CN113988714A - Dynamic planning method, device and medium for park integrated energy system considering multiple uncertainties - Google Patents

Dynamic planning method, device and medium for park integrated energy system considering multiple uncertainties Download PDF

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CN113988714A
CN113988714A CN202111421720.1A CN202111421720A CN113988714A CN 113988714 A CN113988714 A CN 113988714A CN 202111421720 A CN202111421720 A CN 202111421720A CN 113988714 A CN113988714 A CN 113988714A
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许加柱
伍敏
曾林俊
李芸
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Abstract

The invention discloses a dynamic planning method, equipment and medium for a park comprehensive energy system considering multiple uncertainties, wherein the method comprises the following steps: determining an energy hub frame of the park comprehensive energy system, and determining power balance constraints of each energy network and equipment condition constraints of each coupling equipment; establishing a cost model of a full life cycle for the park comprehensive energy system, wherein the cost model comprises a device investment cost part and a running cost part of the full 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 life cycle as an objective function; decomposing the optimization model into a main problem and a sub problem by adopting a column and constraint algorithm, and converting the sub problem by adopting a large M method and a dual theory; and solving through a main-sub problem iterative algorithm to obtain the equipment capacity configuration of each planning stage and the output configuration scheme of each equipment moment in the operation stage. The invention has high economic benefit and provides theoretical support for the development of a comprehensive energy system.

Description

Dynamic planning method, device and medium for park integrated energy system considering multiple uncertainties
Technical Field
The invention relates to the field of comprehensive energy planning, in particular to a dynamic planning method for a park comprehensive energy system considering multiple uncertainties.
Background
The park integrated energy system is a miniature integrated energy system directly facing terminal energy users, is one of typical coupling and supply objects of electricity, gas, heat and cold multiple energy sources at a user side, comprises important equipment for transferring, converting and storing the multiple energy streams, and can obviously improve the consumption capacity of renewable energy sources and the utilization efficiency of the integrated energy sources. The planning of the comprehensive energy system of the park can bring more reasonable network structure and equipment investment, thereby meeting the load requirement and ensuring the high-efficiency stable operation of the system. Therefore, the research on the planning technology of the park comprehensive energy system has important significance for improving the economy of the park comprehensive energy system.
In the planning problem of the comprehensive energy system in the park, most planning methods focus on infrastructure which invests multiple energy sources at one time, including electric power, heating, refrigeration, natural gas and the like, and belong to static planning methods, wherein the research on dynamic planning is less. However, a lot of projects adopt an investment plan, neglecting the construction time sequence characteristics, which results in the phenomenon that the input energy equipment is in an idle state in the initial operation period and the phenomena of equipment aging and capacity shortage occur in the later operation period.
Disclosure of Invention
The dynamic planning method for the park comprehensive energy system, which is provided by the invention, can be used for considering multiple uncertainties, can be used for improving the economy of the park comprehensive energy system and has a guiding function 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 integrated energy system considering multiple uncertainties comprises the following steps:
step S1, determining an energy hub frame of the park comprehensive energy system, and determining power balance constraints of each energy network and equipment condition constraints of each coupling equipment;
step S2, establishing a life cycle cost model for the park integrated energy system, wherein the life cycle cost model comprises a device investment cost part and a life cycle operation cost part which are dynamically planned in stages;
step 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 life cycle as an objective function;
step S4, decomposing the optimization model into a main problem and a sub-problem by adopting a column and constraint algorithm, and converting the sub-problem by adopting a large M method and a dual theory;
and step S5, solving and obtaining the equipment capacity configuration of each planning stage and the output configuration scheme of each equipment moment in the operation stage through a main-sub problem iterative algorithm.
In a more preferred scheme, when the equipment investment cost is dynamically planned in stages, the method for dividing each planning stage comprises the following steps: and performing collaborative planning by matching with the increase rate of the load according to the load demand, wherein the faster the load increases, the shorter the corresponding planning stage is.
In a more preferred embodiment, the expression with the minimum cost over life cycle as the objective function is:
Figure BDA0003376805310000021
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 is the number of each year therein;
Figure BDA0003376805310000022
for the investment cost of the jth planning stage,
Figure BDA0003376805310000023
residual cost of equipment invested for the jth planning stage; tau isjThe time of expense is the years from the beginning of the jth planning stage; r is the discount rate;
Figure BDA0003376805310000024
and
Figure BDA0003376805310000025
the calculation formula of (a) is respectively:
Figure BDA0003376805310000026
Figure BDA0003376805310000027
in the formula, ciIs the investment coefficient of the newly added equipment i,
Figure BDA0003376805310000028
for planning the configured capacity, delta, of the newly added device i in phase jiNet residual value rate, N, for newly added device iiThe service life of the newly added equipment i is prolonged;
Figure BDA0003376805310000029
and
Figure BDA00033768053100000210
the energy purchase cost, the maintenance cost and the treatment cost of the k year of the whole life cycle are respectively operation costs, and are respectively calculated by the following formulas:
Figure BDA00033768053100000211
Figure BDA00033768053100000212
Figure BDA00033768053100000213
h represents different seasons, NhRepresents the number of seasons of a year, sigmahIn season hDuration of days, t denotes different time, NTWhich represents the maximum time of day of the day,
Figure BDA00033768053100000214
to correspond to the output of the device at different times, alphaiRepresenting the energy purchase coefficient, beta, of the plantiRepresenting maintenance factor, gamma, of the equipmentiRepresents the carbon emission coefficient of the plant,
Figure BDA00033768053100000215
for the governing coefficient of carbon emission, set omegabuyComprises electricity and gas purchasing quantities, and set omegaallAll equipment in the comprehensive energy system of the finger park, set omegaenvComprises two devices of a gas boiler and a gas turbine.
In a more preferred scheme, the equipment condition constraint of the coupling equipment means that the running output of the coupling equipment meets the upper and lower limit constraints of the equipment capacity; the power balance constraint of the energy network is specifically expressed as:
Figure BDA0003376805310000031
Figure BDA0003376805310000032
Figure BDA0003376805310000033
Figure BDA0003376805310000034
wherein, the sub-network power balance constraints of the power grid, the air grid, the heat supply network and the cold supply network are respectively expressed by the formulas (1) to (4);
Figure BDA0003376805310000035
Figure BDA0003376805310000036
and
Figure BDA0003376805310000037
respectively representing the output power of a gas turbine, a superior power grid, a fan and a photovoltaic in the power grid;
Figure BDA0003376805310000038
Figure BDA0003376805310000039
respectively representing the electricity load in the power grid, the storage battery, the electric refrigerator and the electricity demand of the electric boiler;
Figure BDA00033768053100000310
representing the output of gas in the gas network;
Figure BDA00033768053100000311
and
Figure BDA00033768053100000312
respectively representing the gas requirements of a gas turbine and a gas boiler;
Figure BDA00033768053100000313
and
Figure BDA00033768053100000314
respectively representing the input quantities of an electric power boiler, a gas turbine and a gas boiler in a heat supply network;
Figure BDA00033768053100000315
and
Figure BDA00033768053100000316
representing the heat requirements of the absorption chiller and the user, respectively;
Figure BDA00033768053100000317
respectively representing the output power of the absorption refrigerator and the electric refrigerator in the cold network;
Figure BDA00033768053100000318
for the cooling load demand of the users in the cooling network,
Figure BDA00033768053100000319
represents the efficiency of the heat exchanger;
in a more preferred scheme, 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 method specifically comprises the following steps:
the original optimization model is simplified to the following form:
Figure BDA00033768053100000320
s.t.Dy＝d
Fx+Gy≥h
Figure BDA00033768053100000321
the first stage objective function is equipment planning cost, and the second stage objective function is equipment operation cost; c and f are constant coefficient matrixes corresponding to the objective function; D. f, G is a coefficient matrix corresponding to the variables under the constraint; d. h is a constant column vector; x and y are optimization variables, namely decision variables of a planning stage and decision variables of an operation stage, namely equipment capacity configuration corresponding to the planning stage and equipment output configuration corresponding to the operation stage;
Figure BDA00033768053100000322
expressing the predicted values of the output and the load of the renewable energy sources;
the main problems of the "min" structure are:
Figure BDA00033768053100000323
s.t.ζ≥fTyl
Dyl＝d
Fx+Gyl≥h
Figure BDA00033768053100000324
where x is the capacity decision quantity of the planning equipment, ζ is the introduced auxiliary variable, ylRepresenting a real-time adjustment decision added to the main problem in the first iteration;
Figure BDA00033768053100000325
representing the worst scenario added to the main problem in the first iteration;
obtaining planning equipment capacity decision quantity x by solving a main problem, and adding a group of variables x after each iterationlAnd C&CG cutting and providing a group of integer solutions for next solving subproblems;
the sub-problem of the "max-min" structure is:
Figure BDA0003376805310000041
s.t.Gy≥h-Fxl→λ
Dy＝d→ψ
Iuy＝u→υ
wherein x islA decision variable for planning equipment capacity found in a known main problem; adjusting decisions y in real time by solving sub-problemslAnd a worst output scene u, namely the equipment output configuration and the worst output scene in the real-time adjustment operation stage; lambda, psi and upsilon are dual variables.
In a more preferred embodiment, the sub-problem is converted by using a large M method and a dual theory, specifically:
converting the subproblems obtained by decomposing the original optimization model into the following dual problems by a dual theory:
Figure BDA0003376805310000042
s.t.GTλ+DTψ+Iuυ≤f
λ≥0
then, a large M method is adopted to carry out linearization processing on the bilinear terms in the dual problem:
Figure BDA0003376805310000043
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
Figure BDA0003376805310000044
wherein, pi-And pi+Is an introduced auxiliary variable; m is a sufficiently large positive real number, ΓgThe method is a precalculated value of uncertain parameters such as wind power, photovoltaic and load.
Further, the iterative solution step is as follows:
let l be 1, give iteration lower and upper limits: LB, UB and convergence criterion epsilon;
solving the main problem under the expected scene, wherein the constraint of the main problem does not contain C&CG cutting to obtain a variable solution x*；
Integer solution x delivered based on main problem*Solving the subproblems and obtaining a set of worst fluctuation scenarios
Figure BDA0003376805310000045
The optimal objective function value of the sub-problem at this time is fspThe update iteration upper limit value is: UB min (UB, f)sp)；
Let l be l +1, add a set of variables to the main question and return a set of C & CG cuts such as:
Figure BDA0003376805310000051
the above equation represents the worst scenario for the current sub-problem
Figure BDA0003376805310000052
Adding the constraint form to the main problem to obtain a variable solution y*、ζ*And updating an iteration lower limit value as follows: LB ═ min (LB, ζ)*)；
Repeating iteration until UB-LB/LB is less than or equal to epsilon, converging the model, and obtaining the equipment capacity allocation scheme of each planning stage and the output allocation scheme of each equipment moment in the operation stage, namely
Figure BDA0003376805310000053
And Pi,t,h。
An electronic device comprising a memory and a processor, wherein the memory stores a computer program, and the computer program, when executed by the processor, causes the processor to implement the dynamic park energy system planning method taking into account multiple uncertainties according to any of the above aspects.
A computer readable storage medium having stored thereon a computer program which, when executed by a processor, causes a method of dynamic planning of an integrated energy system for a campus that accounts for multiple uncertainties according to any of the above aspects.
Compared with the prior art, the invention has the following beneficial effects:
according to the characteristics of the structure of the comprehensive energy of the park, the dynamic planning strategy is introduced, 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 at the initial stage of operation can be avoided, and the capacity shortage and equipment aging caused by insufficient funds at the later stage of operation can be avoided. Compared with the existing method, the method has lower total cost and higher economical efficiency, and can provide a new idea for the planning and construction of comprehensive energy.
Drawings
FIG. 1 is a schematic diagram of an energy hub framework of a campus energy complex according to an embodiment of the present application;
fig. 2 is a flowchart of a dynamic planning method for a campus integrated energy system according to an embodiment of the present application.
Detailed Description
To make the objects, technical solutions and advantages of the embodiments of the present invention clearer, 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, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
A dynamic planning method for a park integrated energy system taking into account multiple uncertainties, as shown in fig. 1, includes the following steps:
and step S1, determining an energy hub frame of the park comprehensive energy system, and determining power balance constraints of each energy network and equipment condition constraints of each coupling equipment.
As shown in fig. 2, the energy hub frame of the park integrated energy system is:
Figure BDA0003376805310000061
Figure BDA0003376805310000062
Figure BDA0003376805310000063
Figure BDA0003376805310000064
formulas (1) to (4) represent the sub-network power balance constraints of the power grid, the air grid, the heat grid and the cold grid respectively;
Figure BDA0003376805310000065
and
Figure BDA0003376805310000066
respectively representing the output power of a gas turbine, a superior power grid, a fan and a photovoltaic in the power grid;
Figure BDA0003376805310000067
respectively representing the electricity load in the power grid, the storage battery, the electric refrigerator and the electricity demand of the electric boiler;
Figure BDA0003376805310000068
representing the output of gas in the gas network;
Figure BDA0003376805310000069
and
Figure BDA00033768053100000610
respectively representing the gas requirements of a gas turbine and a gas boiler;
Figure BDA00033768053100000611
and
Figure BDA00033768053100000612
respectively representing the input quantities of an electric power boiler, a gas turbine and a gas boiler in a heat supply network;
Figure BDA00033768053100000613
and
Figure BDA00033768053100000614
representing the heat requirements of the absorption chiller and the user, respectively;
Figure BDA00033768053100000615
respectively representing the output power of the absorption refrigerator and the electric refrigerator in the cold network;
Figure BDA00033768053100000616
for the cooling load demand of the users in the cooling network,
Figure BDA00033768053100000617
indicating the efficiency of the heat exchanger.
In addition, the equipment condition constraint of the coupling equipment means that the operation output of the coupling equipment meets the upper and lower limit constraints of the equipment capacity.
And step S2, establishing a life-cycle cost model for the park integrated energy system, wherein the life-cycle cost model comprises a device investment cost part and a life-cycle operation cost part which are dynamically planned in stages.
In step S2, a dynamic planning method is introduced to closely relate to the development progress of the park and to synchronize with the project construction of the park. The planning stages in the dynamic planning method are divided according to the load demand and are cooperatively planned according to the load growth rate, and the faster the load growth, the shorter the corresponding planning stage, or else, the opposite is true. With N representing the number of stages divided, SjDenotes the jth planning phase, CplanjRepresenting the coupling equipment configuration capacity of the jth planning phase. The method specifically comprises the following steps:
first, market information such as device parameters, energy prices, and the like is input. Secondly, at S1Planning configuration C for the first year, i.e. the first year of the planning periodplan1Determining the capacity of each type of equipment to satisfy S1The load demand of (c); further, at S2In the first year, at Cplan1On the basis, plan configuration Cplan2Determining Cplan2Capacity of medium-sized and various types of equipment to satisfy S under load increase2In analogy to the above, at SjIn the first year, in configured device set Cplan1∪Cplan2∪Cplan(j-1)On the basis of (2), planning configuration CplanjCapacity of various types of equipment to satisfy SjUntil the last planning stage SNPlanning configuration CplanN。
After the multi-stage dynamic planning method is introduced, the cost model of the park comprehensive energy system is as follows:
Figure BDA00033768053100000618
where C is the total cost of the full life cycle: the first item represents the total investment cost of the equipment, the second item represents the residual value cost of the equipment, and the third item represents the total operation cost of the equipment. 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 is the number of each year therein;
in the total investment cost of the first equipment,
Figure BDA0003376805310000071
investment costs for the jth planning stage. At the end of the planning period, the service life of part of the equipment is still not terminated, and the residual value of the equipment can offset part of the cost. Therefore, the second residual cost of the equipment is used
Figure BDA0003376805310000072
Representing the residual value cost of equipment input in the jth planning stage; tau isjThe time of expense is the years from the beginning of the jth planning stage; r is the discount rate. Wherein the content of the first and second substances,
Figure BDA0003376805310000073
and
Figure BDA0003376805310000074
the calculation formula of (a) is respectively:
Figure BDA0003376805310000075
Figure BDA0003376805310000076
in the formula, ciIs the investment coefficient of the newly added equipment i,
Figure BDA0003376805310000077
for planning the configured capacity, delta, of the newly added device i in phase jiNet residual value rate, N, for newly added device iiThe service life of the newly added equipment i is prolonged;
in the total operating cost of the third item of equipment,
Figure BDA0003376805310000078
and
Figure BDA0003376805310000079
the energy purchase cost, the maintenance cost and the treatment cost of the k year of the whole life cycle are respectively operation costs, and are respectively calculated by the following formulas:
Figure BDA00033768053100000710
Figure BDA00033768053100000711
Figure BDA00033768053100000712
h represents different seasons, NhRepresents the number of seasons of a year, sigmahThe number of days of the season h, t representing different times, NTWhich represents the maximum time of day of the day,
Figure BDA00033768053100000713
to correspond to the output of the device at different times, alphaiRepresenting the energy purchase coefficient, beta, of the plantiRepresenting maintenance factor, gamma, of the equipmentiRepresents the carbon emission coefficient of the plant,
Figure BDA00033768053100000714
for the governing coefficient of carbon emission, set omegabuyComprises electricity and gas purchasing quantities, and set omegaallAll equipment in the comprehensive energy system of the finger park, set omegaenvComprises two devices of a gas boiler and a gas turbine.
And step 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 life cycle as an objective function.
And step S4, decomposing the optimization model into a main problem and a sub problem by adopting a column and constraint algorithm, and converting the sub problem by adopting a large M method and a dual theory.
The original optimization model constructed in step S3 is simply expressed as:
Figure BDA0003376805310000081
the first stage objective function is equipment planning cost, and the second stage objective function is equipment operation cost. c and f are constant coefficient matrixes corresponding to the objective function; D. f, G is a coefficient matrix corresponding to the variables under the constraint; d. h is a constant column vector; x and y are optimization variables, namely decision variables of a planning stage and decision variables of an operation stage, namely equipment capacity configuration corresponding to the planning stage and equipment output configuration corresponding to the operation stage;
Figure BDA0003376805310000082
and (4) representing the predicted values of renewable energy output and load.
The objective function of equation (11) is a simplified form of equation (5), with the constraint of equation (11): the first constraint condition represents equality constraint in the model, and comprises subnet power balance constraint of formula (1) to formula (4) and input and output constraint of each coupling device; the second constraint condition represents inequality constraint in the model, mainly upper and lower limit constraints of each coupling device, and the operation output of each coupling device needs to meet the upper and lower limit constraints of the device capacity; and the third constraint condition indicates that the renewable energy output and the load quantity in the model are consistent with the predicted value, and the model at the moment is a deterministic model.
Decomposing the original optimization model by using a column and constraint algorithm to obtain a main problem and a sub-problem, wherein the main problem is as follows:
Figure BDA0003376805310000083
where ζ is the auxiliary variable introduced and ylRepresenting a real-time adjustment decision added to the main problem in the first iteration;
Figure BDA0003376805310000084
representing the worst scenario added to the main problem in the ith iteration. And solving the main problem to obtain the capacity decision quantity x of the planning equipment. The main problem is the MILP problem which comprehensively considers CCHP investment phase and operation phase and takes the minimum total cost as an objective function, and a set of variable x is added after each iterationlAnd C&And CG cutting, and providing a group of integer solutions for the next solved subproblem.
The sub-problems are:
Figure BDA0003376805310000085
wherein x islA decision variable for planning equipment capacity found in a known main problem; adjusting decisions y in real time by solving sub-problemslAnd a worst output scene u, namely the equipment output configuration and the worst output scene in the real-time adjustment operation stage; lambda, psi and upsilon are dual variables.
For the above subproblems, the original model can be converted by using a large M method and a dual theory, specifically:
by the dual theory, the subproblems of the original model translate into the following dual problem:
Figure BDA0003376805310000091
adopting a large M method to carry out linearization processing on bilinear terms in the formula:
Figure BDA0003376805310000092
wherein, pi-And pi+Is an introduced auxiliary variable; m is a sufficiently large positive real number, ΓuThe method is a precalculated value of uncertain parameters such as wind power, photovoltaic and load.
And step S5, solving and obtaining the equipment capacity configuration of each planning stage and the output scheme of each time period of the equipment through a main-sub problem iterative algorithm.
For the main problem and the sub-problem after the transformation obtained by the decomposition in the step S4, an iterative algorithm may be used to solve and obtain the equipment capacity configuration of each planning stage and the output scheme of each time period of the equipment, which specifically includes:
(1) let l be 1, give iteration lower and upper limits: LB, UB and convergence criterion epsilon;
(2) solving the main problem under the expected scene, wherein the constraint of the main problem does not contain C&CG cutting to obtain a variable solution x*；
(3) Integer solution x delivered based on main problem*Solving equation (15) and obtaining a set of worst fluctuation scenarios
Figure BDA0003376805310000093
The optimal objective function value of the sub-problem at this time is fspThe update iteration upper limit value is: UB min (UB, f)sp)；
(4) Let l be l +1, add a set of variables to the main question and return a set of C & CG cuts such as:
Figure BDA0003376805310000094
the above equation represents the worst scenario for the current sub-problem
Figure BDA0003376805310000095
Adding the constraint form to the main problem to obtain a variable solution y*、ζ*And updating an iteration lower limit value as follows: LB ═ min (LB, ζ)*)；
(5) Repeating iteration in this way until | UB-LB |/| LB | < epsilon, converging the model, and obtaining the equipment capacity configuration scheme of each planning stage and the output configuration scheme y of each equipment moment in the operation stage*I.e. by
Figure BDA0003376805310000101
And Pi,t,h。
The above embodiments are preferred embodiments of the present application, and those skilled in the art can make various changes or modifications without departing from the general concept of the present application, and such changes or modifications should fall within the scope of the claims of the present application.

Claims (9)

1. A dynamic planning method for a park integrated energy system considering multiple uncertainties is characterized by comprising the following steps:
step S1, determining an energy hub frame of the park comprehensive energy system, and determining power balance constraints of each energy network and equipment condition constraints of each coupling equipment;
step S2, establishing a life cycle cost model for the park integrated energy system, wherein the life cycle cost model comprises a device investment cost part and a life cycle operation cost part which are dynamically planned in stages;
step 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 life cycle as an objective function;
step S4, decomposing the optimization model into a main problem and a sub-problem by adopting a column and constraint algorithm, and converting the sub-problem by adopting a large M method and a dual theory;
and step S5, solving and obtaining the equipment capacity configuration of each planning stage and the output configuration scheme of each equipment moment in the operation stage through a main-sub problem iterative algorithm.
2. The method according to claim 1, wherein, when dynamically planning the equipment investment cost in stages, the planning stages are divided into: and performing collaborative planning by matching with the increase rate of the load according to the load demand, wherein the faster the load increases, the shorter the corresponding planning stage is.
3. The method of claim 1, wherein the expression for the objective function of minimizing cost over life is:
Figure FDA0003376805300000011
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 is the number of each year therein;
Figure FDA0003376805300000012
for the investment cost of the jth planning stage,
Figure FDA0003376805300000013
residual cost of equipment invested for the jth planning stage; tau isjThe time of expense is the years from the beginning of the jth planning stage; r is the discount rate;
Figure FDA0003376805300000014
and
Figure FDA0003376805300000015
the calculation formula of (a) is respectively:
Figure FDA0003376805300000016
Figure FDA0003376805300000017
in the formula, ciIs the investment coefficient of the newly added equipment i,
Figure FDA0003376805300000018
for planning the configured capacity, delta, of the newly added device i in phase jiNet residual value rate, N, for newly added device iiThe service life of the newly added equipment i is prolonged;
Figure FDA0003376805300000019
and
Figure FDA00033768053000000110
the energy purchase cost, the maintenance cost and the treatment cost of the k year of the whole life cycle are respectively operation costs, and are respectively calculated by the following formulas:
Figure FDA0003376805300000021
Figure FDA0003376805300000022
Figure FDA0003376805300000023
h represents different seasons, NhRepresents the number of seasons of a year, sigmahThe number of days of the season h, t representing different times, NTWhich represents the maximum time of day of the day,
Figure FDA0003376805300000024
for corresponding to is provided withAgainst forces, alpha, at different timesiRepresenting the energy purchase coefficient, beta, of the plantiRepresenting maintenance factor, gamma, of the equipmentiRepresents the carbon emission coefficient of the plant,
Figure FDA0003376805300000025
for the governing coefficient of carbon emission, set omegabuyComprises electricity and gas purchasing quantities, and set omegaallAll equipment in the comprehensive energy system of the finger park, set omegaenvComprises two devices of a gas boiler and a gas turbine.
4. The method of claim 1, wherein the device condition constraints of the coupled device are that its operating output satisfies upper and lower limit constraints of device capacity; the power balance constraint of the energy network is specifically expressed as:
Figure FDA0003376805300000026
Figure FDA0003376805300000027
Figure FDA0003376805300000028
Figure FDA0003376805300000029
wherein, the sub-network power balance constraints of the power grid, the air grid, the heat supply network and the cold supply network are respectively expressed by the formulas (1) to (4);
Figure FDA00033768053000000210
Figure FDA00033768053000000211
and
Figure FDA00033768053000000212
respectively representing the output power of a gas turbine, a superior power grid, a fan and a photovoltaic in the power grid;
Figure FDA00033768053000000213
Figure FDA00033768053000000214
respectively representing the electricity load in the power grid, the storage battery, the electric refrigerator and the electricity demand of the electric boiler;
Figure FDA00033768053000000215
representing the output of gas in the gas network;
Figure FDA00033768053000000216
and
Figure FDA00033768053000000217
respectively representing the gas requirements of a gas turbine and a gas boiler;
Figure FDA00033768053000000218
and
Figure FDA00033768053000000219
respectively representing the input quantities of an electric power boiler, a gas turbine and a gas boiler in a heat supply network;
Figure FDA00033768053000000220
and
Figure FDA00033768053000000221
representing the heat requirements of the absorption chiller and the user, respectively;
Figure FDA00033768053000000222
respectively representThe output power of the absorption refrigerator and the electric refrigerator in the cold net;
Figure FDA00033768053000000223
for the cooling load demand of the users in the cooling network,
Figure FDA00033768053000000224
indicating the efficiency of the heat exchanger.
5. The method according to claim 2, wherein 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 simplified to the following form:
Figure FDA0003376805300000031
s.t.Dy＝d
Fx+Gy≥h
Figure FDA0003376805300000032
the first stage objective function is equipment planning cost, and the second stage objective function is equipment operation cost; c and f are constant coefficient matrixes corresponding to the objective function; D. f, G is a coefficient matrix corresponding to the variables under the constraint; d. h is a constant column vector; x and y are optimization variables, namely decision variables of a planning stage and decision variables of an operation stage, namely equipment capacity configuration corresponding to the planning stage and equipment output configuration corresponding to the operation stage;
Figure FDA0003376805300000033
expressing the predicted values of the output and the load of the renewable energy sources;
the main problems of the "min" structure are:
Figure FDA0003376805300000034
s.t.ζ≥fTyl
Dyl＝d
Fx+Gyl≥h
Figure FDA0003376805300000035
where x is the capacity decision quantity of the planning equipment, ζ is the introduced auxiliary variable, ylRepresenting a real-time adjustment decision added to the main problem in the first iteration;
Figure FDA0003376805300000036
representing the worst scenario added to the main problem in the first iteration;
obtaining planning equipment capacity decision quantity x by solving a main problem, and adding a group of variables x after each iterationlAnd C&CG cutting and providing a group of integer solutions for next solving subproblems;
the sub-problem of the "max-min" structure is:
Figure FDA0003376805300000037
s.t.Gy≥h-Fxl→λ
Dy＝d→ψ
Iuy＝u→υ
wherein x islA decision variable for planning equipment capacity found in a known main problem; adjusting decisions y in real time by solving sub-problemslAnd a worst output scene u, namely the equipment output configuration and the worst output scene in the real-time adjustment operation stage; lambda, psi and upsilon are dual variables.
6. The method according to claim 5, wherein the sub-problem is transformed by using a large M method and a dual theory, specifically:
converting the subproblems obtained by decomposing the original optimization model into the following dual problems by a dual theory:
Figure FDA0003376805300000038
s.t.GTλ+DTψ+Iuυ≤f
λ≥0
then, a large M method is adopted to carry out linearization processing on the bilinear terms in the dual problem:
Figure FDA0003376805300000041
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
Figure FDA0003376805300000042
wherein, pi-And pi+Is an introduced auxiliary variable; m is a sufficiently large positive real number, ΓgThe method is a precalculated value of uncertain parameters such as wind power, photovoltaic and load.
7. The method of claim 5, wherein the iterative solving step is:
let l be 1, give iteration lower and upper limits: LB, UB and convergence criterion epsilon;
solving the main problem under the expected scene, wherein the constraint of the main problem does not contain C&CG cutting to obtain a variable solution x*；
Integer solution x delivered based on main problem*Solving the subproblems and obtaining a set of worst fluctuation scenarios
Figure FDA0003376805300000043
The optimal objective function value of the sub-problem at this time is fspThe update iteration upper limit value is: UB min (UB, f)sp)；
Let l be l +1, add a set of variables to the main question and return a set of C & CG cuts such as:
Figure FDA0003376805300000044
the above equation represents the worst scenario for the current sub-problem
Figure FDA0003376805300000045
Adding the constraint form to the main problem to obtain a variable solution y*、ζ*And updating an iteration lower limit value as follows: LB ═ min (LB, ζ)*)；
Repeating iteration in this way until | UB-LB |/| LB | < epsilon, converging the model, and obtaining the equipment capacity allocation scheme x of each planning stage*And the configuration scheme y of the output force of each equipment at each moment in the operation stage*I.e. by
Figure FDA0003376805300000046
And Pi,t,h。
8. An electronic device comprising a memory and a processor, the memory having stored therein a computer program, wherein the computer program, when executed by the processor, causes the processor to implement the method of any of claims 1-7.
9. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the method according to any one of claims 1 to 7.
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