A comprehensive energy system optimization method, system, device and storage medium

Abstract

The present invention discloses an integrated energy system optimization method, system, device and storage medium. The integrated energy system optimization method comprises the following steps: taking the annual carbon emission and annualized cost of the user-side integrated energy system as the minimum as the goal, establishing an environmental benefit model and an economic cost model of the user-side integrated energy system; constructing a multi-objective optimization model of the capacity configuration and operation strategy of the user-side integrated energy system based on the environmental benefit model and the economic cost model; solving the 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; 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 integrated energy system. The problem of selecting the best scheme for the capacity configuration and operation strategy of the user-side integrated energy system can be effectively solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of energy systems, and specifically relates to an integrated energy system optimization method, system, device and storage medium. Background Art

[0002] Under the background of the "dual carbon" goal, the integrated energy system, as the latest development model of intelligent, digital, low-carbon and ecological energy systems, has become an important direction for the development of energy systems. At the same time, with the increasing personalized and diversified energy demand of users, the user-side integrated energy system, which is built and operated on the user side and provides users with integrated energy services, is becoming more and more popular among users. The user-side integrated energy system has the characteristics of multi-energy flow coupling. In order to ensure that the system fully utilizes its own advantages of multi-energy complementarity, improve the system's comprehensive energy utilization level and economic benefits, and reduce the system's carbon emission level, it is necessary to reasonably optimize the capacity configuration and operation strategy of each equipment in the user-side integrated energy system. Therefore, it is of great practical application significance and value to propose a design and operation optimization method suitable for the user-side integrated energy system. Summary of the invention

[0003] The purpose of the present invention is to provide a comprehensive energy system optimization method, system, device and storage medium to solve the problem of optimal solution selection for capacity configuration and operation strategy of user-side comprehensive energy system in the prior art.

[0004] In order to achieve the above object, the present invention adopts the following technical solution:

[0005] A first aspect of the present invention provides a user-side integrated energy system optimization method, comprising the following steps:

[0006] With the goal of minimizing the annual carbon emissions and annualized costs of the user-side integrated energy system, an environmental benefit model and an economic cost model of the user-side integrated energy system are established;

[0007] Based on the environmental benefit model and the economic cost model, a multi-objective optimization model of the capacity configuration and operation strategy of the user-side integrated energy system is constructed, wherein the multi-objective optimization model includes two parts: an objective function and constraint conditions;

[0008] 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;

[0009] Determine the priority of each optimization scheme in the multi-objective optimization non-dominated solution set, and take the optimization scheme with the highest priority as the optimal capacity configuration and operation strategy of the user-side integrated energy system.

[0010] Optionally, the environmental benefit model and economic cost model of the user-side integrated energy system are respectively as follows:

[0011] Environmental Benefit Model:

[0012] Obj ACE =NGCE+EPCE

[0013] In the formula, NGCE and EPCE are the carbon emissions corresponding to natural gas consumption and electricity purchase, respectively;

[0014] Economic cost model:

[0015] Obj ATC =CAPEX×CRF+OPEX

[0016] In the formula, CAPEX is the initial investment cost, CRF is the capital recovery rate, and OPEX is the system operating cost.

[0017] Optionally, the objective function of the multi-objective optimization model is as follows:

[0018] min(Obj ACE )

[0019] min(Obj ATC )

[0020] OBj ACE is the environmental benefit model, OBj ATC It is an economic cost model.

[0021] Optionally, the constraints of the multi-objective optimization model are equipment output, system energy balance constraints, equipment minimum partial load rate constraints, equipment maximum startup frequency constraints, and equipment climbing constraints.

[0022] Optionally, an ε-constraint method is used to solve a multi-objective optimization non-dominated solution set of the multi-objective optimization model.

[0023] Optionally, the specific method of using the ε-constraint method to solve the multi-objective optimization non-dominated solution set of the multi-objective optimization model is as follows:

[0024] 1) Single-objective optimization is performed with economic and environmental goals as objective functions, and the solution results are used as the two endpoints of the Pareto frontier;

[0025] 2) Select one of the objective functions to be subdivided, and divide it into segments within the range of the two Pareto front endpoints according to the solution requirements, and convert the objective function into several segment constraints;

[0026] 3) Under each constraint condition, the model is optimized with another objective function as the goal to obtain a series of optimal solutions, and the Pareto front is obtained by connecting the points into lines.

[0027] Optionally, the priority of each optimization solution in the multi-objective optimization non-dominated solution set is determined, specifically including:

[0028] Based on the data envelopment analysis method, a relative efficiency planning model of capacity configuration and operation strategy in each optimization scheme is established, and the relative efficiency of each optimization scheme is obtained;

[0029] The optimization schemes are prioritized according to their relative efficiency, and the scheme with the highest relative efficiency is selected as the final optimization scheme for the capacity configuration and operation strategy of the user-side integrated energy system.

[0030] A second aspect of the present invention provides a system for the integrated energy system optimization method, comprising:

[0031] The first model building module is used to establish an environmental benefit model and an economic cost model of the user-side integrated energy system with the goal of minimizing the annual carbon emissions and the annualized cost of the user-side integrated energy system;

[0032] The second model building module is used to build a multi-objective optimization model of capacity configuration and operation strategy of the user-side integrated energy system based on the environmental benefit model and the economic cost model, wherein the multi-objective optimization model includes two parts: an objective function and constraint conditions;

[0033] A solution module, used for 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;

[0034] The sorting module is used to determine the priority of each optimization scheme in the non-dominated solution set of multi-objective optimization, and take the optimization scheme with the highest priority as the optimal capacity configuration and operation strategy of the user-side integrated energy system.

[0035] A third aspect of the present invention provides 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 when executing the computer program.

[0036] According to a fourth aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the integrated energy system optimization method described in item 1 is implemented.

[0037] The beneficial effects of the present invention are as follows:

[0038] The present invention aims at the user-side integrated energy system, establishes a multi-objective optimization model for system capacity configuration and operation strategy based on the mixed integer programming method, transforms the multi-objective problem into a single objective based on the ε-constraint method, and then solves the Pareto optimal solution set. The priority of the Pareto solution is determined by the data envelopment analysis method, which can effectively solve the problem of selecting the best solution for the capacity configuration and operation strategy of the user-side integrated energy system. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The drawings constituting a part of the present application are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0040] Figure 1 It is a schematic diagram of the structure of the user-side integrated energy system in an embodiment of the present invention.

[0041] Figure 2 It is a flow chart of the comprehensive energy system optimization method in an embodiment of the present invention. DETAILED DESCRIPTION

[0042] The present invention will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that the embodiments and features in the embodiments of the present application can be combined with each other without conflict.

[0043] The following detailed description is an exemplary description, which is intended to provide further detailed description of the present invention. Unless otherwise specified, all technical terms used in the present invention have the same meaning as those generally understood by those skilled in the art to which the present application belongs. The terms used in the present invention are only for describing specific embodiments, and are not intended to limit exemplary embodiments according to the present invention.

[0044] The embodiments of the present invention propose an integrated energy system optimization method, system, device and storage medium, which scientifically and effectively optimize the capacity configuration and operation strategy of the user-side integrated energy system, improve the comprehensive energy efficiency and economy of the user-side integrated energy system, reduce the carbon emission level of the system, and provide strong support for the construction and operation of the user-side integrated energy system.

[0045] The structural diagram of the integrated energy system on the user side is as follows Figure 1As shown in the figure, the system can be divided into hot water area, electric energy area and air conditioning area according to the functional division. For the hot water area, natural gas is used to produce domestic hot water through micro-gas turbines, and then supplied to users after pumping water tanks. The water replenishment system maintains the water level of the water tank basically constant, and the circulation system maintains the water temperature of the water tank constant by forced water circulation. On the one hand, the electric boiler can be used for peak load regulation of hot water, and on the other hand, it can be used as a backup to ensure the normal supply of hot water when the gas supply is insufficient. For the electric energy area, the mains and photovoltaic power generation are used as input power sources, and the micro-gas turbine outputs electric energy. After passing through the electric energy converter, it is supplied to the heat pump and other electrical equipment inside the equipment, and the lighting electricity is delivered to the user. On the one hand, the energy storage battery can absorb the excess electric energy of the absorption system, and on the other hand, it can discharge in time when the power supply is insufficient to support the normal operation of the equipment and improve the reliability of the system. For the air conditioning area, the power is provided by the electric energy area, and the air source heat pump is used to heat or cool the circulating water, thereby adjusting the indoor temperature.

[0046] like Figure 2 As shown, in the first aspect of the present invention, for the user-side integrated energy system proposed by the present invention, the specific steps of the multi-objective optimization method of capacity configuration and operation strategy include:

[0047] (I) Method initialization

[0048] It mainly includes the system architecture of the user-side integrated energy system, main equipment parameters, user load characteristics and parameters, system environmental parameters, decision variables, and the definition and initial setting of the objective function.

[0049] (II) Establish models of various equipment components of the user-side integrated energy system, including micro-turbine model, distributed photovoltaic model, power electronic conversion device model, energy storage battery model, air source heat pump model, electric boiler model and hot water storage tank model. Among them, s represents season, h represents hour, h+1 represents the next moment, and h-1 represents the previous moment.

[0050] 1) Micro-turbine model

[0051] The mathematical model of the micro-turbine can be described by the following formula:

[0052]

[0053]

[0054]

[0055] In the formula, E mgt is the output power of the micro-turbine, η mgt is the electrical efficiency, NG mgt is the amount of natural gas consumed by the micro-turbine, CAP mgt is the installed capacity of the micro-turbine (kW), PLmgt is the partial load rate of the micro-turbine, and β1~β4 are fitting parameters.

[0056] 2) Photovoltaic model

[0057] The output power mathematical model of the photovoltaic power generation system can be expressed as:

[0058]

[0059]

[0060] A pv ≤A limit (6)

[0061] In the formula, the photovoltaic system efficiency η pv Related to solar radiation SRI, the unit of SRI is W / m 2 , and is also related to the ambient temperature T and air density AM; P1~P5 are empirical fitting parameters, SRI0, T0, AM0 are the corresponding reference values, and the specific parameter values ​​are as follows: SRI0=1000W / m 2 , T0=25℃, AM0=1.5, P1=0.2820, P2=0.3967, P3=-0.4473, P4=-0.093, P5=0.1601; E pv is the power generation of the photovoltaic system, A pv is the paving area of ​​photovoltaic cells, A limit The maximum area that can be used to install photovoltaic cells on site.

[0062] 3) Power electronic conversion device model

[0063] The mathematical model of the power electronic converter can be expressed as:

[0064]

[0065]

[0066] In the formula, Indicates the input and output power (kW) of the rectifier side (DC side) of the power electronic converter. Indicates the input and output power (kW) of the inverter side (AC side) of the power electronic converter, η rec , η inv They represent the rectification and inversion efficiency of the power electronic conversion device respectively.

[0067] At the same time, the input power of the rectifier and inverter sides is required to be kept within a certain range:

[0068]

[0069] In the formula, E inv,R 、E rec,R They respectively represent the rated input active power (kW) of the power electronic converter during rectification and inversion.

[0070] 4) Energy storage battery model

[0071] The mathematical model of energy storage battery can be expressed as:

[0072]

[0073]

[0074]

[0075]

[0076]

[0077] In the formula, E bat is the amount of electricity stored in the battery at each moment (kWh), δ is the minimum state of charge of the energy storage device, CAP bat is the energy storage battery capacity (kWh), η st is the energy storage efficiency of the energy storage battery, η st-in is the energy storage battery charging efficiency, η st-out is the discharge efficiency of the energy storage battery, E st-in is the charging capacity (kWh), E st-out is the discharge capacity (kWh), α chr and α dis are binary variables representing the charging and discharging status of the energy storage battery, ensuring that the battery cannot be charged and discharged at the same time. The bat in the formula represents the energy storage battery. is the maximum charging power, is the maximum discharge power, the remaining capacity of the energy storage battery E bat Total Capacity CAP bat The ratio is SOC.

[0078] 5) Air source heat pump model

[0079] The mathematical model of the air source heat pump can be described by the following formula:

[0080]

[0081]

[0082] In the formula, Q hp-cool Indicates the cooling capacity of the air source heat pump; COP cool is the refrigeration coefficient of the air source heat pump; E hpis the power consumption of the heat pump; Q hp-heat Indicates the heating capacity of the air source heat pump; COP heat is the heating coefficient of the air source heat pump.

[0083] 6) Electric boiler model

[0084] The energy conversion model of the electric boiler can be described by the following formula:

[0085]

[0086] In the formula, Q b is the output thermal power of the boiler; E b is the power consumption of the electric boiler; b is the thermal efficiency of the boiler.

[0087] 7) Hot water storage tank model

[0088] The hot water storage tank model can be described by the following formula:

[0089]

[0090] In the formula, Q HST-in To store heat; η HST-in is the efficiency of heat storage, as opposed to the heat dissipation efficiency of heat loss; η HST-cha is the efficiency of heat storage; η HST-disc is the efficiency of heat release; Q HST-cha Q is the heat storage HST-disc The heat storage tank cannot store and release heat at the same time, and the heat storage and release rate of the water tank needs to be controlled within a reasonable range. Therefore, α is introduced. HST-cha and α HST-disc Two binary variables are controlled, and the specific constraints are as follows:

[0091]

[0092]

[0093]

[0094] In the formula, and are the upper limits of heat storage rate and heat release rate respectively.

[0095] (III) Establish an environmental benefit model and an economic cost model. With the goal of minimizing the annual carbon emissions and annualized costs of the user-side integrated energy system, establish an environmental benefit model and an economic cost model for the user-side integrated energy system.

[0096] 1) Environmental benefit model

[0097] The carbon emissions of the user-side integrated energy system mainly come from the consumption of natural gas and the carbon dioxide emissions from the purchase of electricity from the power grid, where s represents the season and h represents the hour. The details are as follows:

[0098] Obj ACE =NGCE+EPCE (22)

[0099]

[0100]

[0101] In the formula, NGCE and EPCE are the carbon emissions corresponding to natural gas consumption and electricity purchase, respectively; ε is the carbon emission factor, ε NG is the carbon emission factor of natural gas consumption, ε grid Carbon emission factor of electricity purchased from the power grid; E im Purchase power for the grid.

[0102] 2) Economic cost model

[0103] The annual cost of the integrated energy system on the user side includes the initial investment cost and the annual operating cost, which is calculated as follows:

[0104] Obj ATC =CAPEX×CRF+OPEX (25)

[0105]

[0106]

[0107]

[0108]

[0109]

[0110] In the formula, tech represents different technical equipment, CAPEX is the initial investment cost, CRF is the capital recovery rate, CAP is the installed capacity of the equipment, and C cap is the equipment cost per unit capacity, r is the interest rate, and n is the operating life of the system equipment; OPEX is the system operating cost, which consists of fuel cost FC and maintenance cost MC, C d is the unit fuel cost, C M It is the unit operation and maintenance cost of the equipment.

[0111] (IV) Based on the mixed integer programming method, a multi-objective optimization model for the capacity configuration and operation strategy of the user-side integrated energy system is constructed. The multi-objective optimization model includes two parts: objective function and constraint conditions.

[0112] 1) Objective function

[0113] The objective function of the multi-objective optimization model of the user-side integrated energy system is established, with the minimization of annual carbon emissions and the minimization of annualized costs of the user-side integrated energy system as the objective function, where s represents season and h represents hour. As shown in the following formula:

[0114] min(Obj ACE ) (31)

[0115] min(Obj ATC ) (32)

[0116] 2) Constraints

[0117] The constraints of the multi-objective optimization model mainly include equipment output, system energy balance constraints, equipment minimum partial load rate constraints, equipment maximum start-up frequency constraints, and equipment climbing constraints.

[0118] (1) Equipment output constraints

[0119] During the operation of each energy conversion device, the output at any moment cannot exceed its corresponding installed capacity. The specific constraints are as follows:

[0120]

[0121]

[0122]

[0123]

[0124] Where CAP represents the installed capacity of each device. Represent the installed capacity of air source heat pumps for cooling and heating respectively.

[0125] (2) Energy balance constraints

[0126] The energy balance constraints include electrical energy balance, thermal energy balance and cold energy balance, as follows:

[0127]

[0128]

[0129]

[0130] In the formula, E demand is the power load of the system; Q heat is the heat load of the system; Q cool is the cooling load of the system; E ex Q is the electric power sold to the grid.mgt-heat It is the waste heat from the micro-turbine; Qb-heat is the heating from the electric boiler.

[0131] (3) Equipment minimum partial load factor constraint

[0132] In order to prevent the micro turbine from operating under lower conditions, it is necessary to introduce a minimum partial load constraint, as follows:

[0133]

[0134]

[0135] In the formula, is the upper limit of the output of the micro-turbine; is a binary variable that controls the switch state of the micro-turbine; μ is the load factor that controls the minimum load rate of the micro-turbine group.

[0136] (4) Maximum device startup frequency constraints

[0137] Since the startup time of the micro-turbine is relatively long, in order to ensure the normal operation of the system, the number of starts and stops of the micro-turbine is restricted, and the micro-turbine is limited to start and stop once each time, as follows:

[0138]

[0139]

[0140]

[0141]

[0142] in, is a binary variable representing the number of times the equipment is started and stopped per day; It is a binary variable that controls the switch state of the micro turbine.

[0143] (5) Climbing constraints

[0144]

[0145]

[0146]

[0147]

[0148] Among them, R down is the maximum downward climbing rate of the equipment, R up It is the maximum upward climbing rate of the equipment.

[0149] (V) Solve the multi-objective optimization model based on the ε-constraint method. Each solution of the non-dominated solution set (Pareto front) of the multi-objective optimization model corresponds to the capacity configuration and operation strategy of an integrated energy system.

[0150] The ε-constraint method can transform the original multi-objective optimization problem into a single-objective problem, that is, firstly, piecewise constraints are imposed on n-1 objectives of the n objective functions of the planning model, and the remaining objective function is optimized and solved for a single objective under the constraint conditions of each piecewise inequality, and finally the Pareto frontier of multi-objective optimization is obtained. The specific steps are as follows:

[0151] 1) The system is optimized with economic and environmental goals as the objective functions, and the solution is used as the two endpoints of the Pareto frontier;

[0152] 2) Select one of the objective functions to be subdivided, and divide it into segments within the range of the two Pareto front endpoints according to the solution requirements, and convert the objective function into several segment constraints;

[0153] 3) Under each constraint condition, the model is optimized with another objective function as the goal to obtain a series of optimal solutions, and the Pareto front is obtained by connecting the points into lines.

[0154] (6) Determine the priority of each solution in the Pareto optimal solution set based on data envelopment analysis, and determine the optimal capacity configuration and operation strategy of the integrated energy system on the user side.

[0155] A non-dominated solution set is obtained when performing multi-objective optimization on the user-side integrated energy system. Combined with the multi-objective evaluation and decision-making method, all solutions on the Pareto front are compared and analyzed to further select the best from the best to obtain the optimal configuration plan for the system.

[0156] The multi-objective decision-making method based on data envelopment analysis is a numerical analysis method that effectively evaluates comparable objects of the same type based on multiple input and output indicators and using linear programming methods. The specific steps of the method are as follows:

[0157] 1) Determine the evaluation decision-making unit DMU, ​​input and output indicator vector

[0158] Assuming that there are K solutions in the non-dominated solution set of the multi-objective optimization model, corresponding to K capacity configuration and operation strategy optimization schemes, the K schemes are the evaluation decision units.

[0159] The annual cost of the system is selected as the input indicator of data envelopment analysis, and the annual carbon dioxide emissions and comprehensive energy utilization efficiency of the system are selected as the output indicators of data envelopment analysis.

[0160] In this embodiment, s represents season and h represents hour. The system comprehensive energy utilization efficiency can be calculated by the following formula:

[0161]

[0162] Where η mgt , η grid , η pv They are the power generation efficiency of internal combustion engines, the conversion efficiency of primary energy into municipal electricity, and the power generation efficiency of photovoltaics.

[0163] 2) Establish a relative efficiency planning model

[0164] The priority order of the evaluation target scheme is determined by the relative efficiency θ of the evaluation target in the group. The relative efficiency value range is between 0 and 1. The higher the value, the better the scheme. In order to solve the relative efficiency, a mathematical programming model needs to be established for solution, as follows:

[0165] min θ (51)

[0166]

[0167]

[0168] λ i ≥0,i=1,....,K (54)

[0169] S - ,S + ≥0 (55)

[0170] Where Xi and Yi are the input and output indicator vector groups of the ith DMU, ​​respectively. target With Y target are the specific values ​​of the target DMU input and output indicator vector groups, respectively; λ is the weight of each DMU; S- and S+ are slack variables that are both greater than 0. K is the number of Pareto solutions.

[0171] 3) Decision-making on capacity configuration and operation strategy optimization

[0172] The relative efficiency values ​​of each evaluation target scheme are solved and sorted according to the relative efficiency values. The capacity configuration and operation strategy optimization schemes with higher relative efficiency values ​​perform better than other schemes.

[0173] A second aspect of the present invention provides a system for the integrated energy system optimization method, comprising:

[0174] The first model building module is used to establish an environmental benefit model and an economic cost model of the user-side integrated energy system with the goal of minimizing the annual carbon emissions and the annualized cost of the user-side integrated energy system;

[0175] The second model building module is used to build a multi-objective optimization model of capacity configuration and operation strategy of the user-side integrated energy system based on the environmental benefit model and the economic cost model, wherein the multi-objective optimization model includes two parts: an objective function and constraint conditions;

[0176] A solution module, used for 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;

[0177] The sorting module is used to determine the priority of each optimization scheme in the non-dominated solution set of multi-objective optimization, and take the optimization scheme with the highest priority as the optimal capacity configuration and operation strategy of the user-side integrated energy system.

[0178] A third aspect of the present invention provides 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 when executing the computer program.

[0179] According to a fourth aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the integrated energy system optimization method described in item 1 is implemented.

[0180] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0181] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0182] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0183] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0184] It is known from common technical knowledge that the present invention can be implemented by other embodiments that do not deviate from its spirit or essential features. Therefore, the above disclosed embodiments are only illustrative in all respects and are not exclusive. All changes within the scope of the present invention or within the scope equivalent to the present invention are included in the present invention.

Claims

1. A user-oriented integrated energy system optimization method, characterized in that: The steps include: With the goal of minimizing the annual carbon emissions and annualized costs of the user-side integrated energy system, an environmental benefit model and an economic cost model of the user-side integrated energy system are established; Based on the environmental benefit model and the economic cost model, a multi-objective optimization model of the capacity configuration and operation strategy of the user-side integrated energy system is constructed, wherein the multi-objective optimization model includes two parts: an objective function and constraint conditions; 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; Determine the priority of each optimization scheme in the multi-objective optimization non-dominated solution set, and use the optimization scheme with the highest priority as the optimal capacity configuration and operation strategy of the user-side integrated energy system; 1) Environmental benefit model The carbon emissions of the user-side integrated energy system mainly come from the consumption of natural gas and the carbon dioxide emissions from the purchase of electricity from the power grid, where s represents the season and h represents the hour; the details are as follows: In the formula, NGCE and EPCE They are the carbon emissions corresponding to natural gas consumption and electricity purchase; ε is the carbon emission factor, ε NG Carbon emission factor for natural gas consumption, ε grid Carbon emission factor of electricity purchased from the power grid; E im Purchase power for the grid; 2) Economic cost model The annual cost of the integrated energy system on the user side includes the initial investment cost and the annual operating cost, which is calculated as follows: In the formula, tech Represents different technical equipment, CAPEX is the initial investment cost, CRF is the capital recovery rate, CAP is the installed capacity of the equipment, C cap is the equipment cost per unit capacity, r is the interest rate, n The operating life of the system equipment; OPEX is the system operating cost, which is determined by the fuel cost FC and maintenance costs MC composition, C d is the unit fuel cost, C M The unit operation and maintenance cost of the equipment; The objective function of the multi-objective optimization model is as follows: OBj ACE is the environmental benefit model, OBj ATC is the economic cost model; The constraints of the multi-objective optimization model are equipment output, system energy balance constraint, equipment minimum partial load rate constraint, equipment maximum startup frequency constraint, and equipment climbing constraint; (1) Equipment output constraints During the operation of each energy conversion device, the output at any moment cannot exceed its corresponding installed capacity. The specific constraints are as follows: In the formula, CAP represents the installed capacity of each device; , Represents the installed capacity of air source heat pumps for cooling and heating respectively; (2) Energy balance constraints The energy balance constraints include electrical energy balance, thermal energy balance and cold energy balance, as follows: In the formula, E demand is the power load of the system; Q heat is the heat load of the system; Q cool is the cooling load of the system; E ex The electric power sold to the grid; Q mgt-heat is the waste heat from the micro-turbine; Qb-heat is the heating from the electric boiler; (3) Equipment minimum partial load factor constraint In order to prevent the micro turbine from operating under lower conditions, it is necessary to introduce a minimum partial load constraint, as follows: In the formula, is the upper limit of the output of the micro-turbine; It is a binary variable that controls the switch state of the micro-turbine; μ The load factor for controlling the minimum load rate of the micro-turbine unit; (4) Maximum startup frequency constraints for devices Since the startup time of the micro-turbine is relatively long, in order to ensure the normal operation of the system, the number of starts and stops of the micro-turbine is restricted, and the micro-turbine is limited to start and stop once each time, as follows: in, is a binary variable representing the number of times the equipment is started and stopped per day; It is a binary variable that controls the switch state of the micro-turbine; (5) Climbing constraints in, Rdown is the maximum downward climbing rate of the device, Rup is the maximum upward climbing rate of the equipment; Using ε-constraint method to solve the multi-objective optimization non-dominated solution set of multi-objective optimization model; The specific method of using the ε-constraint method to solve the multi-objective optimization non-dominated solution set of the multi-objective optimization model is as follows: 1) Take the economic goal and environmental goal as the objective function for single-objective optimization, and use the solution results as the two endpoints of the Pareto frontier; 2) Select one of the objective functions to subdivide, divide it into segments within the range of the two Pareto front endpoints according to the solution requirements, and convert the objective function into several segment constraints; 3) Under each constraint condition, the model is optimized with another objective function as the goal, so as to obtain a series of optimal solutions, and the Pareto frontier is obtained by connecting the points into lines; Determine the priority of each optimization solution in the non-dominated solution set of multi-objective optimization, including: Based on the data envelopment analysis method, a relative efficiency planning model of capacity configuration and operation strategy in each optimization scheme is established, and the relative efficiency of each optimization scheme is obtained; Arrange the priority order of the optimization schemes according to their relative efficiency, and select the scheme with the highest relative efficiency as the final optimization scheme for the capacity configuration and operation strategy of the user-side integrated energy system; 1) Determine the evaluation decision-making unit DMU, ​​input and output indicator vector Assuming that there are K solutions in the non-dominated solution set of the multi-objective optimization model, corresponding to K capacity configuration and operation strategy optimization schemes, the K schemes are the evaluation decision units; The annual cost of the system is selected as the input indicator of data envelopment analysis, and the annual carbon dioxide emissions and comprehensive energy utilization efficiency of the system are selected as the output indicators of data envelopment analysis; s represents season, h represents hour; the system comprehensive energy utilization efficiency is calculated by the following formula: In the formula, η mgt , η grid , η pv They are the power generation efficiency of internal combustion engines, the conversion efficiency of primary energy into municipal electricity, and the power generation efficiency of photovoltaics; 2) Establish a relative efficiency planning model The priority order of the evaluation target scheme is determined by the relative efficiency θ of the evaluation target in the group. The relative efficiency value ranges from 0 to 1. The higher the value, the better the scheme. In order to solve the relative efficiency, a mathematical programming model is established to solve it, as follows: Where Xi and Yi are the input and output indicator vector groups of the i-th DMU, X target and Y target are the specific values ​​of the target DMU input and output indicator vector groups, respectively; λ is the weight of each DMU; S- and S+ are slack variables both greater than 0; K is the number of Pareto solutions; 3) Decision-making on capacity configuration and operation strategy optimization Solve the relative efficiency values ​​of each evaluation target solution and sort them according to the relative efficiency values.

2. A system for the integrated energy system optimization method according to claim 1, characterized in that: include: The first model building module is used to establish an environmental benefit model and an economic cost model of the user-side integrated energy system with the goal of minimizing the annual carbon emissions and the annualized cost of the user-side integrated energy system; The second model building module is used to build a multi-objective optimization model of capacity configuration and operation strategy of the user-side integrated energy system based on the environmental benefit model and the economic cost model, wherein the multi-objective optimization model includes two parts: an objective function and constraint conditions; A solution module, used for 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; The sorting module is used to determine the priority of each optimization scheme in the non-dominated solution set of multi-objective optimization, and take the optimization scheme with the highest priority as the optimal capacity configuration and operation strategy of the user-side integrated energy system.

3. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the integrated energy system optimization method as claimed in claim 1 is implemented.

4. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the integrated energy system optimization method as claimed in claim 1 is implemented.

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