CN113988714B - Multi-uncertainty-based dynamic planning method, equipment and medium for park comprehensive energy system - Google Patents
Multi-uncertainty-based dynamic planning method, equipment and medium for park comprehensive energy system Download PDFInfo
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Abstract
The invention discloses a dynamic planning method, equipment and medium for a park comprehensive energy system, which are used for considering multiple uncertainties, and the method comprises the following steps: determining an energy hub frame of a park comprehensive energy system, and determining power balance constraint of each energy network and equipment condition constraint of each coupling equipment; establishing a cost model of the whole life cycle for the park comprehensive energy system, wherein the cost model comprises an equipment investment cost part and an operation cost part of the whole life cycle, which are dynamically planned in stages; constructing an optimization model based on the determined constraint condition by taking the minimum cost of the whole life cycle as an objective function; decomposing the optimization model into a main problem and a sub problem by adopting a column constraint algorithm, and converting the sub problem by adopting a large M method and a dual theory; and solving the main-sub problem iterative algorithm to obtain the equipment capacity configuration of each planning stage and the output configuration scheme of each moment of each equipment in the operation stage. The invention has high economic benefit and provides theoretical support for the development of the comprehensive energy system.
Description
Technical Field
The invention relates to the field of planning of comprehensive energy, in particular to a dynamic planning method of a park comprehensive energy system considering multiple uncertainties.
Background
The park comprehensive energy system is a miniature comprehensive energy system which is directly oriented to terminal energy users, is used as one of typical coupling and supply objects of electric/gas/heat/cold multiple energy sources at the user side, comprises important equipment for multi-energy flow energy transfer, conversion and storage, and can remarkably improve the renewable energy consumption capability and the comprehensive energy utilization efficiency. The park comprehensive energy system planning can bring more reasonable network structure and equipment investment, thereby meeting load requirements and ensuring the high-efficiency and stable operation of the system. Therefore, the planning technology for researching the park comprehensive energy system has important significance for improving the economical efficiency of the park comprehensive energy system.
In the planning of a park comprehensive energy system, most planning methods focus on investment of various energy infrastructures, including electric power, heating, refrigeration, natural gas and the like, belong to static planning methods, and have less research on dynamic planning. However, a large number of projects take one investment plan, neglecting the construction timing characteristics, which results in the input energy equipment being in an idle state at the early stage of operation, and the phenomena of equipment aging and capacity shortage occurring at the late stage of operation.
Disclosure of Invention
The invention provides a dynamic planning method for a park comprehensive energy system, which aims to improve the economical efficiency of the park comprehensive energy system and has a guiding effect on actual engineering development.
In order to achieve the technical purpose, the invention adopts the following technical scheme:
a dynamic planning method for a park comprehensive energy system considering multiple uncertainties comprises the following steps:
Step S1, determining an energy hub frame of a park comprehensive energy system, and determining power balance constraint of each energy network and equipment condition constraint of each coupling equipment;
Step S2, establishing a cost model of a whole life cycle for the park comprehensive energy system, wherein the cost model of the whole life cycle comprises an equipment investment cost part and an operation cost part of the whole life cycle, which are dynamically planned in stages;
S3, constructing an optimization model based on power balance constraint of each energy network and equipment condition constraint of each coupling equipment by taking the minimum cost of the whole life cycle as an objective function;
S4, decomposing the optimization model into a main problem and a sub problem by adopting a column constraint algorithm, and converting the sub problem by adopting a large M method and a dual theory;
And S5, solving the main-sub problem iterative algorithm to obtain the equipment capacity configuration of each planning stage and the output configuration scheme of each moment of each equipment in the operation stage.
In a more preferable scheme, when the equipment investment cost is dynamically planned in stages, the method for dividing each planning stage is as follows: and carrying out collaborative planning according to the load demand and the load growth rate, wherein the faster the load growth is, the shorter the corresponding planning stage is.
In a more preferred approach, the expression for the objective function with the minimum cost of the full life cycle is:
Wherein C is the total cost of the full life cycle; n represents that the whole life cycle is divided into N planning stages, and j is the number of each planning stage; n represents the total life cycle including n years, k being the number therein per year;
investment costs for the j-th planning stage,/> The residual value cost of equipment is input for the j-th planning stage; τ j is the number of years from the time of the occurrence of the fee to the beginning of the jth planning phase; r is the discount rate; /(I)And/>The calculation formula of (2) is as follows:
Wherein c i is the investment coefficient of the newly added equipment i, For the configuration capacity of the newly added device i in the planning phase j, δ i is the net residue rate of the newly added device i, and N i is the service life of the newly added device i;
and/> The energy purchasing cost, the maintenance cost and the treatment cost of the k-th year of the whole life cycle are running cost and are calculated by the following formulas:
h represents different seasons, N h represents the number of seasons of a year, σ h represents the number of days of duration of the season h, t represents different times of day, N T represents the maximum time of day, For the output of the corresponding equipment at different moments, alpha i represents the purchase energy coefficient of the equipment, beta i represents the maintenance coefficient of the equipment, gamma i represents the carbon emission coefficient of the equipment,/>For the carbon emission treatment coefficient, the set omega buy comprises electricity purchase quantity and gas purchase quantity, the set omega all refers to all equipment in the park comprehensive energy system, and the set omega env comprises two equipment of a gas boiler and a gas turbine.
In a more preferable scheme, the equipment condition constraint of the coupling equipment means that the running output of the coupling equipment meets the upper and lower limit constraint of the equipment capacity; the power balance constraint of the energy network is specifically expressed as:
Wherein, the formulas (1) - (4) respectively represent the sub-network power balance constraint of the power grid, the air grid, the heat supply network and the cold network; and/> Respectively representing the output power of a gas turbine, an upper-level power grid, a fan and photovoltaic in the power grid; the power requirements of an electric load, a storage battery, an electric refrigerator and an electric boiler in the power grid are respectively represented; Representing the output quantity of the fuel gas in the gas network; /(I) And/>Respectively representing the gas requirements of the gas turbine and the gas boiler; and/> Respectively representing input quantities of an electric boiler, a gas turbine and a gas boiler in a heat supply network; /(I)And/>Respectively representing the heat requirements of the absorption chiller and the user; /(I)Respectively representing the output power of an absorption refrigerator and an electric refrigerator in a cold net; /(I)For the user's cold load demand in the cold network,/>Indicating the efficiency of the heat exchanger;
in a more preferable scheme, the optimization model is decomposed to obtain a main problem of a 'min' structure and a sub problem of a 'max-min' structure, specifically:
The original optimization model is reduced to the following form:
s.t.Dy＝d
Fx+Gy≥h
The first-stage objective function is equipment planning cost, and the second-stage objective function is equipment running cost; c and f are constant coefficient matrixes corresponding to the objective functions; D. f, G is a coefficient matrix corresponding to the variable under constraint; d. h is a constant column vector; x and y are optimization variables, and are decision variables of a planning stage and decision variables of an operation stage respectively, namely equipment capacity configuration corresponding to the planning stage and equipment output configuration corresponding to the operation stage; Predicted values representing renewable energy output and load;
The main problems of the 'min' structure are:
s.t.ζ≥fTyl
Dyl＝d
Fx+Gyl≥h
Wherein x is the capacity decision quantity of the planning equipment, ζ is an introduced auxiliary variable, and y l represents the real-time adjustment decision added into the main problem in the first iteration; representing the worst scene added to the main problem in the first iteration;
Obtaining a capacity decision quantity x of planning equipment by solving a main problem, adding a group of variables x l and C & CG (control center) cut after each iteration, and providing a group of integer solutions for the next solved sub-problem;
the sub-problems of the "max-min" structure are:
s.t.Gy≥h-Fxl→λ
Dy＝d→ψ
Iuy＝u→υ
Wherein x l is the planning equipment capacity decision variable found in the known master problem; the decision y l and the worst output scene u are adjusted in real time by solving the sub-problem, namely, the equipment output configuration and the worst output scene in the operation stage are adjusted in real time; lambda, psi and v are dual variables.
In a more preferable scheme, the transforming the sub-problem by using a large M method and a dual theory is specifically as follows:
The sub-problems obtained by decomposing the original optimization model are converted into the following dual problems through the dual theory:
s.t.GTλ+DTψ+Iuυ≤f
λ≥0
And then linearizing bilinear terms in the dual problem by adopting a large M method:
s.t.GTλ+DTμ+Iuυ≤f，λ≥0
0≤π+≤Mξ+
υ-M(1-ξ+)≤π+≤υ
0≤π-≤Mξ-
υ-M(1-ξ-)≤π-≤υ
wherein pi - and pi + are introduced auxiliary variables; m is a large enough positive real number, and Γ g is the budget value of uncertain parameters such as wind power, photovoltaic, load and the like.
Further, the iterative solving step is as follows:
Let l=1, given the iteration lower and upper limits: LB, UB and convergence criterion ε;
Solving a main problem in a desired scene, wherein the constraint of the main problem does not contain C & CG cuts at the moment, and obtaining a variable solution x *;
Based on the integer solution x * transmitted by the main problem, solving the sub-problem and obtaining a group of worst fluctuation scenes At this time, the optimal objective function value of the sub-problem is f sp, and the updated iteration upper limit value is: ub=min (UB, f sp);
let l=l+1, add a set of variables to the master question, and return a set of C & CG cutsets as:
the above represents the worst scene that the current sub-problem is solving Adding the constraint to the main problem to obtain a variable solution y *、ζ*, and updating the iteration lower limit value as follows: lb=min (LB, ζ *);
repeating the iteration until the absolute value of UB-LB/LB is less than or equal to epsilon, converging the model, and obtaining the equipment capacity configuration scheme of each planning stage and the output configuration scheme of each moment of each equipment in the operation stage, namely And P i,t,h.
An electronic device comprising a memory and a processor, wherein the memory stores a computer program which, when executed by the processor, causes the processor to implement the method for dynamically planning a campus integrated energy system with multiple uncertainties according to any one of the above schemes.
A computer readable storage medium having stored thereon a computer program which, when executed by a processor, accounts for multiple uncertainty of a method of dynamic planning of a campus integrated energy system according to any one of the above aspects.
Compared with the prior art, the invention has the following beneficial effects:
According to the method, a dynamic planning strategy is introduced according to the structural characteristics of the comprehensive energy of the park, namely, the capacity of the comprehensive energy system is configured in stages along with the deep construction of the park, so that the redundant configuration and advanced construction in the early operation stage can be avoided, and the capacity shortage and equipment aging caused by insufficient funds in the later operation stage can also be avoided. Compared with the existing method, the method has lower total cost and higher economy, and can provide a new idea for planning and constructing comprehensive energy.
Drawings
FIG. 1 is a schematic diagram of an energy hub framework of a campus integrated energy system according to an embodiment of the present application;
Figure 2 is a flow chart of a method for dynamic planning of a campus integrated energy system according to an embodiment of the present application.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
A dynamic planning method for a park comprehensive energy system considering multiple uncertainties is shown in figure 1, and comprises the following steps:
Step S1, determining an energy hub frame of a park comprehensive energy system, and determining power balance constraint of each energy network and equipment condition constraint of each coupling equipment.
As shown in fig. 2, the energy hub frame of the park comprehensive energy system is:
Formulas (1) - (4) respectively represent sub-network power balance constraints of a power grid, an air network, a heat supply network and a cold network; and/> Respectively representing the output power of a gas turbine, an upper-level power grid, a fan and photovoltaic in the power grid; the power requirements of an electric load, a storage battery, an electric refrigerator and an electric boiler in the power grid are respectively represented; Representing the output quantity of the fuel gas in the gas network; /(I) And/>Respectively representing the gas requirements of the gas turbine and the gas boiler; and/> Respectively representing input quantities of an electric boiler, a gas turbine and a gas boiler in a heat supply network; /(I)And/>Respectively representing the heat requirements of the absorption chiller and the user; /(I)Respectively representing the output power of an absorption refrigerator and an electric refrigerator in a cold net; /(I)For the user's cold load demand in the cold network,/>Indicating the efficiency of the heat exchanger.
In addition, the device condition constraint of the coupling device refers to the upper and lower limit constraints that the operating output of the coupling device is required to meet the device capacity.
And S2, establishing a cost model of the whole life cycle for the park comprehensive energy system, wherein the cost model of the whole life cycle comprises an equipment investment cost part and an operation cost part of the whole life cycle which are dynamically planned in stages.
In step S2, a dynamic planning method is introduced to closely contact the development progress of the park and synchronously carry out engineering construction of the park. The division of the planning stages in the dynamic planning method is based on load demands, and the collaborative planning is carried out in cooperation with the increasing rate of the load, and the faster the load increases, the shorter the corresponding planning stages are, otherwise, the opposite is. The number of divided stages is denoted by N, S j denotes the jth planning stage, and C planj denotes the coupling device configuration capacity of the jth planning stage. The method comprises the following steps:
First, market information such as device parameters and energy prices is input. Next, at the beginning of S 1, i.e., the beginning of the planning period, planning configuration C plan1, determining the capacity of each type of device therein to meet the load demand of S 1; further, at early S 2, the capacity of each type of device in C plan2 is planned and configured on the basis of C plan1 to determine the capacity of each type of device in C plan2 to meet the energy supply requirement of S 2 under load increase, and so on, at early S j, the capacity of each type of device in C planj is planned and configured on the basis of the configured device set C plan1∪Cplan2∪Cplan(j-1) to meet the load requirement of S j until the last planning stage S N, and C planN is planned and configured.
After the multi-stage dynamic planning method is introduced, the cost model of the park comprehensive energy system is as follows:
Where C is the total cost of the full life cycle: the first term represents the total investment cost of the plant, the second term represents the residual cost of the plant, and the third term is the total operating cost of the plant. N represents that the whole life cycle is divided into N planning stages, and j is the number of each planning stage; n represents the total life cycle including n years, k being the number therein per year;
Of the total investment costs of the first equipment, Investment costs for the j-th planning stage. At the end of the planning period, the life of a part of the equipment is not yet ended, and the existence of equipment residues can offset part of the cost. Thus, in the second equipment residue cost, use/>Representing the residual value cost of equipment input in the j-th planning stage; τ j is the number of years from the time of the occurrence of the fee to the beginning of the jth planning phase; r is the discount rate. Wherein/>And/>The calculation formula of (2) is as follows:
Wherein c i is the investment coefficient of the newly added equipment i, For the configuration capacity of the newly added device i in the planning phase j, δ i is the net residue rate of the newly added device i, and N i is the service life of the newly added device i;
In the third equipment total operating cost, And/>The energy purchasing cost, the maintenance cost and the treatment cost of the k-th year of the whole life cycle are running cost and are calculated by the following formulas:
h represents different seasons, N h represents the number of seasons of a year, σ h represents the number of days of duration of the season h, t represents different times of day, N T represents the maximum time of day, For the output of the corresponding equipment at different moments, alpha i represents the purchase energy coefficient of the equipment, beta i represents the maintenance coefficient of the equipment, gamma i represents the carbon emission coefficient of the equipment,/>For the carbon emission treatment coefficient, the set omega buy comprises electricity purchase quantity and gas purchase quantity, the set omega all refers to all equipment in the park comprehensive energy system, and the set omega env comprises two equipment of a gas boiler and a gas turbine.
And S3, constructing an optimization model based on the power balance constraint of each energy network and the equipment condition constraint of each coupling equipment by taking the minimum cost of the whole life cycle as an objective function.
And S4, decomposing the optimization model into a main problem and a sub problem by adopting a column constraint algorithm, and converting the sub problem by adopting a large M method and a dual theory.
The original optimization model constructed in the step S3 is simplified and expressed as:
The first-stage objective function is equipment planning cost, and the second-stage objective function is equipment running cost. c and f are constant coefficient matrixes corresponding to the objective functions; D. f, G is a coefficient matrix corresponding to the variable under constraint; d. h is a constant column vector; x and y are optimization variables, and are decision variables of a planning stage and decision variables of an operation stage respectively, namely equipment capacity configuration corresponding to the planning stage and equipment output configuration corresponding to the operation stage; representing predicted values of renewable energy output and load.
The objective function of equation (11) is a simplified version of equation (5), where the constraint of equation (11) is: the first constraint condition represents an equality constraint in the model, including a subnet power balance constraint of formula (1) -formula (4) and an input-output constraint of each coupling device; the second constraint condition represents inequality constraint in the model, mainly upper and lower limit constraint of each coupling device, and the running output of each coupling device is required to meet the upper and lower limit constraint of the device capacity; the third constraint indicates that the renewable energy output and load in the model remain consistent with the predicted values, indicating that the model at this time is a deterministic model.
After the original optimization model is decomposed by adopting a column constraint algorithm, a main problem and a sub problem are obtained, wherein the main problem is as follows:
Wherein ζ is an introduced auxiliary variable, y l represents the real-time adjustment decision added to the main problem in the first iteration; Representing the worst scenario added to the main problem in the first iteration. And obtaining the capacity decision quantity x of the planning equipment by solving the main problem. The main problem is to comprehensively consider the MILP problem of CCHP investment stage and operation stage with the minimum total cost as an objective function, newly add a set of variables x l and C & CG cut after each iteration, and provide a set of integer solutions for the next solving sub-problem.
The sub-problems are:
Wherein x l is the planning equipment capacity decision variable found in the known master problem; the decision y l and the worst output scene u are adjusted in real time by solving the sub-problem, namely, the equipment output configuration and the worst output scene in the operation stage are adjusted in real time; lambda, psi and v are dual variables.
For the above sub-problems, the original model can be converted by using a large M method and a dual theory, specifically:
By the dual theory, the sub-problem of the original model is converted into the following dual problem:
And (3) linearizing bilinear terms in the formula by adopting a large M method:
Wherein pi - and pi + are introduced auxiliary variables; m is a large enough positive real number, and Γ u is the budget value of uncertain parameters such as wind power, photovoltaic, load and the like.
And S5, solving the main-sub problem iterative algorithm to obtain the equipment capacity configuration of each planning stage and the output scheme of each period of the equipment.
And (3) solving the main problem and the converted sub-problem obtained by decomposing in the step (S4) by adopting an iterative algorithm to obtain equipment capacity configuration of each planning stage and an output scheme of each period of equipment, wherein the method comprises the following specific steps:
(1) Let l=1, given the iteration lower and upper limits: LB, UB and convergence criterion ε;
(2) Solving a main problem in a desired scene, wherein the constraint of the main problem does not contain C & CG cuts at the moment, and obtaining a variable solution x *;
(3) Based on the integer solution x * transmitted by the main problem, solving (15), and obtaining a group of worst fluctuation scenes At this time, the optimal objective function value of the sub-problem is f sp, and the updated iteration upper limit value is: ub=min (UB, f sp);
(4) Let l=l+1, add a set of variables to the master question, and return a set of C & CG cutsets as:
the above represents the worst scene that the current sub-problem is solving Adding the constraint to the main problem to obtain a variable solution y *、ζ*, and updating the iteration lower limit value as follows: lb=min (LB, ζ *);
(5) Repeating the iteration until the absolute value of UB-LB/LB is less than or equal to epsilon, converging the model, and obtaining a device capacity configuration scheme of each planning stage and a force configuration scheme y * of each device at each moment in the operation stage, namely And P i,t,h.
The above embodiments are preferred embodiments of the present application, and various changes or modifications may be made thereto by those skilled in the art, which should be construed as falling within the scope of the present application as claimed herein, without departing from the general inventive concept.
Claims (4)
1. A dynamic planning method for a park comprehensive energy system considering multiple uncertainties is characterized by comprising the following steps:
Step S1, determining an energy hub frame of a park comprehensive energy system, and determining power balance constraint of each energy network and equipment condition constraint of each coupling equipment;
Step S2, establishing a cost model of a whole life cycle for the park comprehensive energy system, wherein the cost model of the whole life cycle comprises an equipment investment cost part and an operation cost part of the whole life cycle, which are dynamically planned in stages;
S3, constructing an optimization model based on power balance constraint of each energy network and equipment condition constraint of each coupling equipment by taking the minimum cost of the whole life cycle as an objective function;
The expression for the objective function with the minimum cost of the full life cycle is:
Wherein C is the total cost of the full life cycle; n represents that the whole life cycle is divided into N planning stages, and j is the number of each planning stage; n represents the total life cycle including n years, k being the number therein per year;
investment costs for the j-th planning stage,/> The residual value cost of equipment is input for the j-th planning stage; τ j is the number of years from the time of the occurrence of the fee to the beginning of the jth planning phase; r is the discount rate; /(I)And/>The calculation formula of (2) is as follows:
Wherein c i is the investment coefficient of the newly added equipment i, For the configuration capacity of the newly added device i in the planning phase j, δ i is the net residue rate of the newly added device i, and N i is the service life of the newly added device i;
and/> The energy purchasing cost, the maintenance cost and the treatment cost of the k-th year of the whole life cycle are running cost and are calculated by the following formulas:
h represents different seasons, N h represents the number of seasons of a year, σ h represents the number of days of duration of the season h, t represents different times of day, N T represents the maximum time of day, For the output of the corresponding equipment at different moments, alpha i represents the purchase energy coefficient of the equipment, beta i represents the maintenance coefficient of the equipment, gamma i represents the carbon emission coefficient of the equipment,/>For the treatment coefficient of carbon emission, the set omega buy comprises electricity purchase quantity and gas purchase quantity, the set omega all refers to all equipment in the park comprehensive energy system, and the set omega env comprises two equipment of a gas boiler and a gas turbine;
The equipment condition constraint of the coupling equipment means that the running output of the coupling equipment meets the upper and lower limit constraint of the equipment capacity; the power balance constraint of the energy network is specifically expressed as:
Wherein, the formulas (1) - (4) respectively represent the sub-network power balance constraint of the power grid, the air grid, the heat supply network and the cold network; and/> Respectively representing the output power of a gas turbine, an upper-level power grid, a fan and photovoltaic in the power grid; /(I) The power requirements of an electric load, a storage battery, an electric refrigerator and an electric boiler in the power grid are respectively represented; /(I)Representing the output quantity of the fuel gas in the gas network; /(I)And/>Respectively representing the gas requirements of the gas turbine and the gas boiler; /(I)And/>Respectively representing input quantities of an electric boiler, a gas turbine and a gas boiler in a heat supply network; /(I)And/>Respectively representing the heat requirements of the absorption chiller and the user; /(I)Respectively representing the output power of an absorption refrigerator and an electric refrigerator in a cold net; /(I)For the user's cold load demand in the cold network,/>Indicating the efficiency of the heat exchanger;
S4, decomposing the optimization model into a main problem and a sub problem by adopting a column constraint algorithm, and converting the sub problem by adopting a large M method and a dual theory;
The optimization model is decomposed to obtain a main problem of a 'min' structure and a sub problem of a 'max-min' structure, and the main problem and the sub problem are specifically as follows:
The original optimization model is reduced to the following form:
s.t.Dy＝d
Fx+Gy≥h
The first-stage objective function is equipment planning cost, and the second-stage objective function is equipment running cost; c and f are constant coefficient matrixes corresponding to the objective functions; D. f, G is a coefficient matrix corresponding to the variable under constraint; d. h is a constant column vector; x and y are optimization variables, and are decision variables of a planning stage and decision variables of an operation stage respectively, namely equipment capacity configuration corresponding to the planning stage and equipment output configuration corresponding to the operation stage; Predicted values representing renewable energy output and load;
The main problems of the 'min' structure are:
s.t.ζ≥fTyl
Dyl＝d
Fx+Gyl≥h
Wherein x is the capacity decision quantity of the planning equipment, ζ is an introduced auxiliary variable, and y l represents the real-time adjustment decision added into the main problem in the first iteration; representing the worst scene added to the main problem in the first iteration;
Obtaining a capacity decision quantity x of planning equipment by solving a main problem, adding a group of variables x l and C & CG (control center) cut after each iteration, and providing a group of integer solutions for the next solved sub-problem;
the sub-problems of the "max-min" structure are:
s.t.Gy≥h-Fxl→λ
Dy＝d→ψ
Iuy＝u→υ
Wherein x l is the planning equipment capacity decision variable found in the known master problem; the decision y l and the worst output scene u are adjusted in real time by solving the sub-problem, namely, the equipment output configuration and the worst output scene in the operation stage are adjusted in real time; lambda, psi and v are dual variables;
the sub-problem is converted by adopting a large M method and a dual theory, and the method specifically comprises the following steps:
The sub-problems obtained by decomposing the original optimization model are converted into the following dual problems through the dual theory:
s.t.GTλ+DTψ+Iuυ≤f
λ≥0
And then linearizing bilinear terms in the dual problem by adopting a large M method:
s.t.GTλ+DTμ+Iuυ≤f，λ≥0
0≤π+≤Mξ+
υ-M(1-ξ+)≤π+≤υ
0≤π-≤Mξ-
υ-M(1-ξ-)≤π-≤υ
Wherein pi - and pi + are introduced auxiliary variables; m is a large enough positive real number, and Γ g is the budget value of uncertain parameters such as wind power, photovoltaic, load and the like;
Step S5, solving a main-sub problem iterative algorithm to obtain equipment capacity configuration of each planning stage and an output configuration scheme of each moment of each equipment in an operation stage; the iterative solving step is as follows:
Let l=1, given the iteration lower and upper limits: LB, UB and convergence criterion ε;
Solving a main problem in a desired scene, wherein the constraint of the main problem does not contain C & CG cuts at the moment, and obtaining a variable solution x *;
Based on the integer solution x * transmitted by the main problem, solving the sub-problem and obtaining a group of worst fluctuation scenes At this time, the optimal objective function value of the sub-problem is f sp, and the updated iteration upper limit value is: ub=min (UB, f sp);
let l=l+1, add a set of variables to the master question, and return a set of C & CG cutsets as:
the above represents the worst scene that the current sub-problem is solving Adding the constraint to the main problem to obtain a variable solution y *、ζ*, and updating the iteration lower limit value as follows: lb=min (LB, ζ *);
Repeating the iteration until the absolute value of UB-LB/LB is less than or equal to epsilon, converging the model, and obtaining a device capacity configuration scheme x * of each planning stage and a force output configuration scheme y * of each device at each moment in the operation stage, namely And P i,t,h.
2. The method according to claim 1, wherein in the step of dynamically planning the equipment investment cost in stages, the method of dividing each planning stage is as follows: and carrying out collaborative planning according to the load demand and the load growth rate, wherein the faster the load growth is, the shorter the corresponding planning stage is.
3. An electronic device comprising a memory and a processor, the memory having stored therein a computer program which, when executed by the processor, causes the processor to implement the method of any of claims 1-2.
4. A computer readable storage medium, on which a computer program is stored, which computer program, when being executed by a processor, implements the method according to any of claims 1-2.
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