CN115907155B

CN115907155B - Generator set planning method and device for introducing carbon emission cost

Info

Publication number: CN115907155B
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: 2023-07-18
Anticipated expiration: 2042-11-22
Also published as: CN115907155A

Abstract

The present application relates to a method, apparatus, computer device, storage medium and program product for generating set planning introducing carbon emission costs. Comprising 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 a running sub model corresponding to a generator set planning decision optimization model; determining 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 from the candidate areas according to the investment cost, the operation cost, the carbon emission cost and the reliability cost, the planning decision main model, the operation sub model and the target constraint condition corresponding to each candidate area; 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 planning result of the generator set 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 for introducing carbon emission cost

Technical Field

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

Background

Generally, for the generator set planning of the power system, the linear planning or the nonlinear planning can be adopted to realize the planning of the generator set by combining load prediction data, economic and reasonable requirements, standby capacity requirements and the like. The linear rule is to linearize the model and realize the generator set planning through a linear programming algorithm. Nonlinear programming is the implementation of genset programming by constructing a nonlinear model of genset programming.

However, the above approach has low accuracy in planning the genset.

Disclosure of Invention

In view of the foregoing, it is desirable to provide a method, an apparatus, a computer device, a computer readable storage medium, and a computer program product for generating set planning that can improve the planning accuracy of generating sets and introduce carbon emission costs.

In a first aspect, the present application provides a method of generating set planning for introducing carbon emission costs, comprising:

acquiring a generator set planning request sent by a request terminal; the generating set planning request comprises candidate areas, and investment cost, running cost, carbon emission cost and reliability cost corresponding to each candidate area;

Responding to the generator set planning request, and calling a planning decision main model and a running sub model corresponding to a generator set planning decision optimization model;

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 from the candidate areas according to the investment cost, the running cost, the carbon emission cost, the reliability cost, the planning decision main model, the running sub model and the target constraint condition corresponding to each candidate area;

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 constraint condition includes a first target constraint condition and a second target constraint condition, and the determining, from the candidate regions, a target region for constructing 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 according to the investment cost, the operation cost, the carbon emission cost, and the reliability cost corresponding to each of the candidate regions includes:

Initializing a bender cut constraint of the planning decision main model, and then solving discrete variables in the planning decision main model according to the initialized bender cut constraint and the first target constraint condition to obtain a first solving result; the first solving result is the installed capacity of the type of the generator set planned and built in the t-th year of the power system in each candidate region, the nodes cutting constraint is related to nodes optimizing cutting, and the nodes optimizing cutting is obtained according to the second target constraint condition and the pair 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 k-th generator set type of the power system in each candidate region;

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

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 variable of the k-th planning decision main model; wherein the operating result of the investment cost operating model is a sum of the investment cost of minimizing the trade-off of each candidate region at the end of the t-th year of the power system and the operating result of the operating sub-model.

In one embodiment, the planning decision master model is represented by a functional relationship, and the operation result of the planning decision master model is represented by J _mastet Representing, the J _mastet The following formula is satisfied: j (J) _mastet ＝min{c _inv (x) +β (x) }; the x represents a discrete variable in the planning decision master model, the c _inv (x) Representing investment costs for each of the candidate regions at a trade-off at the end of the t-th year of the power system; the beta (x) represents the Benders cut constraint of the planning decision master model, and the beta (x) satisfies the following formula: J _sub representing the result of the operation of said operation sub-model, -, etc. >A bender optimization cut representing discrete variables in the planning decisions connecting the main planning decision model and the run sub-model back to the run sub-model, said +.>The following formula is satisfied:

wherein i represents each of the candidate regions, the P _i，k，h (t) represents the generated power of each candidate region at the kth genset type of the power system, the Q _i，k (t) is said x, said Q _i，k (t) representing installed capacity of a kth genset type planned to be built in a nth year of the power system for each of the candidate regions; said P' _i，k，h (t) the generator set representing the planned starting year of the planning period is at the firstThe power generated at the time h is Q' _i，k A trusted capacity of the genset representing a planned starting year of the planning period; the L is _i，h (t) represents the load amount of each of the candidate regions at the h time of the t-th year of the power system, theRepresenting a cut load amount of each candidate region at an h time of a t-th year of the power system; said phi' _i，k，h Said phi _i，k，h Said phi ', and' _i，k，h Representing a pair multiplier corresponding to the second target constraint condition;

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

In one embodiment, the first target constraint is expressed in terms of a functional relationship, the first target constraint comprising a first formula that satisfies the following formula:

wherein i represents each of the candidate regions, the Q _i，k (t) is a discrete variable in a planning decision model, the Q _i，k (t) representsA installed capacity of a kth generator set type built by each candidate region at a nth investment of the electric power system, wherein phi _i.k Representing the trusted capacity of a newly added operational genset, said Q' _i，k Representing the installed capacity of the generator set for the planned start year of the planning period, said Φ' _i，k A trusted capacity of the genset representing a planned start year of the planning period, the Representing a maximum load of the power system, the R (t) representing a 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 (P) _i，k，h (t)≤∑ _t Qi _，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 the P is _i，k，h (t) represents the generated power of each candidate region at the kth genset type of the power system, the Q _i，k (t) representing installed capacity of a kth genset type planned to be built in a nth year of the power system for each of the candidate regions; said P' _i，k，h (t) represents the power generated by the generator set at the h time of the planning start year of the planning period, the Q' _i，k A trusted capacity of the genset representing a planned starting year of the planning period; the L is _i，h (t) represents the load amount of each of the candidate regions at the h time of the t-th year of the power system, theAnd representing the cut load amount of each candidate region at the h time of the t 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 _O Representing the generator set planning decision optimization model, wherein T represents a planning period, lambda represents a discount rate, and c _inv (t) represents the investment cost of each of said candidate regions at the end of the t-th year of the power system, said c _op (t) represents the running cost of each of the candidate regions at the end of the t-th year of the power system, the c _co2 (t) represents the carbon emission costs of each of the candidate regions at the end of the t-th year of the electric power system, the c _ens (t) represents a reliability cost for each of the candidate regions at a time of a t-th year of the power system.

In one of the embodiments of the present invention,

c for investment cost corresponding to each candidate region _inv (t) represents, the c _inv (t) satisfies the following formula: c _inv (t)＝Q _i，k (t)θ _k I represents each of the candidate regions, the Q _i，k (t) represents the installed capacity of a kth genset type planned to be built in a t-th year of each of the candidate regions of the power system, the θ _k A unit capacity installed cost representing a kth genset type in each of the candidate regions of the power system;

c for running cost corresponding to each candidate region _op (t) represents, the c _op (t) satisfies the following formula: c _op (t)＝∑ _i，k，h P _i，k，h (t)ρ _k The P is _i，k，h (t) represents the generation power of the kth genset type in each of the candidate regions of the power system, the ρ _k Unit power generation representing a kth genset type in each of the candidate regions of the power systemIs a power generation cost of the (a);

c for carbon emission cost corresponding to each candidate region _co2 (t) represents, the c _co2 (t) satisfies the following formula: c _co 2(t)＝∑ _i，k ∑ _h P _i，k，h (t)μ(t)(1-γ _i，k ) The μ (t) represents an average price of carbon emissions at the t-th year of each of the candidate regions of the electric power system, the γ _i，k A carbon emission allowance ratio representing a kth genset type at each of the candidate regions of the power system;

reliability cost c corresponding to each candidate region _ens (t) represents, the c _ens (t) satisfies the following formula:said->Representing a cut load amount at an h-th time of a t-th year of each of the candidate regions of the electric power system, the +.>Indicating a unit load shedding value at a t-th year of each of the candidate regions of the power system.

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

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

The calling module is used for responding to the generator set planning request and calling a planning decision main model and a running sub model corresponding to the generator set planning decision optimization model;

the determining module is used for 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 from the candidate areas according to the investment cost, the running cost, the carbon emission cost, the reliability cost, the planning decision main model, the running sub model and the target constraint condition corresponding to each candidate area;

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 planning result of the generator set 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 which when executing the computer program performs the steps of:

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

In a fourth aspect, the present application also 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 also provides a computer program product. The computer program product comprises a computer program which, when executed by a processor, implements the steps of:

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

The method, the device, the computer equipment, the storage medium and the computer program product for planning the generator set, which introduce the carbon emission cost, are characterized in that the generator set planning request sent by the request terminal is obtained, and the generator set planning request comprises candidate areas, and investment cost, running cost, carbon emission cost and reliability cost corresponding to each candidate area; and responding to the generator set planning request, and obtaining a generator set planning result according to the investment cost, the running cost, the carbon emission cost and the reliability cost corresponding to each candidate region, the planning decision main model, the running sub model and the target constraint condition by calling a planning decision main model and a running sub model corresponding to the generator set planning decision optimization model, so that a target region for building the generator set, the type of the generator set in the target region and the installed capacity of the generator set in the target region can be determined from the candidate region, and the generator set planning result is fed back to the request terminal according to the target region, the type of the generator set in the target region and the installed capacity of the generator set in the target region. 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 realized from an economic angle, meanwhile, the influence of carbon emission on the planning of the generator set is also considered, and the accuracy of the planning of the generator set can be improved.

Drawings

FIG. 1 is an application environment diagram of a method of generating set planning incorporating carbon emission costs in one embodiment;

FIG. 2 is a flow diagram of a method of generating set planning incorporating carbon emission costs in one embodiment;

FIG. 3 is a schematic flow diagram of determining a target region for building a generator set, a type of the generator set in the target region, and a installed capacity of the generator set in the target region from candidate regions according to investment costs, operation costs, carbon emission costs, and reliability costs, a planning decision main model, an operation sub model, and target constraint conditions corresponding to each candidate region in one embodiment;

FIG. 4 is a schematic diagram of an iterative solution between a planning decision master model and a run sub model in one embodiment;

FIG. 5 is a block diagram of a generator set planning apparatus incorporating carbon emission costs in one embodiment;

fig. 6 is an internal structural diagram of a computer device in one embodiment.

Detailed Description

In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.

When the linear programming or the nonlinear programming is adopted to realize the programming of the generator set, the influence of carbon emission cost on the programming result is not considered, and the external cost of the environment faced by different generator set types is different, so that the programming accuracy is low.

Based on this, the present application provides a method for planning a generator set, which introduces carbon emission costs, and the method of the present application may be applied to the application environment shown in fig. 1. The request terminal 102 communicates with the server 104 through a network, and the request terminal 102 may be, but not limited to, various personal computers, notebook computers, smartphones, and tablet computers, and the data storage system may store data that needs to be processed by the server 104, and the data storage system may be integrated on the server 104, or may be placed on a cloud or other network servers.

Specifically, the requesting terminal 102 sends a generator set planning request to the server 104, where the generator set planning request includes candidate regions, and investment costs, running costs, carbon emission costs, and reliability costs corresponding to each candidate region. The server 104 calls a planning decision master model and a running sub-model corresponding to the generating set planning decision optimization model in response to the generating set planning request. The server 104 determines a target region for constructing the generator set, the type of the generator set in the target region and the installed capacity of the generator set in the target region from the candidate regions 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 region, the type of the generator set in the target region and the installed capacity of the generator set in the target region, and the server 104 feeds back the generator set planning result to the request terminal 102, so that a staff 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 schematic flow chart of a method for planning a generator set for introducing carbon emission costs is provided, and the method is applied to the server 104 in fig. 1, for example, and includes the following steps:

s202, acquiring a generator set planning request sent by a request terminal.

In this embodiment, the genset planning request includes candidate regions, and investment costs, operating costs, carbon emission costs, and reliability costs corresponding to each candidate region. The candidate region may include a region where a generator set is to be built and a region where a generator set has been built. The investment costs, operating costs, carbon emission costs, and reliability costs for each candidate region may be obtained by:

c for investment cost corresponding to each candidate region _inv (t) represents, c _inv (t) satisfies the following formula: c _inv (t)＝Q _i，k (t)θ _k . Wherein i represents each candidate region, Q _i，k (t) the installed capacity of the kth genset type planned and built in the t-th year in each candidate region of the power system, θ _k The installed cost per unit capacity for the kth genset type in each candidate region of the power system is represented.

C for operation cost corresponding to each candidate region _op (t) represents, c _op (t) satisfies the following formula: c _op (t)＝∑ _i， _kh P _i，k，h (t)ρ _k . Wherein P is _i，k，h (t) represents the generation power of the kth genset type in each candidate region of the power system, ρ _k The generation cost per unit generation power of the kth genset type in each candidate region of the power system is represented.

C for carbon emission cost corresponding to each candidate region _co2 (t) represents, c _co2 (t) satisfies the following formula: c _co2 (t)＝∑ _i，k ∑ _h P _i，k，h (t)μ(t)(1-γ _i，k ). Wherein μ (t) represents the average price of carbon emissions at the t-th year in each candidate region of the power system, γ _i，k Carbon emission allowance ratio of kth genset type in each candidate region of the power system is expressed.

Reliability cost corresponding to each candidate region c _ens (t) represents, c _ens (t) satisfies the following formula:wherein (1)>Representing the tangential load amount at the h time of the t year in each candidate region of the power system, +.>The unit load loss value at the t-th year in each candidate region of the power system is shown.

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

In this embodiment, from an economic standpoint, the sum of the investment cost, the running cost, the carbon emission cost, and the reliability cost in the planning period can be minimized to obtain the generator set planning decision optimization model. The generator set planning decision optimization model can be expressed by a functional relation, and the generator set planning decision optimization model meets the following formula:

Wherein J is _O Represents a generator set planning decision optimization model, T represents a planning period, lambda represents a discount rate, and c _inv (t) represents the investment cost of each candidate region at the end of the t-th year of the power system, c _op (t) represents the running cost of each candidate region at the end of the t-th year of the power system, c _co2 (t) represents the carbon emission costs of each candidate region at the end of the t-th year of the electric power system, c _ens And (t) represents the reliability cost of each candidate region at the end of the t-th year of the power system.

In the method, when the planning of the generator set is realized based on the generator set planning decision optimization model, the generator set planning decision optimization model can be decomposed into two constraint optimization models, and 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 each period.

S206, determining 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 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 condition corresponding to each candidate area.

In this embodiment, the target constraint condition is used to select a region where the generator set is built from among the candidate regions, where the target constraint condition includes a constraint condition that an upper limit of power generation power of the generator set in the newly-added operation and planning start year does not exceed a constraint condition corresponding to an installed capacity of the generator set, a constraint condition corresponding to real-time balance of power generation power and load of the power system, and a constraint condition corresponding to a requirement of the power system operation on rotation standby.

Further, the service may determine a target region for constructing the generator set, a type of the generator set in the target region, and a installed capacity of the generator set in the target region from the candidate regions 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, which is not limited in this embodiment.

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

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

It will be appreciated that the type of genset built in the target area and the maximum power generation capacity of the genset built in the target area may be determined based on the target area, the type of genset in the target area, and the installed capacity of the genset in the target area.

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 staff may execute a subsequent procedure based on the generator set planning result on the request terminal.

In summary, in the embodiment shown in fig. 2, by acquiring a generator set planning request sent by a request terminal, the generator set planning request includes candidate regions, and investment costs, operation costs, carbon emission costs, and reliability costs corresponding to each candidate region; and responding to the generator set planning request, and obtaining a generator set planning result according to the investment cost, the running cost, the carbon emission cost and the reliability cost corresponding to each candidate region, the planning decision main model, the running sub model and the target constraint condition by calling a planning decision main model and a running sub model corresponding to the generator set planning decision optimization model, so that a target region for building the generator set, the type of the generator set in the target region and the installed capacity of the generator set in the target region can be determined from the candidate region, and the generator set planning result is fed back to the request terminal according to the target region, the type of the generator set in the target region and the installed capacity of the generator set in the target region. 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 realized from an economic angle, meanwhile, the influence of carbon emission on the planning of the generator set is also considered, and the accuracy of planning of the generator set can be improved.

On the basis of the embodiment shown in fig. 2, in one embodiment, the target constraint conditions include a first target constraint condition and a second target constraint condition, and as shown in fig. 3, a flow chart of determining a target area for constructing the generator set, a type of the generator set in the target area and a installed capacity of the generator set in the target area from the candidate areas according to investment costs, operation costs, carbon emission costs and reliability costs, planning decision main models, operation sub models and target constraint conditions corresponding to each candidate area is provided, and the flow chart includes the following steps:

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

In this embodiment, the first target constraint condition refers to a constraint condition corresponding to a requirement for rotation standby in operation of the power system, where the first target constraint condition may be expressed by a functional relationship, and 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) represents the installed capacity of the kth generator set type built by each candidate region at the t-th investment of the electric power system, phi _i.k Representing the trusted capacity, Q ', of a newly added operational generator set' _i，k Installed capacity of generator set representing planned initial year of planning period, Φ' _i，k The trusted capacity of the genset representing the planned starting year of the planning period,representing the maximum load of the power system, R (t) represents the rotational standby requirement of the electronic system.

In this embodiment, the Benders cut constraint relates to a Benders optimization cut that is derived from the second target constraint and the corresponding pair multiplier for the second target constraint. Specifically, the main planning decision model is a function of minimizing investment cost of the generator set in a planning period and the sum of running cost, carbon emission cost and reliability cost returned by the running sub model for the discrete planning decision variable x, the main planning decision model can be represented by a functional relation, and the running result of the main planning decision model can be represented by J _mastet Representation, J _mastet The following formula is satisfied:

J _mastet ＝min{c _inv (x)+β(x)}；

where x represents the discrete variable in the planning decision master model, c _inv (x) Representing the investment costs of each candidate region for trade-off at the end of the t-th year of the power system, β (x) represents the Benders cut constraint of the planning decision master model.

It can be understood that by initializing β (x), the 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 built in the t-th year of the electric power system in each candidate region, that is, according to the first target constraint condition and the planning decision main model, the discrete variable x is solved, and the first solution result obtained by combining the calculation formula of the investment cost described in S202 is Q _i，k (t)。

Where β (x) can be expressed in a functional relationship when β (x) is not initialized, β (x) satisfies the following formula:J _su b represents the result of running the sub-model, +.>Benders optimization cuts representing discrete variables in the planning decisions connecting the planning decision master model and the running sub-model returned by the running sub-model. The information interaction between the planning decision main model and the operation sub model can be realized through the nodes optimization cut.

Specifically, the Benders optimization cut may be constructed by running a second target constraint corresponding to the sub-model and a pair 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 appreciated that, since the dual multiplier has marginal meaning, the nodes cut constraint only contains part of the information of the running sub-model, and the optimization result of the planning decision main model can meet the nodes cut constraint, but cannot guarantee that the constraint of the running sub-model is completely met. Therefore, after solving the planning decision main model, the operation sub-model needs to be calculated again, which is a process of iterating the planning decision main model and the operation sub-model back and forth, and the final generating set planning result is obtained through the iterating process.

In particular, the method comprises the steps of,the following formula is satisfied:

wherein i represents each candidate region, P _i，k，h (t) represents the generated power of the kth generator set type in the electric power system in each candidate region, Q _i，k (t) is x, Q _i，k (t) representing installed capacity of a kth generator set type planned to be built in a nth year of the electric power system in each candidate region; p'. _i，k，h (t) represents the power generated by the generator set at the h time point in the planning start year of the planning period, Q' _i，k A trusted capacity of the genset representing a planned starting year of the planning period; l (L) _i，h (t) represents the load amount of each candidate region at the h time of the t-th year of the power system,representing the cut load amount of each candidate region at the h time of the t year of the power system; phi's' _i，k，h 、Φ″ _i，k，h Phi'. _i，k，h And representing the pair multiplier corresponding to the second target constraint condition.

And S304, solving the 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 means that the upper limit of the generated power of the generating set of the newly-added operation and the planned starting year does not exceed the upper limitAnd the constraint condition corresponding to the installed capacity, wherein the second target constraint condition is expressed by a functional relation, and the second target constraint condition comprises a second formula, a third formula and a fourth formula. The second formula satisfies the following formula: p (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 is _i，k，h (t) represents the generated power of the kth generator set type in the electric power system in each candidate region, Q _i，k (t) representing installed capacity of a kth generator set type planned to be built in a nth year of the electric power system in each candidate region; p'. _i，k，h (t) represents the power generated by the generator set at the h time point in the planning start year of the planning period, Q' _i，k A trusted capacity of the genset representing a planned starting year of the planning period; l (L) _i，h (t) represents the load amount of each candidate region at the h time of the t-th year of the power system, The cut load amount of each candidate region at the h time of the t-th year of the power system is shown.

In this embodiment, the operation result of the operation sub-model may be J _sub Representation, J _sub The following formula is satisfied: j (J) _sub ＝min{c _op (y)+c _co2 (y)+c _ens (y) }. Wherein y represents a continuous variable in the run sub-model, c _op (y) represents the running cost of each candidate region at the end of the t-th year of the power system, c _co2 (y) represents the carbon emission costs of each candidate region at the end of the t-th year of the electric power system, c _ens (y) represents the reliability cost of each candidate region at the end of the t-th year of the power system.

It will be appreciated that, based on the first solution (i.e., Q _i，k (t)), the second target constraint condition and the second solving result obtained by running the submodel are candidatesThe power generated by the kth genset type of the regional power system, i.e. according to the first solution (i.e. Q _i，k (t)), a second target constraint condition and an operation submodel, solving for a discrete variable y, and calculating formulas of the operation cost, the carbon emission cost and the reliability cost described in connection with S202, wherein the obtained second solving result is P _i，k，h (t)。

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

In the present application, the second solution result (i.e., P _i，k，h (t)) under the known condition, substituting the second solving result into a formula corresponding to the nodes optimization cut to obtain nodes cutting constraint of the corresponding planning decision main model, and re-solving discrete variables in the planning decision main model by combining the first target constraint condition, the nodes cutting constraint of the corresponding planning decision main model and the planning decision main model, namely re-obtaining Q _i，k (t); retrieve Q _i，k After (t), the recovered Q can be used _i，k (t) substituting the target constraint condition into a formula corresponding to the operation submodel, and combining the target constraint condition to retrieve P _i，k，h (t), and so on, to achieve iterative solution to discrete variables in the planning decision master 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 smaller than or equal to a preset value, determining 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 according to the discrete variable of the k-th planning decision main model.

In this embodiment, the operation model of the investment cost may be represented by a functional relationship, the operation result of the operation model of the investment cost is the sum of the operation results of the operation sub-model and the investment cost for minimizing the trade-off of each candidate region at the end of the t-th year of the power system, and the operation result of the operation model of the investment cost may be represented by UB which satisfies the following equation The formula: ub=min { c _inv (x)+J _sub }. Wherein J is _sub Representing the result of the operation sub-model, x representing the discrete variables in the planning decision sub-model, c _inv (x) Representing the investment costs of each candidate region at the end of the t-th year of the power system.

Wherein if itRepresenting the result of the run of the planning decision master model for the kth time,/-> Representing the running result of the k-th investment cost running model, epsilon representing a preset value, and UB being equal to% ^k ) And when UL I is less than or equal to epsilon, determining the 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.

It will be appreciated that at |UB ^(k) At UL | > epsilon, the discrete variables of the current planning decision master model (i.e., Q _i，k (t)) as a known quantity, substituting the known quantity into a formula corresponding to the operation submodel, and combining a second target constraint condition to retrieve P _i，k，h (t) and based on the retrieved P _i，k，h (t) combining the first target constraint with the Benders cut constraint to retrieve Q _i，k (t) while judging whether or not |UB is satisfied ^(k) UL < epsilon, if so, according to the retrieved Q _i，k (t) determining a target area for construction of the generator set, a type of the generator set in the target area, and a installed capacity of the generator set in the target area; if not, continuing to carry out iteration solution again until meeting the requirement of UB ^(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 senders cut constraint, the planning decision main model and the operation submodel, the discrete variables in the planning decision main model are iteratively solved, so that when a certain condition is met, 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 are determined according to the result corresponding to the discrete variables in the planning decision main model; and 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 back the generator set planning result to the request terminal.

It can be understood that the method and the system can obtain the closest overall optimal generator set planning scheme in the dynamic planning process through multi-stage process optimizing, simultaneously consider the environmental external cost difference faced by different generator set types in the national carbon emission market environment, evaluate the total element cost of the generator set planning, consider the investment cost, the running cost, the carbon emission cost and the reliability cost of each candidate area in the planning of the generator set, realize the planning of the generator set from the economic angle, also consider the influence of carbon emission on the planning of the generator set, and improve the accuracy of the planning of the generator set.

In connection with what is shown in FIG. 3, in one embodiment, a schematic diagram of an iterative solution between a planning decision master model and a run submodel is provided, as shown in FIG. 4. In the process of implementing iterative solution between the planning decision main model and the operation sub model, according to the operation result of the operation sub model, the Benders cut constraints of different planning decision main models can be formed, and by returning the Benders cut constraints of different planning decision main models to the planning decision main model, the planning decision variables (i.e. discrete variables) in the planning decision main model can be solved, so that the generating set planning result can be 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 described above are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.

Based on the same inventive concept, the embodiment of the application also provides a carbon emission introducing generating set planning device for realizing the carbon emission introducing generating set planning method. The implementation of the solution provided by the device is similar to the implementation described in the above method, so the specific limitation in the embodiment of the generating set planning device for introducing carbon emission costs provided below may be referred to the limitation of the generating set planning method for introducing carbon emission costs hereinabove, and will not be repeated here.

In one embodiment, as shown in FIG. 5, there is provided a genset programming apparatus for introducing carbon emission costs, comprising: an acquisition module 502, a calling module 504, a determination module 506, a generation module 508, and a feedback module 510, wherein:

the acquiring module 502 is configured to acquire a generator set planning request sent by a request terminal; the generating set planning request comprises candidate areas, and investment cost, operation cost, carbon emission cost and reliability cost corresponding to each candidate area.

And the calling module 504 is used for responding to the generator set planning request and calling the planning decision main model and the running sub model corresponding to the generator set planning decision optimization model.

And the determining module 506 is 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 a installed capacity of the generator set in the target region according to investment costs, operation costs, carbon emission costs, reliability costs, planning decision main models, operation sub models, and target constraint conditions corresponding to the candidate regions.

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 the feedback module 510 is configured to feed back the 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 initialize a Benders cut constraint of the planning decision main model, and then solve 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 type of the generator set planned and built in the t-th year of the power system in each candidate region, the nodes cutting constraint is related to nodes optimizing cutting, and the nodes optimizing cutting is obtained according to the second target constraint condition and the pair 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 generating power of the kth generating set type of the electric power system in each candidate region; sequentially carrying out iterative solution on discrete variables in the planning decision main model according to the second solving result, the nodes cutting constraint, the first target constraint condition and the second target constraint condition; if the absolute value of the difference between the operation result of the operation model of the investment cost of the kth time and the operation result of the planning decision main model of the kth time 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 planning decision main model of the kth time; the operation result of the investment cost operation model is the sum of the investment cost and the operation result of the operation sub-model, wherein the investment cost and the operation result of the operation sub-model are minimized, and the investment cost is at the end of the t-th year of the power system in each candidate region.

In one embodiment, the planning decision master model is represented by a functional relationship, and the running result of the planning decision master model is represented by J _mastet Representation, J _mastet The following formula is satisfied: j (J) _mastet ＝min{c _inv (x) +β (x) }; x represents a discrete variable in the planning decision master model, c _inv (x) Representing investment costs for each candidate region at the end of the t-th year of the power system; beta (x) represents the Benders cut constraint of the planning decision master model, beta (x) satisfies the following equation:J _sub representing the result of running the sub-model, +.>Benders optimization cuts representing discrete variables in the planning decisions connecting the planning decision main model and the running sub-model returned by the running sub-model, +.>The following formula is satisfied:

wherein i represents each candidate region, P _i，k，h (t) represents the generated power of the kth generator set type in the electric power system in each candidate region, Q _i，k (t) is x, Q _i，k (t) representing installed capacity of a kth generator set type planned to be built in a nth year of the electric power system in each candidate region; p'. _i，k，h (t) represents the power generated by the generator set at the h time point in the planning start year of the planning period, Q' _i，k A trusted capacity of the genset representing a planned starting year of the planning period; l (L) _i，h (t) represents the load amount of each candidate region at the h time of the t-th year of the power system, Representing candidate regionsCut load amount at the h time of the t year of the power system; phi's' _i，k，h 、Φ″ _i，k，h Phi' "and _i，k，h representing a pair multiplier corresponding to the second target constraint condition;

the investment cost operation model is represented by a functional relation, the operation result of the investment cost operation model is represented by UB, and the UB satisfies the following formula: ub=min { c _inv (x)+J _sub -a }; wherein J is _sub Representing the result of running the sub-model, J _sub The following formula is satisfied: j (J) _sub ＝min{c _op (y)+c _co2 (y)+c _ens (y); wherein y represents a continuous variable in the run sub-model, c _op (y) represents the running cost of each candidate region at the end of the t-th year of the power system, c _co2 (y) represents the carbon emission costs of each candidate region at the end of the t-th year of the electric power system, c _ens (y) represents the reliability cost of each candidate region at the end of the t-th year of the power system.

In one embodiment, the first target constraint is expressed in a functional relationship, the first target constraint comprising 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) represents the installed capacity of the kth generator set type built by each candidate region at the t-th investment of the electric power system, phi _i.k Representing the trusted capacity, Q ', of a newly added operational generator set' _i，k Installed capacity of generator set representing planned initial year of planning period, Φ' _i，k The trusted capacity of the genset representing the planned starting year of the planning period,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 (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 is _i，k，h (t) represents the generated power of the kth generator set type in the electric power system in each candidate region, Q _i，k (t) representing installed capacity of a kth generator set type planned to be built in a nth year of the electric power system in each candidate region; p'. _i，k，h (t) represents the power generated by the generator set at the h time point in the planning start year of the planning period, Q' _i，k A trusted capacity of the genset representing a planned starting year of the planning period; l (L) _i，h (t) represents the load amount of each candidate region at the h time of the t-th year of the power system,the cut load amount of each candidate region at the h time of the t-th year of the power system is shown.

wherein J is _O Represents a generator set planning decision optimization model, T represents a planning period, lambda represents a discount rate, and c _inv (t) represents the investment cost of each candidate region at the end of the t-th year of the power system, c _op (t) represents each candidateOperating costs at the end of the t-th year of the electric power system in the region, c _co2 (t) represents the carbon emission costs of each candidate region at the end of the t-th year of the electric power system, c _ens And (t) represents the reliability cost of each candidate region at the end of the t-th year of the power system.

In one embodiment, the investment costs corresponding to each candidate region are c _inv (t) represents, c _inv (t) satisfies the following formula: c _inv (t)＝Q _i，k (t)θ _k I represents each candidate region, Q _i，k (t) the installed capacity of the kth genset type planned and built in the t-th year in each candidate region of the power system, θ _k A unit capacity installed cost representing a kth genset type in each candidate region of the power system; c for operation cost corresponding to each candidate region _op (t) represents, c _op (t) satisfies the following formula: c _op (t)＝∑ _i，k，h P _i，k，h (t)ρ _k ，P _i，k，h (t) represents the generation power of the kth genset type in each candidate region of the power system, ρ _k Generating costs representing unit generating power of a kth genset type in each candidate region of the power system; c for carbon emission cost corresponding to each candidate region _co2 (t) represents, c _co2 (t) satisfies the following formula: c _co2 (t)＝∑ _i，k ∑ _h P _i，k，h (t)μ(t)(1-γ _i，k ) Mu (t) represents the average price of carbon emissions at the t-th year in each candidate region of the power system, gamma _i，k A carbon emission allowance ratio representing a kth genset type at each candidate region of the power system; reliability cost corresponding to each candidate region c _ens (t) represents, c _ens (t) satisfies the following formula: representing the tangential load amount at the h time of the t year in each candidate region of the power system, +.>The unit load loss value at the t-th year in each candidate region of the power system is shown.

The various modules in the above-described genset planning apparatus that introduce carbon emission costs may be implemented in whole or in part by software, hardware, and combinations thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above 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. 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, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer equipment is used for storing a planning decision main model and a running sub model corresponding to the generating 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, when executed by a processor, implements a method of generating set planning that introduces carbon emission costs.

It will be appreciated by those skilled in the art that the structure shown in fig. 6 is merely a block diagram of some of the structures associated with the present application and is not limiting of the computer device to which the present application may be applied, and that a particular computer device may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.

In an embodiment, there is also provided a computer device comprising a memory and a processor, the memory having stored therein a computer program, the processor implementing the steps of the method embodiments described above when the computer program is executed.

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

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

Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in the various embodiments provided herein may 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 (Magnetoresistive RandomAccess Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (Phase Change Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (RandomAccess Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as static random access memory (Static RandomAccess Memory, SRAM) or dynamic random access memory (Dynamic RandomAccess Memory, DRAM), and the like. The databases referred to in the various embodiments provided herein may include at least one of relational databases and non-relational databases. The non-relational database may include, but is not limited to, a blockchain-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 units, quantum computing-based data processing logic units, etc., without being limited thereto.

The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.

The above examples only represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the present application. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application shall be subject to the appended claims.

Claims

1. A method of generating set planning for introducing carbon emission costs, comprising:

feeding back the generating set planning result to the request terminal;

the target constraint condition includes a first target constraint condition and a second target constraint condition, and the determining, from the candidate regions, a target region for constructing 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 according to the investment cost, the running cost, the carbon emission cost, and the reliability cost corresponding to each candidate region includes:

2. The method of claim 1, wherein the step of determining the position of the substrate comprises,

the planning decision main model is expressed by a functional relation, and the operation result of the planning decision main model is J _mastet Representing, the J _mastet The following formula is satisfied: j (J) _mastet ＝min{c _inv (x) +β (x) }; the x represents a discrete variable in the planning decision master model, the c _inv (x) Representing investment costs for each of the candidate regions at a trade-off at the end of the t-th year of the power system; the beta (x) represents the Benders cut constraint of the planning decision master model, and the beta (x) satisfies the following formula: J _sub Representing the result of the operation of said operation sub-model, -, etc.>A bender optimization cut representing discrete variables in the planning decisions connecting the main planning decision model and the run sub-model back to the run sub-model, said +.>The following formula is satisfied:

wherein i represents each of the candidate regions, the P _i,k,h (t) represents each of the candidate placesGenerating power of a kth genset type distinguished from the power system, the Q _i,k (t) is said x, said Q _i,j (t) representing installed capacity of a kth genset type planned to be built in a nth year of the power system for each of the candidate regions; said P' _i,k,h (t) represents the power generated by the generator set at the h time of the planning start year of the planning period, the Q' _i,k A trusted capacity of the genset representing a planned starting year of the planning period; the L is _i,h (t) represents the load amount of each of the candidate regions at the h time of the t-th year of the power system, theRepresenting a cut load amount of each candidate region at an h time of a t-th year of the power system; said phi' _i,k,h Said phi _i，k,h Said phi ', and' _i，k,h Representing a pair multiplier corresponding to the second target constraint condition;

the investment cost operation model is expressed by a functional relation, and an operation result of the investment cost operation model is expressed by UB, wherein the UB satisfies the following formula: ub=min { c _inv (x)+J _sub -a }; wherein the J _sub Representing the operation result of the operation sub-model, the J _sub The following formula is satisfied: j (J) _sub ＝min{c _op (y)+c _co2 (y)+c _ens (y); wherein y represents a continuous variable in the run sub-model, c _op (y) represents the running cost of each of the candidate regions at the end of the t-th year of the power system, c _co2 (y) represents the carbon emission costs of each of the candidate regions at the end of the t-th year of the electric power system, the c _ens (y) represents a reliability cost for each of the candidate regions at a time of a t-th year of the power system.

3. The method of claim 1, wherein the step of determining the position of the substrate comprises,

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

wherein i represents each of the candidate regions, the Q _i,k (t) is a discrete variable in a planning decision model, the Q _i,k (t) representing installed capacity of a kth genset type of each of the candidate regions invested in construction in a nth year of the electric power system, the Φ _i.k Representing the trusted capacity of a newly added operational genset, said Q' _i,k Representing the installed capacity of the generator set for the planned start year of the planning period, said Φ' _i,k A trusted capacity of the genset representing a planned start year of the planning period, theRepresenting a maximum load of the power system, the R (t) representing a 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 (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 the P is _i,k,h (t) represents the generated power of each candidate region at the kth genset type of the power system, the Q _i,k (t) representing installed capacity of a kth genset type planned to be built in a nth year of the power system for each of the candidate regions; said P' _i,k,h (t) represents the power generated by the generator set at the h moment in the planning start year of the planning period,said Q' _i,k A trusted capacity of the genset representing a planned starting year of the planning period; the L is _i,h (t) represents the load amount of each of the candidate regions at the h time of the t-th year of the power system, theAnd representing the cut load amount of each candidate region at the h time of the t year of the power system.

4. The method of claim 1, wherein the genset planning decision optimization model is represented by a functional relationship, the genset planning decision optimization model satisfying the following formula:

5. The method of claim 1, wherein the step of determining the position of the substrate comprises,

c for investment cost corresponding to each candidate region _inv (t) represents, the c _inv (t) satisfies the following formula: c _inv (t)＝Q _i,k (t)θ _k I represents each of the candidate regions, the Q _i,k (t) represents installed capacity of a kth genset type planned to be built in a nth year of each of the candidate regions of the power systemAmount of said theta _k A unit capacity installed cost representing a kth genset type in each of the candidate regions of the power system;

C for running cost corresponding to each candidate region _op (t) represents, the c _op (t) satisfies the following formula: c _op (t)＝∑ _i,k,h P _i,k,h (t)ρ _k The P is _i,k,h (t) represents the generation power of the kth genset type in each of the candidate regions of the power system, the ρ _k Generating cost representing unit generating power of a kth generator set type in each of the candidate regions of the power system, the h representing a time of day;

c for carbon emission cost corresponding to each candidate region _co2 (t) represents, the c _co2 (t) satisfies the following formula: c _co2 (t)＝∑ _i,k ∑ _h P _i,k,h (t)μ(t)(1-γ _i,k ) The μ (t) represents an average price of carbon emissions at the t-th year of each of the candidate regions of the electric power system, the γ _i,k A carbon emission allowance ratio representing a kth genset type at each of the candidate regions of the power system;

6. A generator set planning apparatus for introducing carbon emission costs, comprising:

the feedback module is used for feeding back the planning result of the generator set to the request terminal;

The target constraint condition comprises a first target constraint condition and a second target constraint condition, and the determining module is further used for: initializing a bender cut constraint of the planning decision main model, and then solving discrete variables in the planning decision main model according to the initialized bender cut constraint and the first target constraint condition to obtain a first solving result; the first solving result is the installed capacity of the type of the generator set planned and built in the t-th year of the power system in each candidate region, the nodes cutting constraint is related to nodes optimizing cutting, and the nodes optimizing cutting is obtained according to the second target constraint condition and the pair 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 k-th generator set type of the power system in each candidate region; sequentially carrying out iterative solution on discrete variables in the planning decision main model according to the second solution result, the nodes cutting constraint, the first target constraint condition and the second target constraint condition; 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 variable of the k-th planning decision main model; wherein the operating result of the investment cost operating model is a sum of the investment cost of minimizing the trade-off of each candidate region at the end of the t-th year of the power system and the operating result of the operating sub-model.

7. The apparatus of claim 6, wherein the device comprises a plurality of sensors,

the planning decision main model is expressed by a functional relation, and the operation result of the planning decision main model is J _mastet Representing, the J _mastet The following formula is satisfied: j (J) _mastet ＝min{c _inv (x) +β (x) }; the x represents a discrete variable in the planning decision master model, the c _inv (x) Representing investment costs for each of the candidate regions at a trade-off at the end of the t-th year of the power system; the beta (x) represents the Benders cut constraint of the planning decision master model, and the beta (x) satisfies the following formula:J _sub representing the result of the operation of said operation sub-model, -, etc.>Representing links running sub-model returnsA Benders optimization cut of discrete variables in the planning decisions of the planning decision main model and the running sub model, said +.>The following formula is satisfied:

wherein i represents each of the candidate regions, the P _i,k,h (t) represents the generated power of each candidate region at the kth genset type of the power system, the Q _i,k (t) is said x, said Q _i,k (t) representing installed capacity of a kth genset type planned to be built in a nth year of the power system for each of the candidate regions; said P' _i,k,h (t) represents the power generated by the generator set at the h time of the planning start year of the planning period, the Q' _i,k A trusted capacity of the genset representing a planned starting year of the planning period; the L is _i,h (t) represents the load amount of each of the candidate regions at the h time of the t-th year of the power system, theRepresenting a cut load amount of each candidate region at an h time of a t-th year of the power system; said phi' _i,k,h Said phi _i，k,h Said phi ', and' _i，k,h Representing a pair multiplier corresponding to the second target constraint condition;

the investment cost operation model is expressed by a functional relation, and an operation result of the investment cost operation model is expressed by UB, wherein the UB satisfies the following formula: ub=min { c _inv (x)+J _sub -a }; wherein the J _sub Representing the operation result of the operation sub-model, the J _sub The following formula is satisfied: j (J) _sub ＝min{c _op (y)+c _co2 (y)+c _ens (y); wherein said y represents said runningContinuous variable in submodel, c _op (y) represents the running cost of each of the candidate regions at the end of the t-th year of the power system, c _co2 (y) represents the carbon emission costs of each of the candidate regions at the end of the t-th year of the electric power system, the c _ens (y) represents a reliability cost for each of the candidate regions at a time of a t-th year of the power system.

8. The apparatus of claim 6, wherein the device comprises a plurality of sensors,

The generator set planning decision optimization model is expressed by a functional relation, and the generator set planning decision optimization model meets the following formula:

9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any one of claims 1 to 5 when the computer program is executed.

10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 5.