on preset crossover operators and mutation operators, carrying out pairing crossover and compiling operation on each selected optimized individual to obtain a next generation group; and when the fitness of the corresponding individual in the next generation group meets the preset condition, outputting the corresponding optimal solution, otherwise, when the next iteration is started, taking the next generation group as the initial group in the next iteration, returning to the step of calculating the fitness of each individual in the initial group by an individual evaluation mode, and continuously executing the step until the corresponding optimal solution is output, and ending the iteration cycle.
The storage medium is based on the determined capacity commissioning parameters, the commissioning state of the transmission line and the commissioning capacity of the capacity resource are taken as variables, the minimum total cost commissioning cost is taken as an optimization target, the construction of an optimization function is carried out, the optimization function is optimized and solved based on a preset iterative optimization mode, and a capacity commissioning decision scheme is determined based on the obtained optimal commissioning combination. On one hand, the possible benefit of capacity resources in actual operation is considered, and the potential benefit of the actual operation of the power generation capacity and the cost possibly generated by the transmission line investment are considered, so that the capacity decision investment and the system operation can be better connected. On the other hand, the minimum total cost investment cost is taken as an optimization target, so that the energy installation structure can avoid the overcompensation of capacity resources by using a low-carbon clean transformation regulation and control target, and the capacity investment decision efficiency is improved.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database or other medium used in the embodiments provided herein can include at least one of non-volatile and volatile memory. Non-volatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical storage, or the like. Volatile Memory can include Random Access Memory (RAM) or external cache Memory. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others.
All possible combinations of the technical features in the above embodiments may not be described for the sake of brevity, but should be considered as being within the scope of the present disclosure as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is specific and detailed, but not to be understood as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent application shall be subject to the appended claims.

Claims (10)

1. A method for capacity commissioning decision scheme determination, the method comprising:
determining a capacity commissioning parameter; the capacity commissioning parameters comprise commissioning cost of a transmission line unit length, annual discount rate of capacity resources, operation age of the capacity resources and power generation output conversion coefficients of unit capacity of the corresponding capacity resources;
based on the capacity commissioning parameters, building an optimization function by taking the commissioning state of the transmission line and the commissioning capacity of the capacity resource as variables and the minimum total cost commissioning cost as an optimization target; wherein, the total cost investment cost is determined according to the investment cost of the capacity resource, the investment cost of the transmission line and the operation cost of the capacity resource; the optimization functions comprise an upper layer objective function taking the minimum total cost investment cost as an optimization objective and a lower layer objective function taking the minimum operation cost of the capacity resource as an optimization objective; wherein the upper layer objective function is expressed by the following formula:
Figure FDA0003604021870000011
wherein, PψThe cost is put into operation for the whole cost,
Figure FDA0003604021870000012
in order to bring the cost of the capacity resource into operation,
Figure FDA0003604021870000013
the cost of the construction of the transmission line is increased,
Figure FDA0003604021870000014
operating costs for capacity resources; t is the minimum clearing period of capacity resource market operation, and T is the full period of capacity resource construction; omegaKInstall resource set for increment, ΩMFor inventory of installed resource sets, ΩLIs a line node set;
Figure FDA0003604021870000015
for the unit installed capacity price of the ith incremental capacity resource,
Figure FDA0003604021870000016
a unit installed capacity price for a kth stock capacity resource;
Figure FDA0003604021870000017
the installed capacity of the ith incremental capacity resource,
Figure FDA0003604021870000018
installed capacity which is the kth capacity resource;
Figure FDA0003604021870000019
a conversion factor of the annual capacity investment cost,
Figure FDA00036040218700000110
a conversion factor for annual transmission line investment cost;
Figure FDA00036040218700000111
for the construction costs of the transmission line between line nodes i-j,
Figure FDA00036040218700000112
for the commissioning of a transmission line between line nodes i-j, LijIs the line length, k, of the transmission line between nodes i-jinFor annual discount rate, T, of capacity resourcesCFor the operational age of a capacity resource, TLIs the operational age of the transmission line;
and carrying out optimization solution on the optimization function based on a preset iterative optimization mode to obtain an optimal commissioning combination of the capacity resource and the transmission line, and determining a capacity commissioning decision scheme based on the optimal commissioning combination.
2. The method of claim 1, wherein determining the capacity commissioning parameter comprises:
determining corresponding physical environment parameters of the power system based on a preset typical construction cost standard of the power transmission and transformation project, and determining the construction cost of a transmission line in unit length according to the physical environment parameters of the power system; the power system physical environment parameters comprise at least one of voltage grade, wire type adopted by the power system, meteorological conditions and altitude;
determining corresponding asset assessment discount rates based on the typical cost standards of the power transmission and transformation projects, and determining annual discount rates of capacity resources according to the asset assessment discount rates;
determining the corresponding service life of the power generation capacity resource to be put into operation based on the typical construction cost standard of the power transmission and transformation project, and determining the operation life of the capacity resource according to the service life of the power generation capacity resource to be put into operation;
and determining the ratio of the maximum output of the capacity resource in the historical operating year to the installed capacity based on the typical construction cost standard of the power transmission and transformation project, and determining the power generation output conversion coefficient of the unit capacity of the corresponding capacity resource according to the ratio of the maximum output of the capacity resource in the historical operating year to the installed capacity.
3. The method of claim 1, wherein the lower layer objective function is represented by the following formula:
Figure FDA0003604021870000021
Figure FDA0003604021870000022
Figure FDA0003604021870000023
wherein, PE,tThe operation cost of the capacity resource in the t period is obtained;
Figure FDA0003604021870000024
the running price of the ith incremental capacity resource during the t period,
Figure FDA0003604021870000025
operating price of the kth stock capacity resource in the t period;
Figure FDA0003604021870000026
the power generation amount of the ith incremental capacity resource in the t period,
Figure FDA0003604021870000027
generating capacity of the kth stock capacity resource in a t period; etai,tThe maximum power generation output conversion coefficient, eta, of the ith incremental capacity resource installation level in the t periodk,tAnd (4) converting the maximum power generation output coefficient of the installed level of the kth inventory capacity resource in the t period.
4. The method according to claim 1, wherein the performing optimization solution on the optimization function based on a preset iterative optimization manner to obtain an optimal commissioning combination of a capacity resource and a transmission line comprises:
and carrying out optimization solution on the optimization function based on a genetic algorithm, and outputting the obtained optimal commissioning combination of the capacity resource and the transmission line as an optimal solution, wherein in the process of optimization solution, the method comprises the following steps:
determining an initial population, and calculating the fitness of each individual in the initial population in an individual evaluation mode;
based on the fitness of each individual in the initial population, selecting optimized individuals;
based on a preset crossover operator and a mutation operator, carrying out pairing crossover and compiling operation on each selected optimized individual to obtain a next generation group;
and when the fitness of the corresponding individual in the next generation group meets the preset condition, outputting the corresponding optimal solution, otherwise, when the next iteration is started, taking the next generation group as the initial group in the next iteration, returning to the step of calculating the fitness of each individual in the initial group by the individual evaluation mode, and continuing to execute the step until the corresponding optimal solution is output, and ending the iteration cycle.
5. A capacity commissioning decision scheme determination apparatus, the apparatus comprising a determination module, a first processing module and a second processing module, wherein:
the determining module is used for determining a capacity commissioning parameter; the capacity commissioning parameters comprise commissioning cost of a transmission line unit length, annual discount rate of capacity resources, operation age of the capacity resources and power generation output conversion coefficients of unit capacity of the corresponding capacity resources;
the first processing module is used for constructing an optimization function by taking the commissioning state of the transmission line and the commissioning capacity of the capacity resource as variables and the minimum total cost commissioning cost as an optimization target on the basis of the capacity commissioning parameter; wherein, the total cost investment cost is determined according to the investment cost of the capacity resource, the investment cost of the transmission line and the operation cost of the capacity resource; the optimization function comprises an upper layer objective function taking the minimized total cost investment cost as an optimization objective and a lower layer objective function taking the minimized operation cost of the capacity resource as the optimization objective; wherein the upper layer objective function is expressed by the following formula:
Figure FDA0003604021870000031
wherein, PψThe cost is put into operation for the whole cost,
Figure FDA0003604021870000032
in order to establish the cost of the capacity resource,
Figure FDA0003604021870000033
the cost of the commissioning of the transmission line,
Figure FDA0003604021870000034
operating costs for capacity resources; t is the minimum clearing period of capacity resource market operation, and T is the full period of capacity resource construction; omegaKInstall resource sets, Ω, for incrementsMFor inventory of installed resource sets, ΩLIs a line node set;
Figure FDA0003604021870000041
for the unit installed capacity price of the ith incremental capacity resource,
Figure FDA0003604021870000042
a unit installed capacity price for a kth stock capacity resource;
Figure FDA0003604021870000043
the installed capacity of the ith incremental capacity resource,
Figure FDA0003604021870000044
installed capacity that is the kth inventory capacity resource;
Figure FDA0003604021870000045
a conversion factor of the annual capacity investment cost,
Figure FDA0003604021870000046
a conversion factor for annual transmission line investment cost;
Figure FDA0003604021870000047
for the construction costs of the transmission line between the line nodes i-j,
Figure FDA0003604021870000048
for the commissioning of a transmission line between line nodes i-j, LijIs the line length, k, of the transmission line between nodes i-jinFor annual discount rate of capacity resources, TCFor the operational age, T, of a capacity resourceLIs the operational age of the transmission line;
and the second processing module is used for carrying out optimization solution on the optimization function based on a preset iterative optimization mode to obtain the optimal commissioning combination of the capacity resource and the transmission line, and determining a capacity commissioning decision scheme based on the optimal commissioning combination.
6. The device of claim 5, wherein the determining module is further configured to determine a corresponding physical environment parameter of the power system based on a preset typical cost standard of the power transmission and transformation project, and determine a commissioning cost per unit length of the transmission line according to the physical environment parameter of the power system; the power system physical environment parameters comprise at least one of voltage grade, wire type adopted by the power system, meteorological conditions and altitude; determining corresponding asset assessment discount rates based on the typical cost standards of the power transmission and transformation projects, and determining annual discount rates of capacity resources according to the asset assessment discount rates; determining the corresponding service life of the power generation capacity resource to be put into operation based on the typical construction cost standard of the power transmission and transformation project, and determining the operation life of the capacity resource according to the service life of the power generation capacity resource to be put into operation; and determining the ratio of the maximum output of the capacity resource in the historical operating year to the installed capacity based on the typical construction cost standard of the power transmission and transformation project, and determining the power generation output conversion coefficient of the unit capacity of the corresponding capacity resource according to the ratio of the maximum output of the capacity resource in the historical operating year to the installed capacity.
7. The capacity commissioning decision scheme determination device of claim 5, wherein the first processing module is configured to determine a lower layer objective function according to the following formula:
Figure FDA0003604021870000051
Figure FDA0003604021870000052
Figure FDA0003604021870000053
wherein, PE,tThe operation cost of the capacity resource in the t period is obtained;
Figure FDA0003604021870000054
the running price for the ith incremental capacity resource over the t period,
Figure FDA0003604021870000055
operating price of the kth stock capacity resource in the t period;
Figure FDA0003604021870000056
the power generation amount of the ith incremental capacity resource in the t period,
Figure FDA0003604021870000057
generating capacity of the kth stock capacity resource in a t period; etai,tA maximum power generation output conversion coefficient, eta, of the ith incremental capacity resource installation level in the t periodk,tAnd (4) converting the maximum power generation output coefficient of the installed level of the kth inventory capacity resource in the t period.
8. The capacity commissioning decision scheme determining device of claim 5, wherein the second processing module is configured to perform optimization solution on the optimization function based on a genetic algorithm, and output an optimal commissioning combination of the capacity resource and the transmission line as an optimal solution, and during the process of the optimization solution, the method includes: determining an initial population, and calculating the fitness of each individual in the initial population in an individual evaluation mode; based on the fitness of each individual in the initial population, selecting optimized individuals; based on preset crossover operators and mutation operators, carrying out pairing crossover and compiling operation on each selected optimized individual to obtain a next generation group; and when the fitness of the corresponding individual in the next generation group meets the preset condition, outputting the corresponding optimal solution, otherwise, when the next iteration is started, taking the next generation group as the initial group in the next iteration, returning to the step of calculating the fitness of each individual in the initial group by the individual evaluation mode, and continuing to execute the step until the corresponding optimal solution is output, and ending the iteration cycle.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of the method of any of claims 1 to 4.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 4.
CN202110923802.XA 2021-08-12 2021-08-12 Method and device for determining capacity commissioning decision scheme and computer equipment Active CN113705863B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110923802.XA CN113705863B (en) 2021-08-12 2021-08-12 Method and device for determining capacity commissioning decision scheme and computer equipment

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110923802.XA CN113705863B (en) 2021-08-12 2021-08-12 Method and device for determining capacity commissioning decision scheme and computer equipment

Publications (2)

Publication Number Publication Date
CN113705863A CN113705863A (en) 2021-11-26
CN113705863B true CN113705863B (en) 2022-07-12

Family

ID=78652486

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110923802.XA Active CN113705863B (en) 2021-08-12 2021-08-12 Method and device for determining capacity commissioning decision scheme and computer equipment

Country Status (1)

Country Link
CN (1) CN113705863B (en)

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110163450A (en) * 2019-05-31 2019-08-23 国网山东省电力公司经济技术研究院 A kind of distribution network planning bi-level optimal model construction method limited based on operation
EP3547234A1 (en) * 2018-03-29 2019-10-02 Johnson Controls Technology Company Building energy optimization system with capacity market program (cmp) participation
CN111555281A (en) * 2020-05-29 2020-08-18 国网山东省电力公司经济技术研究院 Method and device for simulating flexible resource allocation of power system

Family Cites Families (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109617065B (en) * 2019-01-02 2020-09-15 清华大学 Power transmission network line planning method of power system considering mass operation scenes
CN109871989A (en) * 2019-01-29 2019-06-11 国网山西省电力公司吕梁供电公司 A kind of power distribution network hierarchical reconfiguration planning method containing distributed generation resource
CN110866702A (en) * 2019-11-20 2020-03-06 国网天津市电力公司 Power distribution network planning method considering dynamic network frame reconstruction and differentiation reliability
CN111507589A (en) * 2020-03-24 2020-08-07 国网新疆电力有限公司经济技术研究院 Incremental power distribution network expansion planning decision method based on master-slave game

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP3547234A1 (en) * 2018-03-29 2019-10-02 Johnson Controls Technology Company Building energy optimization system with capacity market program (cmp) participation
CN110163450A (en) * 2019-05-31 2019-08-23 国网山东省电力公司经济技术研究院 A kind of distribution network planning bi-level optimal model construction method limited based on operation
CN111555281A (en) * 2020-05-29 2020-08-18 国网山东省电力公司经济技术研究院 Method and device for simulating flexible resource allocation of power system

Also Published As

Publication number Publication date
CN113705863A (en) 2021-11-26

Similar Documents

Publication Publication Date Title
Correa-Florez et al. Stochastic operation of home energy management systems including battery cycling
Kuznetsova et al. Reinforcement learning for microgrid energy management
Luo et al. Short‐term operational planning framework for virtual power plants with high renewable penetrations
Quan et al. Incorporating wind power forecast uncertainties into stochastic unit commitment using neural network-based prediction intervals
US20190288513A1 (en) Decentralized energy management utilizing blockchain technology
US10804702B2 (en) Self-organizing demand-response system
CN111709672A (en) Virtual power plant economic dispatching method based on scene and deep reinforcement learning
Hardiansyah et al. Solving economic load dispatch problem using particle swarm optimization technique
Vitor et al. Operation planning and decision-making approaches for Volt/Var multi-objective optimization in power distribution systems
Manickavasagam et al. Sensitivity-based chance-constrained generation expansion planning
CN113988384A (en) Energy storage capacity optimal configuration method for improving reliability of power distribution network
Andoni et al. Analysis of strategic renewable energy, grid and storage capacity investments via Stackelberg-cournot modelling
Gangammanavar et al. Two-scale stochastic optimization for controlling distributed storage devices
CN116957362A (en) Multi-target planning method and system for regional comprehensive energy system
Koutsoukis et al. A multistage distribution network planning method considering distributed generation active management and demand response
CN111126707B (en) Energy consumption equation construction and energy consumption prediction method and device
Pinto et al. Reliability‐constrained robust expansion planning of active distribution networks
CN112101651A (en) Electric energy network coordination control method and system and information data processing terminal
CN113705863B (en) Method and device for determining capacity commissioning decision scheme and computer equipment
CN113421004A (en) Transmission and distribution cooperative active power distribution network distributed robust extension planning system and method
CN111146815A (en) Intelligent power distribution network distributed power generation planning configuration method
CN115907140A (en) Method and device for optimizing power spot shipment scheme, computer equipment and medium
CN111525629B (en) Power supply capacity configuration method and device, computer equipment and storage medium
Dakir et al. On the number of representative days for sizing microgrids with an industrial load profile
CN115310647A (en) Multi-stage random planning energy scheduling method and system considering non-predictability

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant