CN113902225A - Comprehensive energy system optimization method, system, device and storage medium - Google Patents
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Abstract
The invention discloses a method, a system, a device and a storage medium for optimizing a comprehensive energy system, wherein the method for optimizing the comprehensive energy system comprises the following steps: establishing an environmental benefit model and an economic cost model of the user-side comprehensive energy system by taking the minimum annual carbon emission and the minimum annual cost of the user-side comprehensive energy system as targets; constructing a multi-objective optimization model of the capacity configuration and the operation strategy of the user-side comprehensive energy system based on the environmental benefit model and the economic cost model; solving a multi-objective optimization non-dominated solution set of the multi-objective optimization model, wherein each solution in the multi-objective optimization non-dominated solution set corresponds to an optimization scheme; and determining the priority of each optimization scheme in the multi-objective optimization non-dominated solution set, and taking the optimization scheme with the highest priority as the optimal capacity configuration and operation strategy of the user-side comprehensive energy system. The optimal scheme selection problem of the capacity configuration and the operation strategy of the comprehensive energy system at the user side can be effectively solved.
Description
Technical Field
The invention belongs to the technical field of energy systems, and particularly relates to a comprehensive energy system optimization method, a comprehensive energy system optimization system, a comprehensive energy system optimization device and a storage medium.
Background
Under the target background of 'double carbon', a comprehensive energy system serving as a latest development mode of intellectualization, digitalization, low carbonization and ecology of an energy system has become an important direction for the development of the energy system. Meanwhile, with the increasing diversified and diversified energy demands of users, a user-side integrated energy system which is built and operated on a user side and provides integrated energy services for the users is increasingly favored by more users. The user-side comprehensive energy system has the characteristic of multi-energy flow coupling, so that the system can be ensured to fully exert the advantage of self multi-energy complementation, the comprehensive energy utilization level and the economic benefit of the system are improved, the carbon emission level of the system is reduced, and reasonable optimization design needs to be carried out on the capacity configuration and the operation strategy of each device in the user-side comprehensive energy system. Therefore, the design and operation optimization method suitable for the user-side comprehensive energy system has important practical application significance and value.
Disclosure of Invention
The invention aims to provide a method, a system, a device and a storage medium for optimizing an integrated energy system, so as to solve the problem of optimal scheme selection of capacity allocation and operation strategies of the integrated energy system at a user side in the prior art.
In order to achieve the purpose, the invention adopts the following technical scheme:
in a first aspect of the present invention, a user-side-oriented method for optimizing an integrated energy system is provided, which includes the following steps:
establishing an environmental benefit model and an economic cost model of the user-side comprehensive energy system by taking the minimum annual carbon emission and the minimum annual cost of the user-side comprehensive energy system as targets;
constructing a multi-objective optimization model of the capacity configuration and the operation strategy of the user-side comprehensive energy system based on the environmental benefit model and the economic cost model, wherein the multi-objective optimization model comprises an objective function and a constraint condition;
solving a multi-objective optimization non-dominated solution set of a multi-objective optimization model, wherein each solution in the multi-objective optimization non-dominated solution set corresponds to an optimization scheme;
and determining the priority of each optimization scheme in the multi-objective optimization non-dominated solution set, and taking the optimization scheme with the highest priority as the optimal capacity configuration and operation strategy of the user-side comprehensive energy system.
Optionally, the environmental benefit model and the economic cost model of the user-side integrated energy system are respectively as follows:
the environmental benefit model is as follows:
ObjACE＝NGCE+EPCE
in the formula, NGCE and EPCE are respectively carbon emissions corresponding to natural gas consumption and electricity purchase;
an economic cost model:
ObjATC＝CAPEX×CRF+OPEX
in the formula, CAPEX is the initial investment cost, CRF is the capital recovery, and OPEX is the system operating cost.
Optionally, the objective function of the multi-objective optimization model is as follows:
min(ObjACE)
min(ObjATC)
OBjACEas an environmental benefit model, OBjATCIs an economic cost model.
Optionally, the constraint conditions of the multi-objective optimization model are device output, system energy balance constraint, device minimum part load rate constraint, device maximum starting frequency constraint, and device climbing constraint.
Optionally, an epsilon-constraint method is used for solving a multi-objective optimization non-dominated solution set of the multi-objective optimization model.
Optionally, the specific method for solving the multi-objective optimization non-dominated solution set of the multi-objective optimization model by using the epsilon-constraint method is as follows:
1) respectively taking an economic target and an environmental protection target as target functions to carry out single-target optimization, and taking a solving result as two end points of a pareto frontier;
2) selecting one of the objective functions to subdivide, segmenting the objective function in the range of the two pareto leading edge endpoints according to the solving requirement, and converting the objective function into a plurality of segments of constraint conditions;
3) and under the constraint condition of each section, carrying out optimization solution on the model by taking another objective function as a target so as to obtain a series of optimal solutions, and connecting points to form a line to obtain the pareto front.
Optionally, determining the priority of each optimization scheme in the multi-objective optimization non-dominated solution set specifically includes:
establishing a relative efficiency planning model of capacity allocation and operation strategies in each optimization scheme based on a data envelope analysis method, and solving to obtain the relative efficiency of each optimization scheme;
and arranging the priority order of the optimization schemes according to the relative efficiency, and selecting the scheme with the maximum relative efficiency as a final optimization scheme of the user-side comprehensive energy system capacity configuration and operation strategy.
In a second aspect of the present invention, there is provided a system for the integrated energy system optimization method, comprising:
the first model building module is used for building an environmental benefit model and an economic cost model of the user-side integrated energy system by taking the minimum annual carbon emission and the minimum annual cost of the user-side integrated energy system as targets;
the second model building module is used for building a multi-objective optimization model of the capacity allocation and the operation strategy of the user-side comprehensive energy system based on the environmental benefit model and the economic cost model, wherein the multi-objective optimization model comprises an objective function and a constraint condition;
the solving module is used for solving a multi-objective optimization non-dominated solution set of the multi-objective optimization model, and each solution in the multi-objective optimization non-dominated solution set corresponds to an optimization scheme;
and the sequencing module is used for determining the priority of each optimization scheme in the multi-objective optimization non-dominated solution set and taking the optimization scheme with the highest priority as the optimal capacity configuration and operation strategy of the user-side comprehensive energy system.
In a third aspect of the present invention, a computer device is provided, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method for optimizing an integrated energy system when executing the computer program.
In a fourth aspect of the present invention, a computer-readable storage medium is provided, which stores a computer program, which when executed by a processor, implements the method for optimizing an integrated energy system.
The invention has the following beneficial effects:
aiming at a user-side comprehensive energy system, the invention establishes a system capacity configuration and operation strategy multi-objective optimization model based on a mixed integer programming method, singly targets multi-objective questions based on an epsilon-constraint method, further solves to obtain a pareto optimal solution set, determines the priority of the pareto solutions through a data envelope analysis method, and can effectively solve the problem of optimal scheme selection of the user-side comprehensive energy system capacity configuration and operation strategy.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the invention and, together with the description, serve to explain the invention and not to limit the invention. In the drawings:
fig. 1 is a schematic structural diagram of a user-side integrated energy system according to an embodiment of the present invention.
Fig. 2 is a flowchart of an integrated energy system optimization method according to an embodiment of the present invention.
Detailed Description
The present invention will be described in detail below with reference to the embodiments with reference to the attached drawings. It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
The following detailed description is exemplary in nature and is intended to provide further details of the invention. Unless otherwise defined, all technical terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention.
The embodiment of the invention provides a method, a system, a device and a storage medium for optimizing a comprehensive energy system, which are used for scientifically and effectively optimizing the capacity configuration and the operation strategy of the comprehensive energy system at a user side, improving the comprehensive energy efficiency level and the economical efficiency of the comprehensive energy system at the user side, reducing the carbon emission level of the system and providing powerful support for the construction and the operation of the comprehensive energy system at the user side.
The schematic structural diagram of the user-side integrated energy system is shown in fig. 1, and the system can be divided into a hot water area, an electric energy area and an air conditioning area according to functions. For the hot water area, natural gas produces domestic hot water through the micro-combustion engine, and the domestic hot water is supplied to users through the water pumping box. The water replenishing system maintains the water level of the water tank to be basically constant, and the circulating system enables the water temperature of the water tank to be maintained to be constant through forced water circulation. The electric boiler can be used for peak regulation of hot water load on one hand, and can be used for standby on the other hand, and normal supply of hot water can still be ensured when the gas supply is insufficient. For the electric energy area, commercial power and photovoltaic power generation are used as input power supplies, the micro-combustion engine is used for outputting electric energy, the electric energy is supplied to the heat pump after passing through the electric energy converter, other internal electric equipment is equipped, and illumination power utilization is conveyed to users. The energy storage battery can absorb redundant electric energy of the system, and can timely discharge to support normal operation of the equipment when the power supply is insufficient, so that the reliability of the system is improved. For the air conditioning area, the electric energy area provides a power supply, and the air source heat pump is adopted to realize the heating or cooling of the circulating water, so as to further realize the regulation of the indoor temperature.
As shown in fig. 2, in a first aspect of the present invention, for the user-side integrated energy system provided by the present invention, the specific steps of the method for multi-objective optimization of capacity allocation and operation strategy include:
method initialization
The method mainly comprises the definition and initial setting of a system architecture, equipment main parameters, user load characteristics and parameters, system environment parameters, decision variables and objective functions of a user-side comprehensive energy system.
And (II) establishing a model of each equipment element of the user-side comprehensive energy system, specifically comprising a micro-gas turbine model, a distributed photovoltaic model, a power electronic conversion device model, an energy storage battery model, an air source heat pump model, an electric boiler model and a hot water storage tank model. Wherein s represents season, h represents hour, h +1 represents next time, and h-1 represents last time.
1) Micro-combustion engine model
The mathematical model of a micro-combustion engine can be described by the following equation:
in the formula, EmgtIs the output power of the micro-combustion engine, etamgtFor electrical efficiency, NGmgtAmount of natural gas consumed by micro-combustion engines, CAPmgtIs the installed capacity (kW), PL of micro-combustion enginemgtIs the partial load factor, beta, of a micro-combustion engine1～β4Are fitting parameters.
2) Photovoltaic model
The mathematical model of the output power of the photovoltaic power generation system can be expressed as:
Apv≤Alimit (6)
in the formula, the efficiency eta of the photovoltaic systempvIs related to the degree of solar radiation SRI, and the unit of the SRI is W/m2And is also related to the ambient temperature T and the air density AM; p1～P5For empirical fitting of parameters, SRI0、T0、AM0The reference values are respectively corresponding, and the specific parameters take the following values: SRI0＝1000W/m2，T0＝25℃，AM0＝1.5，P1＝0.2820，P2＝0.3967，P3＝-0.4473，P4＝-0.093，P5＝0.1601；EpvIs the power generation of the photovoltaic system, ApvAs a photovoltaic cellArea of laying of (A)limitThe maximum area in which the photovoltaic cell can be installed in the field.
3) Power electronic converter model
The mathematical model of the power electronic converter may be expressed as:
in the formula (I), the compound is shown in the specification,
represents the input and output power (kW) of the rectifying side (DC side) of the power electronic converter,
indicating the input and output power (kW), eta of the inverter side (AC side) of the power electronic converterrec、ηinvRespectively showing the rectification efficiency and the inversion efficiency of the power electronic conversion device.
Meanwhile, the input power at the rectification and inversion sides is required to be kept within a certain range:
in the formula, Einv,R、Erec,RThe rated input active power (kW) of the power electronic converter during rectification and inversion are shown, respectively.
4) Energy storage battery model
The mathematical model of the energy storage battery can be expressed as:
in the formula, EbatThe amount of electricity stored in the battery (kWh) at each moment, δ being the minimum value of the state of charge of the energy storage device, CAPbatFor the energy storage cell capacity (kWh), ηstFor storing the electrical efficiency of the energy storage cell, etast-inEfficiency of charging of energy storage cells, ηst-outFor the discharge efficiency of the energy storage cell, Est-inIs the charge (kWh), Est-outAs discharge capacity (kWh), αchrAnd alphadisIn order to respectively represent binary variables of the charging and discharging states of the energy storage battery and ensure that the charging and discharging of the battery can not be carried out simultaneously, bat in a formula represents the energy storage battery,
in order to be the maximum charging power,
the residual capacity E of the energy storage battery is the maximum discharge powerbatAnd the total capacity CAPbatThe ratio of (d) is the SOC.
5) Air source heat pump model
The mathematical model of the air source heat pump can be described by:
in the formula, Qhp-coolThe refrigerating capacity of the air source heat pump is represented; COPcoolThe refrigeration coefficient of the air source heat pump; ehpIs the power consumption of the heat pump; qhp-heatRepresenting the heat supply amount of the air source heat pump; COPheatThe heating coefficient of the air source heat pump.
6) Electric boiler model
The energy conversion model of an electric boiler can be described by the following equation:
in the formula, QbOutputting thermal power for the boiler; ebThe power consumption of the electric boiler; etabIs the thermal efficiency of the boiler.
7) Heat storage water tank model
The hot water storage tank model can be described by the following formula:
in the formula, QHST-inFor storing heat; etaHST-inEfficiency for storing heat, as opposed to heat dissipation efficiency for heat loss; etaHST-chaEfficiency in heat storage; etaHST-discEfficiency on heat release; qHST-chaThe amount of stored heat is; qHST-discThe heat release is shown. The heat storage and the heat release of the heat storage water tank can not be carried out simultaneously, and the heat storage and the heat release rate of the water tank needs to be controlled within a reasonable range, so that alpha is introducedHST-chaAnd alphaHST-discTwo binary variables are controlled with the specific constraints as follows:
in the formula (I), the compound is shown in the specification,
and
the upper limits of the heat accumulation rate and the heat release rate, respectively.
And (III) establishing an environmental benefit model and an economic cost model. And establishing an environmental benefit model and an economic cost model of the user-side comprehensive energy system by taking the minimum annual carbon emission and the minimum annual cost of the user-side comprehensive energy system as targets.
1) Environmental benefit model
The carbon emission of the integrated energy system at the user side mainly comes from carbon dioxide emission caused by consuming natural gas and purchasing electricity from a power grid, wherein s represents season, and h represents hour. The method comprises the following specific steps:
ObjACE＝NGCE+EPCE (22)
in the formula, NGCE and EPCE are respectively carbon emissions corresponding to natural gas consumption and electricity purchase; ε is the carbon emission factor, εNGTo consume the carbon emission factor of natural gas, epsilongridA power grid purchase carbon emission factor; eimAnd purchasing power for the power grid.
2) Economic cost model
The annual cost of the user-side comprehensive energy system comprises initial investment cost conversion and annual operation cost, and the specific calculation is as follows:
ObjATC＝CAPEX×CRF+OPEX (25)
wherein tech represents different technical equipment, CAPEX is initial investment cost, CRF is capital recovery, CAP is installed capacity of equipment, CcapThe equipment cost per unit capacity, r the interest rate and n the operation life of the system equipment; OPEX is the system operating cost, consisting of fuel cost FC and maintenance cost MC, CdAs cost per unit fuel, CMThe unit operating maintenance cost of the equipment.
And (IV) constructing a multi-objective optimization model of the capacity allocation and the operation strategy of the user-side comprehensive energy system based on a mixed integer programming method, wherein the multi-objective optimization model comprises an objective function and a constraint condition.
1) Objective function
And establishing an objective function of a multi-objective optimization model of the user-side integrated energy system, and taking the minimum annual carbon emission and the minimum annual cost of the user-side integrated energy system as the objective function, wherein s represents the season and h represents the hour. The following formula:
min(ObjACE) (31)
min(ObjATC) (32)
2) constraint conditions
The constraint conditions of the multi-objective optimization model mainly comprise equipment output, system energy balance constraint, equipment minimum part load rate constraint, equipment maximum starting frequency constraint and equipment climbing constraint.
(1) Device force constraints
When each energy conversion device is in the operation process, the output force at each moment cannot exceed the corresponding installed capacity, and the specific constraints are as follows:
in the formula, CAP represents the installed capacity of each device.
Respectively representing the installed capacity of the air source heat pump for supplying cold and heat.
(2) Energy balance constraint
The energy balance constraint comprises electric energy balance, heat energy balance and cold energy balance, and specifically comprises the following steps:
in the formula, EdemandIs the electrical load of the system; qheatIs the thermal load of the system; qcoolIs the cold load of the system; eexTo sell electrical power to the grid. Qmgt-heatThe waste heat of the micro-combustion engine; qb-heat is electric boiler heating.
(3) Device minimum fractional load rate constraints
In order to avoid the micro-combustion engine running under a lower working condition, a minimum part load constraint needs to be introduced, which is as follows:
in the formula (I), the compound is shown in the specification,
the upper limit of the output of the micro-combustion engine;
a binary variable for controlling the on-off state of the micro-combustion engine; mu is a load factor for controlling the minimum load rate of the micro-combustion engine set.
(4) Maximum startup frequency constraint of equipment
Because the micro-gas turbine starting time is longer, in order to guarantee the normal operation of the system, the number of times of starting and stopping the micro-gas turbine is restricted, and the micro-gas turbine is limited to be started and stopped at most once every time, and the method specifically comprises the following steps:
wherein the content of the first and second substances,
the number of the equipment is represented as a binary variable of the daily start-stop times of the equipment;
is a binary variable for controlling the on-off state of the micro-combustion engine.
(5) Climbing restraint
Wherein R isdownTo be provided withMaximum ramp-down rate, RupIs the maximum upward ramp rate of the device.
And (V) solving each solution of a multi-objective optimization non-dominated solution set (pareto frontier) of the multi-objective optimization model based on an epsilon-constraint method, wherein the solution corresponds to the capacity configuration and the operation strategy of the comprehensive energy system.
The epsilon-constraint method can convert the original multi-objective optimization problem into a single-objective problem, namely firstly, carrying out segmentation constraint on n-1 objectives in n objective functions of a planning model, and carrying out single-objective optimization solution on only the remaining objective functions under the constraint condition of each segmentation inequality to finally obtain the pareto frontier of multi-objective optimization, and the method comprises the following specific steps:
1) respectively taking an economic target and an environmental protection target as target functions to carry out single-target optimization on the system, and taking a solving result as two end points of a pareto frontier;
2) selecting one of the objective functions to subdivide, segmenting the objective function in the range of the two pareto leading edge endpoints according to the solving requirement, and converting the objective function into a plurality of segments of constraint conditions;
3) and under the constraint condition of each section, carrying out optimization solution on the model by taking another objective function as a target so as to obtain a series of optimal solutions, and connecting points to form a line to obtain the pareto front.
And (VI) determining the priority of each scheme in the pareto optimal solution set based on a data envelope analysis method, and determining the optimal capacity configuration and operation strategy of the user-side comprehensive energy system.
And (3) solving a non-dominated solution set obtained by the user side comprehensive energy system during multi-objective optimization, comparing and analyzing all solutions on the pareto frontier by combining a multi-objective evaluation and decision method, and further optimizing to obtain an optimal configuration scheme of the system.
The multi-objective decision method based on data envelope analysis is a numerical analysis method for effectively evaluating comparable objects of the same type according to multiple input and output indexes by using a linear programming method, and the method specifically comprises the following steps:
1) determining evaluation decision unit DMU, input and output index vector
And assuming that K solutions are concentrated in the non-dominated solution set of the multi-objective optimization model and correspond to K capacity configuration and operation strategy optimization schemes, the K schemes are evaluation decision units.
The annual cost of the system is selected as an input index of data envelope analysis, and the annual carbon dioxide emission and the comprehensive energy utilization efficiency of the system are selected as output indexes of the data envelope analysis.
In this example, s represents season, and h represents hour. Wherein, the comprehensive energy utilization efficiency of the system can be calculated by the following formula:
in the formula etamgt、ηgrid、ηpvThe power generation efficiency of the internal combustion engine, the conversion efficiency of converting primary energy into commercial power and the photovoltaic power generation efficiency are respectively.
2) Establishing a relative efficiency planning model
The priority sequence of the evaluation target scheme is determined by the relative efficiency theta of the evaluation target in the group, the value range of the relative efficiency is 0-1, and the scheme is better if the value is higher. In order to solve the relative efficiency, a mathematical programming model needs to be established for solving, which specifically comprises the following steps:
min θ (51)
λi≥0,i＝1,....,K (54)
S-,S+≥0 (55)
wherein Xi and Yi are the input and output index vector sets of the ith DMU, respectively, XtargetAnd YtargetSpecific values of the evaluation target DMU input and output index vector groups are respectively, and lambda is the weight of each DMU; s-, and S + are relaxation variables each greater than 0. K is the number of pareto solutions.
3) Capacity allocation and operation strategy optimization scheme decision
And solving the relative efficiency values of the evaluation target schemes, and sequencing according to the relative efficiency values, wherein the higher the relative efficiency value is, the better the capacity configuration and operation strategy optimization scheme is compared with other schemes.
In a second aspect of the present invention, there is provided a system for the integrated energy system optimization method, comprising:
the first model building module is used for building an environmental benefit model and an economic cost model of the user-side integrated energy system by taking the minimum annual carbon emission and the minimum annual cost of the user-side integrated energy system as targets;
the second model building module is used for building a multi-objective optimization model of the capacity allocation and the operation strategy of the user-side comprehensive energy system based on the environmental benefit model and the economic cost model, wherein the multi-objective optimization model comprises an objective function and a constraint condition;
the solving module is used for solving a multi-objective optimization non-dominated solution set of the multi-objective optimization model, and each solution in the multi-objective optimization non-dominated solution set corresponds to an optimization scheme;
and the sequencing module is used for determining the priority of each optimization scheme in the multi-objective optimization non-dominated solution set and taking the optimization scheme with the highest priority as the optimal capacity configuration and operation strategy of the user-side comprehensive energy system.
In a third aspect of the present invention, a computer device is provided, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method for optimizing an integrated energy system when executing the computer program.
In a fourth aspect of the present invention, a computer-readable storage medium is provided, which stores a computer program, which when executed by a processor, implements the method for optimizing an integrated energy system.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be appreciated by those skilled in the art that the invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The embodiments disclosed above are therefore to be considered in all respects as illustrative and not restrictive. All changes which come within the scope of or equivalence to the invention are intended to be embraced therein.
Claims (10)
1. A comprehensive energy system optimization method facing a user side is characterized by comprising the following steps:
establishing an environmental benefit model and an economic cost model of the user-side comprehensive energy system by taking the minimum annual carbon emission and the minimum annual cost of the user-side comprehensive energy system as targets;
constructing a multi-objective optimization model of the capacity configuration and the operation strategy of the user-side comprehensive energy system based on the environmental benefit model and the economic cost model, wherein the multi-objective optimization model comprises an objective function and a constraint condition;
solving a multi-objective optimization non-dominated solution set of a multi-objective optimization model, wherein each solution in the multi-objective optimization non-dominated solution set corresponds to an optimization scheme;
and determining the priority of each optimization scheme in the multi-objective optimization non-dominated solution set, and taking the optimization scheme with the highest priority as the optimal capacity configuration and operation strategy of the user-side comprehensive energy system.
2. The method for optimizing an integrated energy system according to claim 1, wherein the environmental benefit model and the economic cost model of the user-side integrated energy system are respectively as follows:
the environmental benefit model is as follows:
ObjACE＝NGCE+EPCE
in the formula, NGCE and EPCE are respectively carbon emissions corresponding to natural gas consumption and electricity purchase;
an economic cost model:
ObjATC＝CAPEX×CRF+OPEX
in the formula, CAPEX is the initial investment cost, CRF is the capital recovery, and OPEX is the system operating cost.
3. The integrated energy system optimization method according to claim 1, wherein the objective function of the multi-objective optimization model is as follows:
min(ObjACE)
min(ObjATC)
OBjACEas an environmental benefit model, OBjATCIs an economic cost model.
4. The method according to claim 1, wherein the constraint conditions of the multi-objective optimization model are a device output, a system energy balance constraint, a device minimum part load rate constraint, a device maximum start-up frequency constraint and a device climbing constraint.
5. The integrated energy system optimization method according to claim 1, wherein the multi-objective optimization non-dominated solution set of the multi-objective optimization model is solved using an epsilon-constraint method.
6. The method for optimizing the integrated energy system according to claim 5, wherein the specific method for solving the multi-objective optimization non-dominated solution set of the multi-objective optimization model by using the epsilon-constraint method is as follows:
1) respectively taking an economic target and an environmental protection target as target functions to carry out single-target optimization, and taking a solving result as two end points of a pareto frontier;
2) selecting one of the objective functions to subdivide, segmenting the objective function in the range of the two pareto leading edge endpoints according to the solving requirement, and converting the objective function into a plurality of segments of constraint conditions;
3) and under the constraint condition of each section, carrying out optimization solution on the model by taking another objective function as a target so as to obtain a series of optimal solutions, and connecting points to form a line to obtain the pareto front.
7. The method for optimizing the integrated energy system according to claim 1, wherein determining the priority of each optimization scheme in the multi-objective optimization non-dominated solution set specifically comprises:
establishing a relative efficiency planning model of capacity allocation and operation strategies in each optimization scheme based on a data envelope analysis method, and solving to obtain the relative efficiency of each optimization scheme;
and arranging the priority order of the optimization schemes according to the relative efficiency, and selecting the scheme with the maximum relative efficiency as a final optimization scheme of the user-side comprehensive energy system capacity configuration and operation strategy.
8. A system for the integrated energy system optimization method of claim 1, comprising:
the first model building module is used for building an environmental benefit model and an economic cost model of the user-side integrated energy system by taking the minimum annual carbon emission and the minimum annual cost of the user-side integrated energy system as targets;
the second model building module is used for building a multi-objective optimization model of the capacity allocation and the operation strategy of the user-side comprehensive energy system based on the environmental benefit model and the economic cost model, wherein the multi-objective optimization model comprises an objective function and a constraint condition;
the solving module is used for solving a multi-objective optimization non-dominated solution set of the multi-objective optimization model, and each solution in the multi-objective optimization non-dominated solution set corresponds to an optimization scheme;
and the sequencing module is used for determining the priority of each optimization scheme in the multi-objective optimization non-dominated solution set and taking the optimization scheme with the highest priority as the optimal capacity configuration and operation strategy of the user-side comprehensive energy system.
9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the integrated energy system optimization method of any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, in which a computer program is stored, which, when being executed by a processor, carries out the integrated energy system optimization method according to any one of claims 1 to 7.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114492991A (en) * | 2022-01-25 | 2022-05-13 | 苗韧 | Multi-objective optimization index automatic decomposition method based on hierarchical sequence method |
| CN114818059A (en) * | 2022-04-11 | 2022-07-29 | 深圳市微筑科技有限公司 | Building energy-saving strategy optimization control method, device, equipment and readable storage medium |
| CN116485042A (en) * | 2023-06-16 | 2023-07-25 | 国网上海能源互联网研究院有限公司 | Method and device for optimizing park energy system operation based on load clustering |
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2021
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114492991A (en) * | 2022-01-25 | 2022-05-13 | 苗韧 | Multi-objective optimization index automatic decomposition method based on hierarchical sequence method |
| CN114818059A (en) * | 2022-04-11 | 2022-07-29 | 深圳市微筑科技有限公司 | Building energy-saving strategy optimization control method, device, equipment and readable storage medium |
| CN114818059B (en) * | 2022-04-11 | 2023-08-29 | 深圳市微筑科技有限公司 | Building energy-saving strategy optimization control method, device, equipment and readable storage medium |
| CN116485042A (en) * | 2023-06-16 | 2023-07-25 | 国网上海能源互联网研究院有限公司 | Method and device for optimizing park energy system operation based on load clustering |
| CN116485042B (en) * | 2023-06-16 | 2023-09-01 | 国网上海能源互联网研究院有限公司 | Method and device for optimizing park energy system operation based on load clustering |
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