CN115907155A

CN115907155A - Generator set planning method and device introducing carbon emission cost

Info

Publication number: CN115907155A
Application number: CN202211466050.XA
Authority: CN
Inventors: 黄国日; 别佩
Original assignee: Energy Development Research Institute of China Southern Power Grid Co Ltd
Current assignee: Energy Development Research Institute of China Southern Power Grid Co Ltd
Priority date: 2022-11-22
Filing date: 2022-11-22
Publication date: 2023-04-04
Anticipated expiration: 2042-11-22
Also published as: CN115907155B

Abstract

The present application relates to a method, apparatus, computer device, storage medium and program product for generating set planning that incorporates carbon emission costs. The method comprises the following steps: acquiring a generator set planning request sent by a request terminal; responding to a generator set planning request, and calling a planning decision main model and an operation sub model corresponding to a generator set planning decision optimization model; determining a target area for constructing the generator set, a type of the generator set in the target area and an installed capacity of the generator set in the target area from the candidate areas according to investment cost, operation cost, carbon emission cost and reliability cost corresponding to each candidate area, a planning decision main model, an operation sub model and a target constraint condition; obtaining a generator set planning result according to the target area, the type of the generator set in the target area and the installed capacity of the generator set in the target area; and feeding back a generator set planning result to the request terminal. By adopting the method, the planning accuracy of the generator set can be improved.

Description

Generator set planning method and device introducing carbon emission cost

Technical Field

The present application relates to the field of power distribution network technologies, and in particular, to a method, an apparatus, a computer device, a storage medium, and a computer program product for planning a generator set that introduces a carbon emission cost.

Background

Generally, for generator set planning of a power system, linear planning or nonlinear planning may be adopted to implement planning of a generator set in combination with load prediction data, economic and reasonable requirements, reserve capacity requirements and the like. The linear rule is to linearize the model and realize the planning of the generator set through a linear planning algorithm. The nonlinear planning is realized by constructing a nonlinear model of the generator set planning.

However, the planning accuracy of the generator set is low in the above mode.

Disclosure of Invention

In view of the above, there is a need to provide a method, an apparatus, a computer device, a computer readable storage medium, and a computer program product for planning a generator set, which can improve the planning accuracy of the generator set and introduce carbon emission cost.

In a first aspect, the present application provides a method for planning a power generation unit that introduces a cost of carbon emissions, comprising:

acquiring a generator set planning request sent by a request terminal; the generator set planning request comprises candidate regions, and investment cost, operation cost, carbon emission cost and reliability cost corresponding to each candidate region;

responding to the generator set planning request, and calling a planning decision main model and an operation sub model corresponding to a generator set planning decision optimization model;

determining a target area for building a generator set, a type of the generator set in the target area and an installed capacity of the generator set in the target area from the candidate areas according to the investment cost, the operation cost, the carbon emission cost and the reliability cost corresponding to each candidate area, the planning decision main model, the operation sub model and a target constraint condition;

obtaining a generator set planning result according to the target area, the type of the generator set in the target area and the installed capacity of the generator set in the target area;

and feeding back the generator set planning result to the request terminal.

In one embodiment, the target constraints include a first target constraint and a second target constraint, and the determining a target region for building a generator set, a type of the generator set in the target region, and an installed capacity of the generator set in the target region from the candidate regions according to the investment cost, the operating cost, the carbon emission cost, and the reliability cost corresponding to each of the candidate regions, the planning decision main model, the operating sub model, and the target constraints includes:

after initializing the Benders cut constraint of the planning decision main model, solving the discrete variables in the planning decision main model according to the initialized Benders cut constraint and the first target constraint condition to obtain a first solution result; the first solving result is the installed capacity of the generator set type planned and constructed in the t year of the power system in each candidate region, the Benders cut constraint is related to the Benders optimal cut, and the Benders optimal cut is obtained according to the second target constraint condition and the dual multiplier corresponding to the second target constraint condition;

solving continuous variables in the operation submodel according to the first solving result and the second target constraint condition to obtain a second solving result; the second solving result is the generated power of the kth generator set type of each candidate region in the power system;

according to the second solving result, the Benders cut constraint, the first target constraint condition and the second target constraint condition, sequentially carrying out iterative solution on discrete variables in the planning decision main model;

if the absolute value of the difference between the operation result of the k-th investment cost operation model and the operation result of the k-th planning decision main model is smaller than or equal to a preset value, determining a target area for constructing a generator set, the type of the generator set in the target area and the installed capacity of the generator set in the target area according to the discrete variables of the k-th planning decision main model; wherein the operation result of the investment cost operation model is the sum of the investment cost which is reduced at the end of the t year of the power system in each candidate region and the operation result of the operation submodel.

In one embodiment, the planning decision main model is represented by a functional relationship, and the operation result of the planning decision main model is represented by J _mastet Is represented by the formula J _mastet Satisfies the following formula: j. the design is a square _mastet ＝min{c _inv (x) + β (x) }; the x represents a discrete variable in the planning decision main model, the c _inv (x) Representing the investment cost of each candidate region discounted at the end of the t year of the power system; the β (x) represents a Benders cut constraint of the planning decision main model, the β (x) satisfying the following formula:

J _sub represents the result of the operation of the operating sub-model, and->

A Benders optimization cut representing discrete variables in the planning decisions connecting the planning decision main model and the running submodel returned by the running submodel, is/are>

Satisfies the following formula:

wherein i represents each of the candidate regions, and P _i，k，h (t) represents generated power of the kth genset type in the power system for each of the candidate regions, the Q _i，k (t) is said x, said Q _i，k (t) indicating installed capacity of a kth genset type for which each of the candidate regions was planned to be constructed in the t year of the power system; p' _i，k，h (t) represents the generated power at the h time of the generator set of the planned starting year of the planned cycle, Q' _i，k A trusted capacity of the generator set representing a planned start year of the planned cycle; said L is _i，h (t) represents a load amount of each of the candidate regions at a time h of the t year of the power system, the load amount being calculated based on the load amount

Representing a load shedding amount of each of the candidate regions at a time h of the t year of the power system; phi' _i，k，h The phi ″) _i，k，h And the phi' _i，k，h Representing a dual multiplier corresponding to the second target constraint condition;

the investment cost operation model is expressed by a functional relation, the operation result of the investment cost operation model is expressed by UB, and the UB satisfies the following formula: UB = min { c _inv (x)+J _sub }; wherein, the J is _sub Representing the result of the operation submodel, said J _sub Satisfying the following formula: j. the design is a square _sub ＝min{c _op (y)+c _co2 (y)+c _ens (y) }; wherein y represents a continuous variable in the run submodel, c _op ( _y ) Representing the operating costs of each of the candidate regions discounted at the end of the t year of the power system, c _co2 (y) represents a carbon emission cost discounted by each of the candidate regions at the end of the t-th year of the power system, and c _ens (y) represents a reliability cost discounted by each of the candidate regions at the end of the t-th year of the power system.

In one embodiment, the first target constraint is represented by a functional relationship, the first target constraint includes a first formula, and the first formula satisfies the following formula:

wherein i represents each of the candidate regions, and Q _i，k (t) as discrete variables in the planning decision model, said Q _i，k (t) represents installed capacity of kth genset type at the t year investment construction of the power system for each of the candidate regions, the Φ _i.k Representing the trusted capacity, Q ', of the newly commissioned generator set' _i，k Denotes an installed capacity of a generator set of a planned starting year of the planned cycle, Φ' _i，k A trusted capacity of the generator set representing a planned start year of the planned cycle, the

Representing a maximum load of the power system, the R (t) representing a rotational reserve requirement of the electrical system;

the second target constraint condition is expressed by a functional relation, the second target constraint condition comprises a second formula, a third formula and a fourth formula, and the second formula satisfies the following formula: p is _i，k，h (t)≤∑ _t Qi _，k( t), the third formula satisfying the following formula: p' _i，k，h (t)≤Q′ _i，k The fourth formula satisfies the following formula:

wherein, the P is _i，k，h (t) represents the generated power of the kth genset type in the power system for each of the candidate regions, the Q _i，k (t) indicating installed capacity of a kth genset type for which each of the candidate regions was planned to be constructed in the t year of the power system; p' _i，k，h (t) represents the generated power at the h time of the generator set of the planned starting year of the planned cycle, Q' _i，k A trusted capacity of the generator set representing a planned start year of the planned cycle; said L is _i，h (t) indicates that each of the candidate regions is in the second place of the power systemthe load capacity at the h-th moment of t years, the

And a load shedding amount of each candidate region at the h-th time of the t-th year of the power system.

In one embodiment, the generator set planning decision optimization model is represented by a functional relationship, and the generator set planning decision optimization model satisfies the following formula:

wherein, the J is _O Representing the generator set planning decision optimization model, the T representing a planning period, the lambda representing a discount rate, the c _inv (t) represents the investment cost of each of the candidate regions discounted at the end of the t year of the power system, c _op (t) represents the operating cost discounted by each of the candidate regions at the end of the t year of the power system, c _co2 (t) represents a carbon emission cost that each of the candidate regions discounted at the end of the t year of the power system, the c _ens (t) represents reliability costs incurred by each of the candidate regions at the end of the t-th year of the power system.

In one of the embodiments, the first and second parts of the device,

investment cost c corresponding to each of the candidate regions _inv (t) represents, said c _inv (t) satisfies the following formula: c. C _inv (t)＝Q _i，k (t)θ _k I represents each of the candidate regions, and Q _i，k (t) represents installed capacity of kth generator set type planned and constructed in t year of each of the candidate regions of the power system, and θ _k A cost per capacity installed representing a kth genset type at each of the candidate regions of the power system;

operation cost c corresponding to each of the candidate regions _op (t) represents, said c _op (t) satisfies the following formula: c. C _op (t)＝∑ _i，k，h P _i，k，h (t)ρ _k Said P is _i，k，h (t) represents the generated power of the kth generator set type in each candidate region of the power system, and ρ is _k A cost of power generation representing a unit of generated power of a kth genset type at each of the candidate regions of the power system;

carbon emission cost c for each candidate region _co2 (t) represents, said c _co2 (t) satisfies the following formula: c. C _co 2(t)＝∑ _i，k ∑ _h P _i，k，h (t)μ(t)(1-γ _i，k ) μ (t) represents an average price of carbon emissions in the t-year for each of the candidate regions of the power system, and γ _i，k A carbon emission allowance ratio representing a kth power generation group type at each of the candidate regions of the power system;

reliability cost c for each candidate area _ens (t) represents, said c _ens (t) satisfies the following formula:

is/are>

Represents a load cut amount at a time h of the t year of each of the candidate areas of the electric power system, the +>

And a unit loss load value in the t year of each of the candidate areas of the power system.

In a second aspect, the present application provides a genset planning apparatus that introduces carbon emission costs, comprising:

the acquisition module is used for acquiring a generator set planning request sent by a request terminal; the generator set planning request comprises candidate regions and investment cost, operation cost, carbon emission cost and reliability cost corresponding to each candidate region;

the calling module is used for calling a planning decision main model and an operation sub model corresponding to the generator set planning decision optimization model in response to the generator set planning request;

a determining module, configured to determine, according to the investment cost, the operation cost, the carbon emission cost, the reliability cost, the main planning decision model, the sub-operation model, and a target constraint condition corresponding to each candidate region, a target region for building a generator set, a type of the generator set in the target region, and an installed capacity of the generator set in the target region from the candidate region;

the generating module is used for obtaining a generator set planning result according to the target area, the type of the generator set in the target area and the installed capacity of the generator set in the target area;

and the feedback module is used for feeding back the generator set planning result to the request terminal.

In a third aspect, the present application also provides a computer device. The computer device comprises a memory storing a computer program and a processor implementing the following steps when executing the computer program:

and feeding back the generator set planning result to the request terminal.

In a fourth aspect, the present application further provides a computer-readable storage medium. The computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of:

and feeding back the generator set planning result to the request terminal.

In a fifth aspect, the present application further provides a computer program product. The computer program product comprising a computer program which when executed by a processor performs the steps of:

and feeding back the generator set planning result to the request terminal.

According to the method, the device, the computer equipment, the storage medium and the computer program product for planning the generator set introducing the carbon emission cost, the generator set planning request sent by the request terminal is obtained, and comprises candidate areas and investment cost, operation cost, carbon emission cost and reliability cost corresponding to each candidate area; in response to a generator set planning request, a planning decision main model and an operation submodel corresponding to a generator set planning decision optimization model are called, a target area for building the generator set, the type of the generator set in the target area and the installed capacity of the generator set in the target area can be determined from the candidate areas according to the investment cost, the operation cost, the carbon emission cost and the reliability cost corresponding to each candidate area, the planning decision main model, the operation submodel and the target constraint condition, and a generator set planning result is obtained according to the target area, the type of the generator set in the target area and the installed capacity of the generator set in the target area and fed back to a request terminal. Investment cost, operation cost, carbon emission cost and reliability cost of each candidate area are considered in the planning of the generator set, the generator set is planned economically, meanwhile, the influence of carbon emission on the planning of the generator set is considered, and the accuracy of the planning of the generator set can be improved.

Drawings

FIG. 1 is an environmental diagram illustrating an exemplary implementation of a method for generating set planning that incorporates carbon emissions costs;

FIG. 2 is a schematic flow diagram of a method for generator set planning incorporating carbon emissions costs in one embodiment;

FIG. 3 is a schematic flow chart illustrating the steps of determining a target area for constructing the generator set, a type of the generator set in the target area, and an installed capacity of the generator set in the target area from the candidate areas according to the investment cost, the operating cost, the carbon emission cost, and the reliability cost corresponding to each candidate area, the planning decision main model, the operating sub model, and the target constraint condition in one embodiment;

FIG. 4 is a diagram illustrating an iterative solution between a planning decision main model and a run sub model, according to one embodiment;

FIG. 5 is a block diagram of a genset planning apparatus incorporating carbon emissions costs in one embodiment;

FIG. 6 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 the present application and are not intended to limit the present application.

When the planning of the generator set is realized by adopting linear planning or nonlinear planning, the influence of carbon emission cost on a planning result is not considered, and the environmental external cost faced by different generator set types is different, so that the planning accuracy is low.

Based on this, the present application provides a method for planning a power generation unit that introduces carbon emission cost, and the method of the present application can be applied to the application environment shown in fig. 1. The request terminal 102 communicates with the server 104 through a network, the request terminal 102 may be, but is not limited to, various personal computers, notebook computers, smart phones, and tablet computers, the data storage system may store data that the server 104 needs to process, the data storage system may be integrated on the server 104, or may be placed on a cloud or other network servers.

Specifically, the request terminal 102 sends a generator set planning request to the server 104, where the generator set planning request includes candidate regions, and investment costs, operation costs, carbon emission costs, and reliability costs corresponding to the candidate regions. The server 104 responds to the generator set planning request, and calls a planning decision main model and an operation sub model corresponding to the generator set planning decision optimization model. The server 104 determines a target area for constructing the generator set, a type of the generator set in the target area, and an installed capacity of the generator set in the target area from the candidate area according to the planning decision main model, the operation sub model, and the target constraint condition, obtains a generator set planning result according to the target area, the type of the generator set in the target area, and the installed capacity of the generator set in the target area, and the server 104 feeds the generator set planning result back to the request terminal 102, so that a worker can execute a subsequent process based on the generator set planning result on the request terminal 102.

In one embodiment, as shown in fig. 2, a flow diagram of a method for planning a generator set that introduces carbon emission cost is provided, which includes the following steps, for example, when the method is applied to the server 104 in fig. 1:

and S202, acquiring a generator set planning request sent by the request terminal.

In this embodiment, the generator set planning request includes candidate regions, and the investment cost, the operation cost, the carbon emission cost, and the reliability cost corresponding to each candidate region. The candidate areas can include areas where the generator sets are to be built and areas where the generator sets are built. The investment cost, the operation cost, the carbon emission cost and the reliability cost corresponding to each candidate region can be obtained by the following modes:

investment cost c for each candidate area _inv (t) represents c _inv (t) satisfies the following formula: c. C _inv (t)＝Q _i，k (t)θ _k . Wherein i represents each candidate region, Q _i，k (t) installed capacity, θ, of the kth generator set type planned and constructed in the t year of each candidate area of the power system _k And (3) a unit capacity installed cost of the kth generator set type in each candidate region of the power system.

Operation cost c for each candidate area _op (t) represents c _op (t) satisfies the following formula: c. C _op (t)＝∑ _i， _kh P _i，k，h (t)ρ _k . Wherein, P _i，k，h (t) represents the generated power of the kth power generation group type in each candidate area of the power system, ρ _k And a power generation cost per unit generated power of the kth generator set type in each candidate region of the power system.

Carbon emission cost for each candidate area c _co2 (t) represents c _co2 (t) satisfies the following formula: c. C _co2 (t)＝∑ _i，k ∑ _h P _i，k，h (t)μ(t)(1-γ _i，k ). Where μ (t) represents the average price of carbon emissions in the t-th year in each candidate area of the power system, γ _i，k Indicating the carbon emission allowance ratio of the kth power generation group type in each candidate region of the power system.

Reliability cost for each candidate area c _ens (t) represents c _ens (t) satisfies the following formula:

wherein it is present>

Indicates the load shedding amount at the h th time of the t year of each candidate area of the power system, and/or the like>

The unit loss load value in the t-th year in each candidate area of the power system is shown.

And S204, responding to the generator set planning request, and calling a planning decision main model and an operation sub model corresponding to the generator set planning decision optimization model.

In the embodiment, the generator set planning decision optimization model can be obtained by minimizing the sum of investment cost, operation cost, carbon emission cost and reliability cost in a planning period from an economic point of view. The generator set planning decision optimization model can be expressed by a functional relation, and meets the following formula:

wherein, J _O Representing a generator set planning decision optimization model, T representing a planning period, lambda representing a discount rate, c _inv (t) represents investment costs of each candidate area discounted at the end of the t-th year of the power system, c _op (t) represents the running cost of each candidate area discounted at the end of the t year of the power system, c _co2 (t) represents carbon emission cost discounted at the end of the t-th year in the power system in each candidate area, c _ens (t) represents reliability costs incurred by each candidate area at the end of the t-th year of the power system.

In the application, when the generator set is planned based on the generator set planning decision optimization model, the generator set planning decision optimization model can be decomposed into two constraint optimization models, wherein the two constraint optimization models are a planning decision main model and an operation sub model respectively. The investment decision main model is used for determining discrete variables corresponding to planning decisions of the generator sets in a planning period, and the operation sub model is used for determining the output conditions of the generator sets in all time periods.

And S206, determining a target area for constructing the generator set, the type of the generator set in the target area and the installed capacity of the generator set in the target area from the candidate areas according to the investment cost, the operation cost, the carbon emission cost, the reliability cost, the planning decision main model, the operation sub model and the target constraint conditions corresponding to each candidate area.

In this embodiment, the target constraint conditions are used to select a region for building a generator set from the candidate regions, and the target constraint conditions include a constraint condition corresponding to the installed capacity of the generator set in the initial year of new commissioning and planning, a constraint condition corresponding to the real-time balance between the generated power and the load of the power system, and a constraint condition corresponding to the requirement of the power system on the spinning reserve.

Further, the service may determine, from the candidate regions, a target region for constructing the generator set, a type of the generator set in the target region, and an installed capacity of the generator set in the target region based on the constraint conditions, investment costs, operation costs, carbon emission costs, and reliability costs corresponding to the candidate regions, the planning decision main model, and the operation sub model. The number of the target areas may be one or more, and the embodiment is not limited.

And S208, obtaining a generator set planning result according to the target area, the type of the generator set in the target area and the installed capacity of the generator set in the target area.

In this embodiment, the target regions are one or more regions among the candidate regions, and the target regions may include regions where the generator sets are constructed or regions where the generator sets are not constructed. The types of the generator set in the target area can comprise hydroelectric power, coal-fired power generation, gas power generation, new energy (photovoltaic, wind power) and nuclear power.

It is understood that the type of generator set constructed in the target area and the maximum power generating capacity of the generator set constructed in the target area can be determined according to the target area, the type of generator set in the target area and the installed capacity of the generator set in the target area.

And S210, feeding back a generator set planning result to the request terminal.

In this embodiment, after determining the target area, the type of the generator set in the target area, and the installed capacity of the generator set in the target area, the server may encapsulate the target area, the type of the generator set in the target area, and the installed capacity of the generator set in the target area, and feed back the encapsulated generator set planning result to the request terminal, so that the worker may execute subsequent processes based on the generator set planning result on the request terminal.

In summary, in the embodiment shown in fig. 2, by obtaining the generator set planning request sent by the request terminal, the generator set planning request includes candidate regions, and the investment cost, the operation cost, the carbon emission cost, and the reliability cost corresponding to each candidate region; in response to a generator set planning request, a planning decision main model and an operation sub model corresponding to a generator set planning decision optimization model are called, a target area for building a generator set, the type of the generator set in the target area and the installed capacity of the generator set in the target area can be determined from the candidate areas according to the investment cost, the operation cost, the carbon emission cost and the reliability cost corresponding to each candidate area, the planning decision main model, the operation sub model and the target constraint condition, a generator set planning result is obtained according to the target area, the type of the generator set in the target area and the installed capacity of the generator set in the target area, and the generator set planning result is fed back to a request terminal. Investment cost, operation cost, carbon emission cost and reliability cost of each candidate area are considered in the planning of the generator set, the planning of the generator set is achieved from an economic perspective, meanwhile, the influence of carbon emission on the planning of the generator set is also considered, and the accuracy rate of planning of the generator set can be improved.

Based on the embodiment shown in fig. 2, in an embodiment, the target constraints include a first target constraint and a second target constraint, and as shown in fig. 3, a flow chart for determining a target area for constructing the generator set, a type of the generator set in the target area, and an installed capacity of the generator set in the target area from the candidate areas according to the investment cost, the operation cost, the carbon emission cost, and the reliability cost corresponding to each candidate area, the planning decision main model, the operation sub model, and the target constraints is provided, which includes the following steps:

s302, after initializing the Benders cut constraint of the planning decision main model, solving the discrete variables in the planning decision main model according to the initialized Benders cut constraint and the first target constraint condition to obtain a first solving result.

In this embodiment, the first target constraint condition refers to a constraint condition corresponding to a requirement of the power system for running the rotating standby, and may be represented by a functional relationship, where the first target constraint condition includes a first formula that satisfies the following formula:

wherein i represents each candidate region, Q _i，k (t) is a discrete variable in the planning decision model, Q _i，k (t) installed capacity, Φ, of kth genset type invested in construction of power system in year t for each candidate region _i.k Representing the trusted capacity, Q 'of the newly commissioned generator set' _i，k Installed capacity of generator set, Φ ', representing planned starting year of planned cycle' _i，k The credible capacity of the generator set representing the planned starting year of the planned cycle,

representing the maximum load of the power system and R (t) representing the rotational standby requirement of the electronic system.

In this embodiment, the Benders cut constraint is related to the Benders optimal cut, which is obtained according to the second target constraint condition and the dual multipliers corresponding to the second target constraint condition. Specifically, the main planning decision model is to minimize the investment cost of the generator set in the planning period, and the operation sub-modelThe sum of the operation cost, the carbon emission cost and the reliability cost returned by the model is a function of the planning decision discrete variable x, the planning decision main model can be represented by a functional relation, and the operation result of the planning decision main model can be represented by J _mastet Is represented by J _mastet Satisfies the following formula:

J _mastet ＝min{c _inv (x)+β(x)}；

where x represents a discrete variable in the planning decision main model, c _inv (x) Representing the investment cost discounted by each candidate region at the end of the t year of the power system, and beta (x) representing the Benders cut constraint of the planning decision main model.

It can be understood that β (x) is initialized, a first solution result obtained according to the initialized β (x), the first target constraint condition and the planning decision main model is the installed capacity of the generator set type planned and constructed in the t-th year of the power system in each candidate region, that is, according to the first target constraint condition and the planning decision main model, a discrete variable x is solved, and in combination with the calculation formula of the investment cost described in S202, the first solution result obtained is Q _i，k (t)。

Wherein β (x) can be expressed as a functional relationship when β (x) is not initialized, β (x) satisfying the following formula:

J _su b denotes the result of the operation of the operating sub-model, ->

And (3) representing Benders optimization cuts of discrete variables in the planning decisions of the main planning decision model and the running submodel returned by the running submodel. The planning decision main model and the operation sub model can realize information interaction between the planning decision main model and the operation sub model through Benders optimization cutting.

Specifically, the Benders optimization cut may be constructed by running a second target constraint corresponding to the submodel and a dual multiplier corresponding to the second target constraint. The Benders cut constraint of the planning decision main model can be determined according to the Benders optimization cut, and the optimization space of the planning decision main model can be corrected by adding the Benders cut constraint to the planning decision main model.

It can be understood that because the dual multipliers have marginal meanings, the Benders cut constraint only contains partial information of the operation submodels, and the optimization result of the planning decision main model can meet the Benders cut constraint but cannot guarantee that the constraint of the operation submodels is completely met. Therefore, after the planning decision main model is solved, the operation sub-model needs to be calculated again, and the process is a process of back-and-forth iteration of the planning decision main model and the operation sub-model, and a final generator set planning result is obtained through the iteration process.

In particular, the amount of the solvent to be used,

satisfies the following formula: />

Wherein i represents each candidate region, P _i，k，h (t) power generation power, Q, of the kth genset type in the power system for each candidate area _i，k (t) is x, Q _i，k (t) indicating installed capacity of kth generator set type planned and constructed in the t year of the power system in each candidate region; p' _i，k，h (t) represents the generated power, Q ', of the genset at time h for the planned starting year of the planned cycle' _i，k A trusted capacity of the generator set representing a planned start year of the planned cycle; l is _i，h (t) represents the load amount of each candidate district at the h-th time of the t-th year of the power system,

indicating the load shedding amount of each candidate region at the h-th time of the t year of the power system; phi' _i，k，h 、Φ″ _i，k，h And Φ' _i，k，h And representing the dual multipliers corresponding to the second target constraint.

And S304, solving continuous variables in the operation submodel according to the first solving result and the second target constraint condition to obtain a second solving result.

In this embodiment, the second target constraint condition refers to a constraint condition that the upper limit of the generated power of the generator set in the initial year of new commissioning and planning does not exceed the corresponding installed capacity of the generator set, the second target constraint condition is expressed by a functional relationship, and the second target constraint condition includes a second formula, a third formula and a fourth formula. The second formula satisfies the following formula: p _i，k，h (t)≤∑ _t Q _i，k (t), the third formula satisfies the following formula: p' _i，k，h (t)≤Q′ _i，k The fourth formula satisfies the following formula:

wherein, P _i，k，h (t) power generation power, Q, of the kth genset type in the power system for each candidate area _i，k (t) indicating installed capacity of kth generator set type planned and constructed in the t year of the power system in each candidate region; p' _i，k，h (t) represents the generated power, Q ', of the genset at time h for the planned starting year of the planned cycle' _i，k A trusted capacity of the generator set representing a planned start year of the planned cycle; l is _i，h (t) represents the load amount of each candidate area at the h-th time of the t-th year of the power system,

the load shedding amount at the h-th time of the t-th year of the power system in each candidate area is shown.

In this embodiment, the operation result of the operation submodel may be represented by J _sub Is represented by J _sub Satisfies the following formula: j. the design is a square _sub ＝min{c _op (y)+c _co2 (y)+c _ens (y). Where y denotes the continuous variable in the run submodel, c _op (y) represents the running cost of each candidate area discounted at the end of the t-th year of the power system, c _co2 (y) represents the carbon emission cost of each candidate area discounted at the end of the t-th year of the power system, c _ens (y) represents reliability costs incurred by each candidate area at the end of the t-th year of the power system.

It will be appreciated that the first solution (i.e., Q) is based on _i，k (t)), the second target constraint condition and the second solution result obtained by operating the sub-model are the generated power of the kth generator set type of each candidate region in the power system, namely, the generated power is obtained according to the first solution result (namely Q) _i，k (t)), a second target constraint condition and an operation submodel, wherein the discrete variable y is solved, and a second solving result obtained by combining the operation cost, the carbon emission cost and the reliability cost calculation formula described in S202 is P _i，k，h (t)。

And S306, sequentially carrying out iterative solution on discrete variables in the planning decision main model according to the second solution result, the Benders cut constraint, the first target constraint condition and the second target constraint condition.

In the present application, the result of the second solution (i.e., P) _i，k，h (t)) under the known condition, substituting the second solving result into the formula corresponding to the Benders optimization cut to obtain the Benders cut constraint of the corresponding planning decision main model, and combining the first target constraint condition, the Benders cut constraint of the corresponding planning decision main model and the planning decision main model to re-solve the discrete variables in the planning decision main model, namely, re-obtain Q _i，k (t); to recover Q _i，k After (t), Q may be recovered _i，k (t) substituting into the formula corresponding to the operation submodel, and combining with the second target constraint condition, obtaining P again _i，k，h (t), and so on, to achieve iterative solution of discrete variables in the planning decision main model.

And S308, if the absolute value of the difference between the operation result of the k-th investment cost operation model and the operation result of the k-th planning decision main model is less than or equal to the preset value, determining a target area for constructing the generator set, the type of the generator set in the target area and the installed capacity of the generator set in the target area according to the discrete variables of the k-th planning decision main model.

In this embodiment, the investment cost operational modelThe operation result of the investment cost operation model is the sum of the investment cost which is reduced at the end of the t year of the power system in each candidate region and the operation result of the operation sub-model, the operation result of the investment cost operation model can be expressed by UB, and the UB satisfies the following formula: UB = min { c _inv (x)+J _sub }. Wherein, J _sub Representing the result of the operation of the sub-model, x representing the discrete variables in the planning decision sub-model, c _inv (x) Indicating the investment costs of each candidate region discounted at the end of the t year of the power system.

Wherein, if

Represents the result of the operation of the k-th planning decision main model>

Represents the operation result of the k-th investment cost operation model, epsilon represents a preset value, and ^k ) When UL | ≦ ε, the target region for the construction of the generator set, the type of generator set in the target region, and the installed capacity of the generator set in the target region may be determined according to the discrete variables of the main model for the kth planning decision.

It can be understood that under | UB ^(k) When UL | > ε, the discrete variables (i.e., Q) of the current planning decision primary model are assigned _i，k (t)) as a known quantity, substituting the known quantity into a formula corresponding to the operation submodel, combining a second target constraint condition, and obtaining P again _i，k，h (t) and according to the recovered P _i，k，h (t) combining the first target constraint and the Benders cut constraint to retrieve Q _i，k (t) simultaneously determining whether | UB is satisfied ^(k) -UL ≦ ε, if satisfied, depending on the Q retrieved _i，k (t) a target area for building the generator set, a type of the generator set in the target area, and an installed capacity of the generator set in the target area may be determined; if not full ofAnd continuing to carry out iterative solution again until | UB is satisfied ^(k) -UL|≤ε。

In summary, in the embodiment shown in fig. 3, by combining the first target constraint condition, the second target constraint condition, the Benders cut constraint, the main planning decision model and the operation submodel, iterative solution is performed on the discrete variables in the main planning decision model, so that when a certain condition is met, a target area for constructing the generator set, a type of the generator set in the target area, and an installed capacity of the generator set in the target area are determined according to a result corresponding to the discrete variables in the main planning decision model; and then, the server obtains a generator set planning result according to the target area, the type of the generator set in the target area and the installed capacity of the generator set in the target area, and feeds the generator set planning result back to the request terminal.

It can be understood that the optimal planning scheme of the generator set closest to the overall optimization can be obtained in the dynamic planning process through multi-stage process optimization, the environmental externality cost difference faced by different generator set types in the national carbon emission market environment is considered at the same time, the whole element cost of the generator set planning is evaluated, the investment cost, the operation cost, the carbon emission cost and the reliability cost of each candidate area are considered in the generator set planning, the generator set planning is realized from the economic perspective, the influence of the carbon emission on the generator set planning is also considered at the same time, and the accuracy of the generator set planning can be improved.

In one embodiment, shown in FIG. 4 in conjunction with the description of FIG. 3, a schematic diagram of an iterative solution between a planning decision main model and a run sub model is provided. In the process of realizing the iterative solution between the planning decision main model and the operation sub model, benders cut constraints of different planning decision main models can be formed according to the operation result of the operation sub model, and planning decision variables (namely discrete variables) in the planning decision main model can be solved by returning the Benders cut constraints of the different planning decision main models to the planning decision main model, so that a generator set planning result is obtained based on the result corresponding to the discrete variables in the planning decision main model.

It should be understood that, although the steps in the flowcharts related to the embodiments as described above are sequentially displayed as indicated by arrows, the steps are not necessarily performed sequentially 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 a part of the steps in the flowcharts related to the embodiments described above may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, and the execution order of the steps or stages is not necessarily sequential, but may be performed alternately or alternately with other steps or at least a part of the steps or stages in other steps.

Based on the same inventive concept, the embodiment of the application also provides a generator set planning device for the introduced carbon emission cost, which is used for realizing the generator set planning method for the introduced carbon emission cost. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the method, so that specific limitations in one or more embodiments of the carbon emission cost-introducing generator set planning device provided below can be referred to the limitations on the carbon emission cost-introducing generator set planning method in the above, and details are not repeated herein.

In one embodiment, as shown in fig. 5, there is provided a genset planning apparatus that introduces carbon emission costs, comprising: an obtaining module 502, a calling module 504, a determining module 506, a generating module 508 and a feedback module 510, wherein:

an obtaining module 502, configured to obtain a generator set planning request sent by a request terminal; the generator set planning request comprises candidate regions and investment cost, operation cost, carbon emission cost and reliability cost corresponding to each candidate region.

And the calling module 504 is configured to call, in response to the generator set planning request, a planning decision main model and an operation sub model corresponding to the generator set planning decision optimization model.

And a determining module 506, configured to determine, from the candidate regions, a target region for building the generator set, a type of the generator set in the target region, and an installed capacity of the generator set in the target region according to the investment cost, the operation cost, the carbon emission cost, the reliability cost, the main planning decision model, the sub-operation model, and the target constraint condition corresponding to each candidate region.

And the generating module 508 is configured to obtain a generator set planning result according to the target area, the type of the generator set in the target area, and the installed capacity of the generator set in the target area.

And a feedback module 510, configured to feed back a generator set planning result to the request terminal.

In one embodiment, the target constraint conditions include a first target constraint condition and a second target constraint condition, and the determining module 506 is further configured to, after initializing a Benders cut constraint of the planning and decision main model, solve the discrete variables in the planning and decision main model according to the initialized Benders cut constraint and the first target constraint condition, and obtain a first solution result; the first solving result is the installed capacity of the generator set type planned and constructed in the t year of the power system in each candidate region, the Benders cut constraint is related to the Benders optimal cut, and the Benders optimal cut is obtained according to a second target constraint condition and a dual multiplier corresponding to the second target constraint condition; solving continuous variables in the operation submodels according to the first solving result and the second target constraint condition to obtain a second solving result; the second solving result is the generated power of the kth generator set type of each candidate region in the power system; according to the second solving result, the Benders cut constraint, the first target constraint condition and the second target constraint condition, sequentially carrying out iterative solving on discrete variables in the planning decision main model; if the absolute value of the difference between the operation result of the k-th investment cost operation model and the operation result of the k-th planning decision main model is smaller than or equal to a preset value, determining a target area for constructing the generator set, the type of the generator set in the target area and the installed capacity of the generator set in the target area according to the discrete variable of the k-th planning decision main model; and the operation result of the investment cost operation model is the sum of the investment cost which is reduced at the end of the t year of the power system in each candidate region and the operation result of the operation submodel.

In one embodiment, the planning decision main model is represented by a functional relationship, and the operation result of the planning decision main model is represented by J _mastet Is represented by J _mastet Satisfying the following formula: j. the design is a square _mastet ＝min{c _inv (x) + β (x) }; x denotes the discrete variable in the planning decision main model, c _inv (x) Representing the investment cost of each candidate region discounted at the end of the t year of the power system; β (x) represents the Benders cut constraint of the planning decision main model, and β (x) satisfies the following formula:

J _sub represents the result of the operation of the operating sub-model>

Benders optimal cut representing discrete variables in the connected planning decision master model returned by the running sub-model and the planning decisions of the running sub-model->

Satisfies the following formula:

wherein i represents each candidate region, P _i，k，h (t) generated power, Q, of the kth genset type in the power system for each candidate area _i，k (t) is x, Q _i，k (t) indicating installed capacity of kth generator set type planned and constructed in the t year of the power system in each candidate region; p' _i，k，h (t) represents the generated power, Q ', of the genset at time h for the planned starting year of the planned cycle' _i，k Power generation representing planned initial years of a planned cycleThe credible capacity of the unit; l is _i，h (t) represents the load amount of each candidate area at the h-th time of the t-th year of the power system,

indicating the load shedding amount of each candidate region at the h-th time of the t year of the power system; phi' _i，k，h 、Φ″ _i，k，h And phi _i，k，h Representing a dual multiplier corresponding to the second target constraint condition;

the investment cost operation model is expressed by a functional relation, the operation result of the investment cost operation model is expressed by UB, and the UB satisfies the following formula: UB = min { c _inv (x)+J _sub }; wherein, J _sub Represents the operating result of the operating submodel, J _sub Satisfying the following formula: j is a unit of _sub ＝min{c _op (y)+c _co2 (y)+c _ens (y) }; where y denotes a continuous variable in the run submodel, c _op (y) represents the running cost of each candidate area discounted at the end of the t-th year of the power system, c _co2 (y) represents the carbon emission cost of each candidate region discounted at the end of the t year of the power system, c _ens (y) represents reliability costs incurred by each candidate area at the end of the t-th year of the power system.

In one embodiment, the first target constraint is represented as a functional relationship, the first target constraint includes a first formula, and the first formula satisfies the following formula:

wherein i represents each candidate region, Q _i，k (t) is a discrete variable in the planning decision model, Q _i，k (t) installed capacity, Φ, of kth genset type invested in construction of power system in year t for each candidate region _i.k Representing the trusted capacity, Q 'of the newly commissioned generator set' _i，k Installed capacity of generator sets, Φ ', representing the planned starting year of the planned cycle' _i，k Plan initial year representing plan periodThe trusted capacity of the generator set of (c),

representing the maximum load of the power system, R (t) representing the rotational standby requirement of the electronic system;

the second target constraint condition is expressed by a functional relation, the second target constraint condition comprises a second formula, a third formula and a fourth formula, and the second formula satisfies the following formula: p _i，k，h (t)≤∑ _t Q _i，k (t), the third formula satisfies the following formula: p' _i，k，h (t)≤Q′ _i， k, the fourth formula satisfies the following formula:

wherein, P _i，k，h (t) power generation power, Q, of the kth genset type in the power system for each candidate area _i，k (t) indicating installed capacity of the kth generator set type planned and constructed in the t year of the power system in each candidate region; p' _i，k，h (t) represents the generated power, Q 'at time h for the genset at the planned start year of the planned cycle' _i，k A trusted capacity of the generator set representing a planned start year of the planned cycle; l is a radical of an alcohol _i，h (t) represents the load amount of each candidate area at the h-th time of the t-th year of the power system,

In one embodiment, the generator set planning decision optimization model is represented by a functional relationship, and satisfies the following formula:

wherein, J _O Representing a generator set planning decision optimization model, T representing a planning period, lambda representing a discount rate, c _inv (t) represents investment costs of each candidate area discounted at the end of the t-th year of the power system, c _op (t) represents the running cost of each candidate area discounted at the end of the t year of the power system, c _co2 (t) represents the carbon emission cost of each candidate district discounted at the end of the t year of the power system, c _ens (t) represents reliability costs incurred by each candidate area at the end of the t-th year of the power system.

In one embodiment, the investment cost corresponding to each candidate region is c _inv (t) represents c _inv (t) satisfies the following formula: c. C _inv (t)＝Q _i，k (t)θ _k I denotes each candidate area, Q _i，k (t) represents the installed capacity of the kth generator set type planned and constructed in the t year of each candidate region of the power system, theta _k A unit capacity installed cost representing the kth genset type at each candidate area of the power system; operation cost c for each candidate area _op (t) represents c _op (t) satisfies the following formula: c. C _op (t)＝∑ _i，k，h P _i，k，h (t)ρ _k ，P _i，k，h (t) represents the generated power of the kth power generation group type in each candidate area of the power system, ρ _k A power generation cost per unit generated power representing the kth genset type at each candidate region of the power system; carbon emission cost for each candidate area c _co2 (t) represents c _co2 (t) satisfies the following formula: c. C _co2 (t)＝∑ _i，k ∑ _h P _i，k，h (t)μ(t)(1-γ _i，k ) And μ (t) represents an average price of carbon emissions in t-th year in each candidate area of the power system, γ _i，k A carbon emission quota ratio representing a kth power generation group type at each candidate region of the power system; reliability cost for each candidate area c _ens (t) represents c _ens (t) satisfies the following formula:

The various modules in the above-described carbon emission cost-incurring genset planning apparatus may be implemented in whole or in part by software, hardware, and combinations 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, and its internal structure diagram may be as shown in fig. 6. 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 includes a non-volatile 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 equipment is used for storing a planning decision main model and an operation sub model corresponding to the generator set planning decision optimization model. 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 method of generator set planning that introduces carbon emission costs.

Those skilled in the art will appreciate that the architecture shown in fig. 6 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 further provided, which includes a memory and a processor, the memory stores a computer program, and the processor implements the steps of the above method embodiments when executing the computer program.

In an embodiment, a computer-readable storage medium is provided, on which a computer program is stored which, when being executed by a processor, carries out the steps of the above-mentioned method embodiments.

In an embodiment, a computer program product is provided, comprising a computer program which, when executed by a processor, carries out the steps in the method embodiments described above.

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, databases, or other media used in the embodiments provided herein can include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high-density embedded nonvolatile Memory, resistive Random Access Memory (ReRAM), magnetic Random Access Memory (MRAM), ferroelectric Random Access Memory (FRAM), phase Change Memory (PCM), graphene Memory, and the like. Volatile Memory can include Random Access Memory (RAM), external cache Memory, and the like. 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). The databases referred to in various embodiments provided herein may include at least one of relational and non-relational databases. The non-relational database may include, but is not limited to, a block chain based distributed database, and the like. The processors referred to in the embodiments provided herein may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic devices, quantum computing based data processing logic devices, etc., without limitation.

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 construed as limiting the scope of the present application. 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 application shall be subject to the appended claims.

Claims

1. A method of generator set planning incorporating carbon emission costs, comprising:

acquiring a generator set planning request sent by a request terminal; the generator set planning request comprises candidate regions and investment cost, operation cost, carbon emission cost and reliability cost corresponding to each candidate region;

and feeding back the generator set planning result to the request terminal.

2. The method of claim 1, wherein the target constraints include a first target constraint and a second target constraint, and wherein determining a target region for construction of a generator set, a type of the generator set at the target region, and an installed capacity of the generator set at the target region from the candidate regions based on the investment cost, the operating cost, the carbon emission cost, and the reliability cost corresponding to each of the candidate regions, the planning decision master model, the operating submodel, and the target constraints comprises:

3. The method of claim 2,

the planning decision-making main model is expressed by a functional relation, and the operation result of the planning decision-making main model is represented by J _mastet Is represented by the formula J _mastet Satisfies the following formula: j is a unit of _mastet ＝min{c _inv (x) + β (x) }; the x represents a discrete variable in the planning decision main model, the c _inv (x) Representing investment costs of each of the candidate regions discounted at the end of the t year of the power system; the β (x) represents a Benders cut constraint of the planning decision main model, the β (x) satisfying the following formula:

J _sub represents the result of the operation of the operating sub-model, and->

Satisfies the following formula:

wherein i represents each of the candidate regions, and P _i,k,h (t) represents generated power of the kth genset type in the power system for each of the candidate regions, the Q _i,k (t) is said x, said Q _i,k (t) indicating installed capacity of a kth genset type for which each of the candidate regions was planned to be constructed in the t year of the power system; p' _i,k,h (t) represents the generated power at the h time of the generator set of the planned starting year of the planned cycle, Q' _i,k Representing a credible capacity of the generator set for a planned start year of the planned cycle; said L _i,h (t) represents a load amount of each of the candidate regions at a time h of the t year of the power system, the load amount being calculated based on the load amount

Representing a load shedding amount of each of the candidate regions at a time h of the t year of the power system; phi 'to' _i,k,h Phi ″', described _i,k,h And the phi' _i,k,h Representing a dual multiplier corresponding to the second target constraint condition;

the investment cost operation model is expressed by a functional relation, the operation result of the investment cost operation model is expressed by UB, and the UB satisfies the following formula: UB = min { c _inv (x)+J _sub }; wherein, the J is _sub Represents the operation result of the operation submodel, J _sub Satisfies the following formula: j is a unit of _sub ＝min{c _op (y)+c _co2 (y)+c _ens (y) }; wherein y represents a continuous variable in the run submodel, c _op (y) represents the operating cost discounted by each of the candidate regions at the end of the t year of the power system, c _co2 (y) represents the carbon discounted by each of the candidate regions at the end of the t-th year of the power systemCost of emissions, said c _ens (y) represents a reliability cost discounted by each of the candidate regions at the end of the t-th year of the power system.

4. The method of claim 2,

the first target constraint is expressed by a functional relationship, the first target constraint comprises a first formula, and the first formula satisfies the following formula:

wherein i represents each of the candidate regions, and Q _i,k (t) is a discrete variable in the planning decision model, Q _i,k (t) represents installed capacity of kth genset type at the t year investment construction of the power system for each of the candidate regions, the Φ _i，k Representing the trusted capacity, Q ', of the newly commissioned generator set' _i,k Represents an installed capacity of a generator set of a planned starting year of the planned cycle, Φ' _i,k A trusted capacity of the generator set representing a planned start year of the planned cycle, the

the second target constraint condition is expressed by a functional relation, the second target constraint condition comprises a second formula, a third formula and a fourth formula, and the second formula satisfies the following formula: p is _i,k,h (t)≤∑ _t Q _i,k (t), the third formula satisfying the following formula: p' _i,k,h (t)≤Q′ _i,k The fourth formula satisfies the following formula:

wherein, the P is _i,k,h (t) represents the generated power of the kth genset type in the power system for each of the candidate regions, the Q _i,k (t) indicating installed capacity of a kth genset type for which each of the candidate regions was planned for construction in a t year of the power system; p' _i,k,h (t) represents the generated power at time h for the genset of the planned starting year of the planned cycle, Q' _i,k A trusted capacity of the generator set representing a planned start year of the planned cycle; said L _i,h (t) represents a load amount of each of the candidate regions at a time h of the t year of the power system, the load amount being calculated based on the load amount

And a load shedding amount of each of the candidate regions at a time h of the t year of the power system is indicated.

5. The method of claim 1, wherein the generator set planning decision optimization model is expressed as a functional relationship, and wherein the generator set planning decision optimization model satisfies the following equation:

wherein, the J is _O Representing the generator set planning decision optimization model, the T representing a planning period, the lambda representing a discount rate, the c _inv (t) represents the investment cost of each of the candidate regions discounted at the end of the t year of the power system, c _op (t) represents the operating cost discounted by each of the candidate regions at the end of the t year of the power system, c _co2 (t) represents carbon emission costs incurred by each of the candidate regions at the end of the t year of the power system, c _ens (t) represents reliability costs incurred by each of the candidate regions at the end of the t-th year of the power system.

6. The method of claim 1,

investment cost c corresponding to each of the candidate regions _inv (t) represents the formula c _inv (t) satisfies the following formula: c. C _inv (t)＝Q _i,k (t)θ _k I represents each of the candidate regions, and Q _i,k (t) represents installed capacity of kth generator set type planned and constructed in t year of each of the candidate regions of the power system, and θ _k A cost per capacity installed representing a kth genset type at each of the candidate regions of the power system;

operation cost c corresponding to each candidate region _op (t) represents, said c _op (t) satisfies the following formula: c. C _op (t)＝∑ _i,k,h P _i,k,h (t)ρ _k Said P is _i,k,h (t) represents the generated power of the kth power generation group type at each of the candidate regions of the power system, the ρ _k A cost of power generation representing a unit of generated power of a kth genset type at each of the candidate regions of the power system;

carbon emission cost c for each of the candidate regions _co2 (t) represents, said c _co2 (t) satisfies the following formula: c. C _co2 (t)＝∑ _i,k ∑ _h P _i,k,h (t)μ(t)(1-γ _i,k ) μ (t) represents an average price of carbon emissions in the t-year for each of the candidate regions of the power system, and γ _i,k A carbon emission allowance ratio representing a kth power generation group type at each of the candidate regions of the power system;

said +>

And a unit loss value in the t year of each candidate region of the power system.

7. A generator set planning apparatus that incurs carbon emissions costs, comprising:

the acquisition module is used for acquiring a generator set planning request sent by a request terminal; the generator set planning request comprises candidate regions, and investment cost, operation cost, carbon emission cost and reliability cost corresponding to each candidate region;

a determining module, configured to determine, according to the investment cost, the operation cost, the carbon emission cost, the reliability cost, the planning decision main model, the operation submodel, and a target constraint condition corresponding to each candidate region, a target region for building a generator set, a type of the generator set in the target region, and an installed capacity of the generator set in the target region from the candidate region;

8. 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 6.

9. 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 6.

10. A computer program product comprising a computer program, characterized in that the computer program, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 6.