CN111626487A - Multi-evaluation index optimization planning technical method and system for comprehensive energy system - Google Patents

Multi-evaluation index optimization planning technical method and system for comprehensive energy system Download PDF

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CN111626487A
CN111626487A CN202010412923.3A CN202010412923A CN111626487A CN 111626487 A CN111626487 A CN 111626487A CN 202010412923 A CN202010412923 A CN 202010412923A CN 111626487 A CN111626487 A CN 111626487A
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CN111626487B (en
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林小杰
张淑婷
王安阳
王丽腾
钟崴
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Zhejiang University ZJU
Wuxi Huaguang Environment and Energy Group Co Ltd
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    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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Abstract

The invention discloses a multi-evaluation index optimization planning technical method and a multi-evaluation index optimization planning technical system for an integrated energy system. The method comprises the following steps: s1, providing an evaluation index model of the comprehensive energy system; s2, providing an energy supply and demand configuration model based on comprehensive evaluation indexes; s3, establishing model constraint conditions aiming at the specific comprehensive energy system; s4, providing a model solving method of an optimization algorithm; and S5, providing a comprehensive energy system optimization scheme decision selection method. The invention can provide more real evaluation for the comprehensive energy system and provide an effective and feasible scheme for planning, designing, optimizing and reconstructing the comprehensive energy system.

Description

Multi-evaluation index optimization planning technical method and system for comprehensive energy system
Technical Field
The invention relates to a multi-evaluation index optimization planning technical method and a multi-evaluation index optimization planning technical system for an integrated energy system, and belongs to the field of optimization planning of the integrated energy system.
Background
With the increasing energy demand, the problems of environmental pollution and unbalanced energy supply and demand are aggravated continuously, and the exploration of a wider clean energy consumption mode, a more efficient energy comprehensive utilization method and a more optimized energy system regulation and control means has important engineering significance. The comprehensive energy system is a key technology for realizing coordination and complementation of various energy sources and cascade utilization of multi-level energy, and can effectively support transformation and upgrading of the energy industry. At present, research on an integrated energy system is mainly developed around an electric power system, research on heating subsystems with different time scales is relatively less compared with the electric power system, part of research is over simplified for modeling the integrated energy system, an evaluation model is focused on the fields of economy and environmental protection, other benefits are ignored, and a plurality of challenges are brought to the optimization planning of the integrated energy system. Meanwhile, as the comprehensive energy system has the characteristics of multi-energy flow coupling, a general model is not completely adaptive, and the characteristic evaluation of the general model is hardly researched.
The method comprises the steps of establishing a comprehensive energy system evaluation model, determining an optimization planning objective function and model constraint conditions, generating a feasible scheme solution set by using an optimization algorithm, quantitatively analyzing by using energy structure evaluation indexes, and deciding to select an optimization planning scheme. The method can provide more real evaluation for the comprehensive energy system and provide an effective and feasible scheme for planning, designing, optimizing and reconstructing the comprehensive energy system.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides an evaluation method and an optimization planning method and system for an integrated energy system, which can provide more real evaluation for the integrated energy system and provide an effective feasible scheme for planning, designing, optimizing and modifying the integrated energy system.
The invention is realized by adopting the following technical scheme:
a multi-evaluation index optimization planning technical method for an integrated energy system comprises the following steps:
and step S1, providing an evaluation index model of the comprehensive energy system. The model is formed by layering and combining four submodels of economy, environment, energy and system, corresponding secondary indexes are selected under the submodels, and a calculation method of each submodel and the secondary indexes is determined, and the method comprises the following specific steps:
s11: and establishing an evaluation index framework of the comprehensive energy system, wherein primary indexes are an economic evaluation index model ECI, an environmental evaluation index model ENI, an energy evaluation index model RUI and a system evaluation index model RSE.
Constructing an economic evaluation index model ECI, reflecting the economic level of the comprehensive energy system by using the indexes of initial investment cost IC, operation maintenance cost MC and scrapping cost SC by adopting a comprehensive cost method, and evaluating the economic benefit optimization degree under the multi-energy configuration condition;
ECI＝IC+MC+SC
and determining each secondary index calculation method by adopting an equal-year value method and the discount rate I.
Initial investment cost
Figure BDA0002493898950000021
Wherein, ICi,jA jth device that is an ith energy source; n is the energy variety; miThe total number of production equipment of the ith energy source; li,jThe service life of the jth production facility as a source of the first energy; the sto subscript represents the energy storage device.
Cost of operation and maintenance
Figure BDA0002493898950000022
Therein, FCi,jAverage hourly fuel costs for the jth energy production facility; h isi,jThe number of hours of annual average operation of the jth production equipment for the ith energy source; RC (resistor-capacitor) capacitori,jThe annual average maintenance cost of the jth production equipment of the ith energy source.
Cost of scrapping
Figure BDA0002493898950000023
Wherein, SCi,jThe scrapping cost of the jth production equipment of the ith energy source is saved.
S12 defines an environment evaluation index model ENI, reflects the environment-friendly degree of the system on the comprehensive utilization of the multi-energy coordination and the clean energy by using a climate change index ENIC and a pollutant emission index ENIP, and evaluates the environmental benefits brought by the alternative application of renewable energy and the high-efficiency energy of the multi-energy coordination.
Determining a climate change index ENIC by a carbon trading price CP, an annual energy supply P, an annual average load rate ξ, an equivalent carbon dioxide emission CEP per energy yield, and an equivalent carbon dioxide emission CE per energy supply
Figure BDA0002493898950000031
Figure BDA0002493898950000032
Wherein, KMiTotal amount of greenhouse gas species produced by the jth production facility for the ith energy source; the equivalent factor of the carbon dioxide is converted for the whole greenhouse gas emission; GGE is the amount of greenhouse gas emitted.
Determining a calculation method of a pollutant emission index ENIP, and measuring the environmental benefit of the system by calculating the equivalent economic value of pollutant emission reduced by the system; through the environmental value VM of pollutants, the PEP of pollutant emission per unit energy supply in the reference energy system, and the ENIP of pollutant emission PE under the unit energy supply
Figure BDA0002493898950000033
Wherein L isMiTotal amount of pollutant species produced by the jth production facility for the ith energy source.
S13 defines an energy evaluation index model RUI to comprehensively describe renewable energy utilization REU and cascade energy utilization ECU, throughAnnual average total load demand W for various energy sourcesiLoad demand W satisfied by renewable energy and cascade energyREU、WECUAnd the contribution thereof
Figure BDA0002493898950000034
Determining energy evaluation index
Figure BDA0002493898950000035
S14, defining a system evaluation index model RSE to describe the coupling and mutual substitution characteristics of the multi-energy flows in the comprehensive energy system;
coupling indexes of system multi-energy flows in the comprehensive energy system comprise equipment coupling rate CRE and interconversion rate CRSE of secondary energy; device coupling refers to the ability to produce more than two sources of energy simultaneously, the device coupling ratio CRE of the ith source of energyiEnergy quantity w converted for coupled equipmenti,jAmount of energy W ultimately supplied to the user year by yeariRatio of (1)
Figure BDA0002493898950000041
Wherein, CN represents the coupling index of the equipment, namely the number of the energy types produced by the coupling equipment; a. theiThe total number of devices in the ith energy production device that have energy coupling.
Interconversion rate CRSE of secondary energy of each energy formiEnergy quantity d converted from secondary energyiAmount of energy W supplied to the end useriRatio of (1) to (b)
Figure BDA0002493898950000042
Wherein d isi,jThe quantity of the ith energy source converted from the jth energy source; b isiThe amount of other energy sources that can be converted to the ith energy source.
Coupling rate CRE of each energy form deviceiThe system equipment coupling ratio CRE is integrated through a weighted form, and the weighted weight of each energy system equipment coupling ratio is determined by the following method: for energy type G capable of directly measuring total energy amount, the energy measuring unit is GJ, the total amount is G, and the weight is WDCgThe amount of energy supplied W by the energy kindgDetermining the proportion of the total energy which can be directly measured; for energy types H which cannot directly measure the total amount of energy, such as compressed air, hot water, steam, the total amount is H, and the weight WIC ishBy calculating the total energy consumption of the primary energy source for producing the energy source as a ratio of the total energy consumption of the primary energy source for all production sources for which direct energy metering is not possible, i.e.
Figure BDA0002493898950000043
Figure BDA0002493898950000044
G+H＝N
Wherein A ishThe number of primary energy types for generating the energy types h which can not directly measure the total energy amount;
weighting WDCgAnd WIChRespectively normalizing under the respective sets of { WDC }, and { WIC } to obtain normalized weight WDCPgAnd WICPhThen all weights in the two sets are normalized together to obtain WDCSgAnd WICShAnd establishing weight constraint conditions
Figure BDA0002493898950000051
The system device coupling ratio is described as CRE
Figure BDA0002493898950000052
Interconversion rate CRSE of secondary energy of each energy formiIntegration into the interconversion rate CRSE of the secondary energy of the system by means of weighting, the weighting weights of whichThe interconversion rate CRSE of the secondary energy of the system is described as the same weight as in the calculation of the coupling ratio of the device
Figure BDA0002493898950000053
Finally, integrating the system equipment coupling rate CRE and the interconversion rate CRSE of the system secondary energy into the system secondary energy coupling rate RSE
RSE＝p1·CRSE+p2·CRE
Wherein the weight index satisfies
p1+p2＝1
And the numerical value is determined by expert evaluation or according to the specific structure, operation condition and construction target of the system.
And step S2, providing an energy supply and demand configuration model based on the comprehensive evaluation index. The method comprises the following steps of establishing a multi-objective optimization function of comprehensive energy system planning by taking economic benefits, environmental benefits and the like as optimization targets and different grade energy supply quantities of each energy device as optimization variables, and specifically comprises the following steps:
s21, describing the multi-objective optimization problem description of the energy supply and demand configuration as follows: in the planning region of the comprehensive energy system, in order to meet the requirements of heat supply load and refrigeration load of users planned or calculated in a simulation mode and match the energy grades of different load requirements, the load supply amounts of various energy equipment in the region are configured, so that the optimal load distribution mode and the optimal combination of the various energy equipment are obtained, and the set optimization target is realized.
S22 makes assumptions about the integrated energy system optimization planning model. The assumptions include: the investment cost and installed capacity of various energy equipment are positively correlated, and the operation cost and the actual load output power of the energy equipment are positively correlated; the total load demand constraint during the planning period remains unchanged; the prices of various energy sources in the planning period are kept unchanged, such as the purchase price of electric power and the purchase price of steam; the investment cost coefficients of various energy devices in the planning period are not changed along with market conditions; various types of energy devices exist which directly or indirectly emit greenhouse gases and pollutants, and the emission amount of the energy devices is in a linear relationship with the running time under rated power.
S23, according to the description and the hypothesis, based on the comprehensive energy system evaluation index system, with economic benefit, environmental benefit and the like as optimization targets, and with different grade energy supply quantities of each energy device as optimization variables, a model objective function is constructed.
S231, constructing an economic benefit objective function. The economic investment to be considered in the planning and design stage includes the initial investment cost EC of the equipmentINVAnd the equipment operation and maintenance cost ECOPRCost EC associated with operation energy consumptionFUETo obtain
Figure BDA0002493898950000061
Figure BDA0002493898950000062
ECOPR,i,j＝λi,j·ECINI,i,j
ECFUE,i,j＝FCi,j·hi,j
Figure BDA0002493898950000063
Wherein, lambda is the investment coefficient of equipment operation and maintenance; EC (EC)INIInitial purchase and installation costs for the equipment; subscript i, j is the jth production facility for the ith energy source; subscript sto is the energy storage device.
S232, an environmental benefit objective function is constructed, the environmental benefit objective function is independent of economic benefits, and the environmental benefit objective function is determined by pollutant emission E, energy annual load total W and equivalent emission coefficient gamma of pollutants and can be expressed as
Figure BDA0002493898950000064
Wherein L isMiTotal amount of pollutant species produced by the jth production facility for the ith energy source.
And step S3, establishing model constraint conditions for a specific comprehensive energy system, carrying out technical route analysis by combining energy technical characteristics meeting different energy requirements, and setting energy structure constraint conditions and equipment capacity limitation constraint. And constructing supply and demand balance constraint conditions through load simulation calculation.
S31, aiming at a specific comprehensive energy system, carrying out energy demand analysis, combining the technical characteristics of different energy sources, carrying out technical route analysis, and giving different possible combinations of energy utilization and conversion.
S32, constructing supply and demand balance constraint conditions through load simulation calculation, and setting equipment capacity limit constraint and energy structure constraint conditions; establishing heat load supply and demand balance constraints including steam demand and hot water demand, and simultaneously establishing cold load supply and demand balance constraints; constructing equipment capacity limit constraint, which is determined by the upper limit and the lower limit of energy supply (heating and refrigerating) capacity of various energy equipment, and simultaneously constraining the occupied space of all the equipment to comprehensively constrain the equipment capacity; and setting energy structure constraint conditions, and mutually restricting the heating and refrigerating design capacities of the equipment under the heating and refrigerating coupling conditions of the equipment.
And step S4, providing a model solving method of the optimizing algorithm. And carrying out optimization solution on the comprehensive energy system multi-target optimization model by utilizing the global search capability of a heuristic algorithm to obtain a pareto solution set and form a feasible scheme combination. And selecting a proper optimization algorithm to solve according to actual conditions for a specific comprehensive energy system.
And step S5, a decision selection method of the comprehensive energy system optimization scheme is provided. And carrying out sensitivity analysis on all indexes to obtain five indexes with the maximum sensitivity, and carrying out quantitative analysis by utilizing an evaluation index model of the comprehensive energy system to serve as a key factor for planning and optimizing scheme decision.
In another aspect, the present invention further provides a multi-evaluation index-based integrated energy system optimization planning system, including: comprises a comprehensive energy system evaluation index calculation module, an energy supply and demand configuration mechanism model module based on comprehensive evaluation indexes, a model constraint condition establishment module, a comprehensive energy system optimization planning method module based on an optimization algorithm and a comprehensive energy system optimization scheme decision module,
the modules are respectively subjected to module packaging, a data transmission channel is established, so that data processed by the comprehensive energy system evaluation index calculation module, the comprehensive evaluation index-based energy supply and demand configuration mechanism model module and the model constraint condition establishment module are extracted in time by the comprehensive energy system optimization planning method module and the comprehensive energy system optimization scheme decision module based on the optimization algorithm, and a decision is solved to obtain the optimal optimization planning scheme of the comprehensive energy system;
the comprehensive energy system evaluation index calculation module is used for calculating the comprehensive energy system by using four indexes (including secondary indexes) of economy, environment, energy and system, and comprises an economy evaluation index model ECI, an environment evaluation index model ENI, an energy evaluation index model RUI and a system evaluation index model RSE.
An energy supply and demand configuration mechanism model module based on comprehensive evaluation indexes takes economy, environment and energy income as optimization targets, takes different grade energy supply quantities of each energy device as optimization variables, and establishes a multi-objective optimization function of comprehensive energy system planning.
And the model constraint condition establishing module is used for establishing a boundary constraint condition by combining the technical characteristics of energy sources with different energy requirements according to the specific working conditions of a specific region, and setting an energy structure, equipment capacity limitation and supply and demand balance constraint.
And the comprehensive energy system optimization planning method module based on the optimization algorithm utilizes the global search capability of the heuristic algorithm to carry out optimization solution on the comprehensive energy system multi-target optimization model so as to obtain a pareto solution set.
And the comprehensive energy system optimization scheme decision module is used for carrying out sensitivity analysis on indexes and utilizing an energy structure evaluation system to carry out quantitative analysis, and the analysis is taken as a key factor for planning optimization scheme decision.
The invention principle of the invention is as follows:
the evaluation index of the comprehensive energy system is an important basis for system planning and design. The evaluation of the coupling degree of the multi-energy flow and the energy utilization efficiency is an important index for researching the multi-energy coordination degree of the comprehensive energy system and reducing the initial investment capacity, and the economical efficiency and the environmental benefit of the system are important measurement indexes considering both the economic aspect and the environmental protection aspect. Therefore, reasonable evaluation index architecture setting is beneficial to assisting in better planning of the comprehensive energy system, and the advantages of the system in the aspects of coordination of multiple energy technologies, reduction of multi-energy coupling cost, improvement of comprehensive energy efficiency and the like are brought into play. Secondly, consideration and attention of environmental benefits in planning and optimizing the comprehensive energy system are important ways for promoting clean development of energy, supporting efficient utilization of energy, saving energy and reducing emission.
The invention has the beneficial effects that:
the comprehensive energy system optimization planning method is characterized in that a comprehensive energy system optimization planning model is established, based on an established comprehensive economy, environment, energy and system four-dimensional evaluation index system, the economy, environmental protection, energy utilization efficiency and energy flow coupling conditions of the comprehensive energy system planning design are comprehensively evaluated aiming at the characteristics of comprehensive energy flow coupling and energy cascade utilization, economic cost elements and environment elements are selected as optimization targets, a pareto solution set is obtained by constructing constraint conditions such as supply and demand balance, capacity limitation, energy structure and the like, and feasible solutions meeting the optimization planning targets are selected. Aiming at specific indexes with high sensitivity in the solution set scheme, quantitative analysis is carried out by utilizing an energy structure evaluation system, a planning optimization scheme is selected according to the decision, the optimization problem of energy supply and demand configuration is solved, and a multi-evaluation index optimization planning system of the comprehensive energy system is established. The method and the system can provide more real evaluation for the comprehensive energy system and provide an effective feasible scheme for planning, designing, optimizing and modifying the comprehensive energy system.
Drawings
FIG. 1 is a diagram of an evaluation index system architecture of the integrated energy system;
FIG. 2 is a frame diagram of a multi-objective optimization mathematical model for energy supply and demand configuration;
FIG. 3 is a flow chart of a multi-objective optimization programming search based on an improved genetic algorithm;
FIG. 4 is a heat demand technical route diagram for a typical integrated energy system;
fig. 5 shows an optimized pareto solution set for a typical energy supply and demand configuration.
Detailed Description
The present invention will now be described in further detail with reference to the accompanying drawings.
According to the multi-evaluation index optimization planning technical method of the comprehensive energy system, a specific process of planning and optimizing the comprehensive energy system is provided.
Step S1, first, an evaluation index model of the integrated energy system is constructed, as shown in fig. 1, and is formed by layering and combining three sub-models of economy, environment, and energy, and corresponding secondary indexes are selected under the sub-models, and a calculation method of each secondary index is determined. The method comprises the following specific steps:
s11, establishing a comprehensive energy system evaluation index architecture, wherein the primary indexes are an economic evaluation index model ECI, an environmental evaluation index model ENI, an energy evaluation index model REUI and a system evaluation index model RSE.
Constructing an economic evaluation index model ECI, reflecting the economic level of the comprehensive energy system by using the indexes of initial investment cost IC, operation maintenance cost MC and scrapping cost SC by adopting a comprehensive cost method, and evaluating the economic benefit optimization degree under the multi-energy configuration condition;
ECI＝IC+MC+SC
and determining each secondary index calculation method by adopting an equal-year value method and the discount rate I. Initial investment cost
Figure BDA0002493898950000091
Wherein, ICi,jA jth device that is an ith energy source; n is the energy variety; miThe total number of production equipment of the ith energy source; li,jThe service life of the jth production facility as a source of the first energy; the sto subscript represents the energy storage device.
Cost of operation and maintenance
Figure BDA0002493898950000092
Therein, FCi,jAverage hourly fuel costs for the jth energy production facility; h isi,jThe number of hours of annual average operation of the jth production equipment for the ith energy source; RC (resistor-capacitor) capacitori,jThe annual average maintenance cost of the jth production equipment of the ith energy source.
Cost of scrapping
Figure BDA0002493898950000101
Wherein, SCi,jThe scrapping cost of the jth production equipment of the ith energy source is saved.
S12 defines an environment evaluation index model ENI, reflects the environment-friendly degree of the system on the comprehensive utilization of the multi-energy coordination and the clean energy by using a climate change index ENIC and a pollutant emission index ENIP, and evaluates the environmental benefits brought by the alternative application of renewable energy and the high-efficiency energy of the multi-energy coordination.
Determining a climate change index ENIC by a carbon trading price CP, an annual energy supply P, an annual average load rate ξ, an equivalent carbon dioxide emission CEP per energy yield, and an equivalent carbon dioxide emission CE per energy supply
Figure BDA0002493898950000102
Figure BDA0002493898950000103
Wherein, KMiTotal amount of greenhouse gas species produced by the jth production facility for the ith energy source; the equivalent factor of the carbon dioxide is converted for the whole greenhouse gas emission; GGE is the amount of greenhouse gas emitted.
Determining a calculation method of a pollutant emission index ENIP, and measuring the environmental benefit of the system by calculating the equivalent economic value of pollutant emission reduced by the system; through the environmental value VM of pollutants, the PEP of pollutant emission per unit energy supply in the reference energy system, and the ENIP of pollutant emission PE under the unit energy supply
Figure BDA0002493898950000104
Wherein L isMiTotal amount of pollutant species produced by the jth production facility for the ith energy source.
S13 defines an energy evaluation index model RUI to comprehensively describe the renewable energy utilization REU and the cascade energy utilization ECU, and the annual average total load demand W of various energy sourcesiLoad demand W satisfied by renewable energy and cascade energyREU、WECUAnd the contribution thereof
Figure BDA0002493898950000105
Determining energy evaluation index
Figure BDA0002493898950000111
S14, defining a system evaluation index model RSE, and describing the coupling and mutual substitution characteristics of multi-energy flows in the comprehensive energy system;
the coupling index RSE of the system multi-energy flow in the comprehensive energy system comprises an equipment coupling rate CRE and a mutual conversion rate CRSE of secondary energy; device coupling refers to the device coupling ratio CRE of the ith energy source, which is capable of producing more than two energy sources simultaneouslyiEnergy quantity w converted for coupled equipmenti,jAmount of energy W ultimately supplied to the user year by yeariRatio of (1)
Figure BDA0002493898950000112
Wherein, CN represents the coupling index of the equipment, namely the number of the energy types produced by the coupling equipment; a. theiThe total number of devices in the ith energy production device that have energy coupling.
Interconversion rate CRSE of secondary energy of each energy formiEnergy quantity d converted from secondary energyiAmount of energy W supplied to the end useriRatio of (1) to (b)
Figure BDA0002493898950000113
Wherein d isi,jThe quantity of the ith energy source converted from the jth energy source; b isiThe amount of other energy sources that can be converted to the ith energy source.
Coupling rate CRE of each energy form deviceiThe system equipment coupling ratio CRE is integrated through a weighted form, and the weighted weight of each energy system equipment coupling ratio is determined by the following method: for energy type G capable of directly measuring total energy amount, the energy measuring unit is GJ, the total amount is G, and the weight is WDCgThe amount of energy supplied W by the energy kindgDetermining the proportion of the total energy which can be directly measured; for energy types H which cannot directly measure the total amount of energy, such as compressed air, hot water, steam, the total amount is H, and the weight WIC ishBy calculating the energy consumption EC of the primary energy for generating the energyhThe sum of the total primary energy consumption in all the production is determined by the ratio of the primary energy consumption which cannot be directly measured, i.e.
Figure BDA0002493898950000121
Figure BDA0002493898950000122
G+H＝N
Wherein A ishThe number of primary energy types for generating the energy types h which can not directly measure the total energy amount;
weighting WDCgAnd WIChRespectively normalizing under the respective sets of { WDC }, and { WIC } to obtain normalized weight WDCPgAnd WICPhThen all weights in the two sets are normalized together to obtain WDCSgAnd WICShAnd establishing weight constraint conditions
Figure BDA0002493898950000123
The system device coupling ratio is described as CRE
Figure BDA0002493898950000124
Interconversion rate CRSE of secondary energy of each energy formiIntegrating the conversion rate CRSE of the system secondary energy source by a weighting form, wherein the weighting weight is the same as the weight in the calculation of the coupling rate of the equipment, and then the conversion rate CRSE of the system secondary energy source is described as
Figure BDA0002493898950000125
Finally, integrating the system equipment coupling rate CRE and the interconversion rate CRSE of the system secondary energy into the system secondary energy coupling rate RSE
RSE＝p1·CRSE+p2·CRE
Wherein the weight index satisfies
p1+p2＝1
And the numerical value is determined by expert evaluation or according to the specific structure, operation condition and construction target of the system.
And step S2, constructing an energy supply and demand configuration model based on the comprehensive evaluation indexes aiming at the specific comprehensive energy system. And establishing a multi-objective optimization function of the comprehensive energy system planning by taking economic benefits, environmental benefits and the like as optimization targets and taking the different-grade energy supply quantities of each energy device as optimization variables, wherein the framework structure of the multi-objective optimization function is shown in figure 2.
The method specifically comprises the following steps:
s21, expressing the multi-objective optimization problem of energy supply and demand configuration as follows: in the planning region of the comprehensive energy system, in order to meet the requirements of heat supply load and refrigeration load of users planned or calculated in a simulation mode and match the energy grades of different load requirements, the load supply amounts of various energy equipment in the region are configured, so that the optimal load distribution mode and the optimal combination of the various energy equipment are obtained, and the set optimization target is realized.
S22 makes assumptions about the integrated energy system optimization planning model. The model assumptions include: the investment cost and installed capacity of various energy equipment are positively correlated, and the operation cost and the actual load output power of the energy equipment are positively correlated; the total load demand constraint during the planning period remains unchanged; the prices of various energy sources in the planning period are kept unchanged, such as the purchase price of electric power and the purchase price of steam; the investment cost coefficients of various energy devices in the planning period are not changed along with market conditions; various types of energy devices exist which directly or indirectly emit greenhouse gases and pollutants, and the emission amount of the energy devices is in a linear relationship with the running time under rated power.
S23, according to the description and the hypothesis, based on the comprehensive energy system evaluation index system, with economic benefit, environmental benefit and the like as optimization targets, and with different grade energy supply quantities of each energy device as optimization variables, a model objective function is constructed.
S231, constructing an economic benefit objective function, wherein the economic investment to be considered in the planning and designing stage comprises the initial investment cost EC of equipmentINVAnd the equipment operation and maintenance cost ECOPRCost EC associated with operation energy consumptionFUETo obtain
Figure BDA0002493898950000131
Figure BDA0002493898950000132
ECOPR,i,j＝λi,j·ECINI,i,j
ECFUE,i,j＝FCi,j·hi,j
Figure BDA0002493898950000133
Wherein, lambda is the investment coefficient of equipment operation and maintenance; EC (EC)INIInitial purchase and installation costs for the equipment; subscript i, j is the jth production facility for the ith energy source; subscript sto is the energy storage device.
S232, an environmental benefit objective function is constructed, the environmental benefit objective function is independent of economic benefits, and the environmental benefit objective function is determined by pollutant emission E, energy annual load total W and equivalent emission coefficient gamma of pollutants and can be expressed as
Figure BDA0002493898950000141
Wherein L isMiTotal amount of pollutant species produced by the jth production facility for the ith energy source.
Step S3, establishing model constraint conditions for specific comprehensive energy system, further subdividing building types according to the general function partitions to analyze energy utilization characteristics, analyzing the technical route of available energy by combining the technical characteristics of energy meeting different energy utilization requirements, obtaining the technical route map shown in FIG. 4, and simultaneously setting energy structure constraint conditions and equipment capacity limitation constraint. And constructing supply and demand balance constraint conditions through load simulation calculation.
S31, aiming at a specific comprehensive energy system, carrying out energy demand analysis, combining the technical characteristics of different energy sources, carrying out technical route analysis, and giving different possible combinations of energy utilization and conversion.
S32, constructing supply and demand balance constraint conditions through load simulation calculation, and setting equipment capacity limit constraint.
S321 heat load supply and demand balance constraints. The heat supply requirements are divided into steam requirements and hot water requirements with different grades.
Steam demand
Figure BDA0002493898950000142
Wherein, WstIs the steam demand; mstTotal number of devices producing steam; wst,TOLThe total amount of steam demand on the user side.
Hot water demand
Figure BDA0002493898950000143
Wherein, WhsIs the steam demand; mhsTotal number of devices producing steam; whs,TOLThe total amount of steam demand on the user side.
And S322, balancing and restraining the cooling load supply and demand. Similar to the heating technical route, the civil cooling demand is mainly met by the absorption heat pump refrigeration, the ground source heat pump refrigeration and the electric driven water chilling unit refrigeration technology.
Figure BDA0002493898950000151
Wherein, WcIs the steam demand; mcTotal number of devices producing steam; wc,TOLThe total amount of steam demand on the user side.
S333, constructing equipment capacity limit constraints, which are determined by upper and lower limits of energy supply (heating and refrigerating) capacities of various energy equipment. The lower bound is a non-negative constraint condition.
Figure BDA0002493898950000152
Figure BDA0002493898950000153
Wherein, Ph、PcRespectively the capacities of the refrigerating and heating equipment; pmin、PmaxRespectively the upper and lower capacity limits.
Setting energy structure constraint conditions, and mutually restricting the heat supply and refrigeration design capacities of equipment under the condition of heat supply and refrigeration coupling of the equipment
Figure BDA0002493898950000154
Wherein the content of the first and second substances,
Figure BDA0002493898950000155
and the proportionality coefficients of the equipment influenced by the heating energy efficiency ratio and the cooling energy efficiency ratio are respectively.
The absorption heat pump does not have strict restriction on heating and cooling, but the reasonable configuration of the heating load and the cooling load can reduce the setting of the extra single-season running unit, thereby reducing the initial investment cost and limiting the setting of the single-season running unit in the form of a punishment coefficient.
And step S4, solving the model by using a heuristic optimization algorithm. The improved genetic algorithm combined with the simulated annealing algorithm is described as an example, and the solving flow is shown in fig. 3. The global searching capability of the simulated annealing algorithm is utilized to improve the genetic algorithm so as to realize the continuous correction of the fitness in the optimizing process; and self-adaptive cross probability and mutation probability are introduced to further improve the searching capability so as to obtain the pareto solution set. The multi-objective optimization planning search flow chart is shown in FIG. 4.
S41 improving the genetic algorithm according to the model features of step S2.
S411 utilizes the characteristic that the simulated annealing algorithm has stronger global search capability to improve the genetic algorithm to realize the continuous correction of the fitness in the optimizing process
Figure BDA0002493898950000156
Wherein, fit (x)imFor the fitness function based on simulated annealing correction, k is a simulated annealing function coefficient, and can be a number which is slightly less than 1.0 generally, T is an evolution algebra of a genetic algorithm, and T is0To simulate the initial temperature of annealing, typically a number of the same order of magnitude as the objective function is taken, and f (x) is the value of the objective function for an individual in the population.
S412, self-adaptive cross probability and mutation probability are introduced, so that cross and probability operation can be adjusted along with the change of population fitness, self-adaptive adjustment of genetic operation is realized, and the search capability of a genetic algorithm is improved. The adaptive cross probability and mutation probability are expressed as
Figure BDA0002493898950000161
Figure BDA0002493898950000162
Therein, fitmaxThe maximum individual fitness value in the population; fitavgIs the average fitness value of the population; fitcThe fitness value of the better individual in the cross operation; fitmIs the fitness value of an individual in the mutation operation; pc1、Pc2、Pm1、Pm2The self-adaptive cross probability and the upper and lower limits of the mutation probability.
S413, aiming at the specific comprehensive energy system model, setting relevant parameters of the optimization model according to actual conditions. Firstly, setting economic parameters, wherein the investment cost and the operation cost of different types of equipment are influenced by the operation energy efficiency ratio of the equipment and economic indexes of the equipment; setting environmental parameters, and measuring pollutant emission of unit generating capacity by adopting pollutant emission intensity; setting optimization parameters, setting simulation annealing function coefficients and upper and lower limits of self-adaptive intersection and variation probability, and solving by using an improved genetic algorithm to obtain a pareto solution set, as shown in figure 5.
And step S5, a decision selection method of the comprehensive energy system optimization scheme is provided. And carrying out sensitivity analysis on all indexes to obtain five indexes with the maximum sensitivity, and carrying out quantitative analysis by utilizing an evaluation index model of the comprehensive energy system to serve as a key factor for planning and optimizing scheme decision.
The method comprises the steps of establishing an integrated energy system evaluation model system, establishing an energy supply and demand configuration optimization model, carrying out planning design or optimization transformation on a specific integrated energy system, utilizing energy structure evaluation index quantitative analysis, deciding and screening a feasible scheme which gives consideration to economic benefits and environmental benefits, and effectively supporting energy efficient utilization and energy conservation and emission reduction. The method provides more real evaluation for the comprehensive energy system and provides a guiding scheme and practical feasibility for the energy supply and demand balance optimization problem.

Claims (10)

1. A multi-evaluation index optimization planning technical method for an integrated energy system is characterized by comprising the following steps:
step S1, a comprehensive energy system evaluation index model is provided: the model is formed by layering and combining four submodels of economy, environment, energy and system, corresponding secondary indexes are selected under the submodels, and a calculation method of each submodel and the secondary indexes is determined;
step S2, an energy supply and demand configuration model based on comprehensive evaluation indexes is provided: establishing a multi-objective optimization function of the comprehensive energy system planning by taking economic benefits and environmental benefits as optimization targets and taking different grade energy supply quantities of each energy device as optimization variables;
step S3, establishing model constraint conditions aiming at a specific comprehensive energy system: analyzing a technical route by combining the technical characteristics of energy sources meeting different energy requirements; constructing a supply and demand balance constraint condition through load simulation calculation, and setting an energy structure constraint condition and an equipment capacity limit constraint;
step S4, a model solving method of the optimization algorithm is provided: optimizing and solving the model by using the global search capability of a heuristic algorithm to obtain a pareto solution set and form a feasible scheme combination;
step S5, providing a comprehensive energy system optimization scheme decision selection method: and carrying out sensitivity analysis on all indexes to obtain five indexes with the maximum sensitivity, and carrying out quantitative analysis by utilizing an evaluation index model of the comprehensive energy system to serve as a key factor for planning and optimizing scheme decision.
2. The method for optimizing planning technology for multiple evaluation indexes of an integrated energy system according to claim 1, wherein in step S1:
constructing an economic evaluation index model ECI, and determining three secondary index calculation methods of initial investment cost IC, operation maintenance cost MC and scrapping cost SC;
defining an economic evaluation index model ECI, reflecting the economic level of the comprehensive energy system by using the indexes of initial investment cost IC, operation maintenance cost MC and scrapping cost SC by adopting a comprehensive cost method, and evaluating the economic benefit optimization degree under the multi-energy configuration condition;
ECI＝IC+MC+SC
determining each secondary index calculation method by adopting an equal-year value method and the discount rate I,
initial investment cost
Figure FDA0002493898940000021
Wherein, ICi,jA jth device that is an ith energy source; n is the energy variety; miThe total number of production equipment of the ith energy source; li,jThe service life of the jth production equipment of the ith energy source; the sto subscript represents the energy storage device;
cost of operation and maintenance
Figure FDA0002493898940000022
Therein, FCi,jAverage hourly fuel costs for the jth energy production facility; h isi,jThe number of hours of annual average operation of the jth production equipment for the ith energy source; RC (resistor-capacitor) capacitori,jThe annual average maintenance cost of the jth production equipment of the ith energy source;
cost of scrapping
Figure FDA0002493898940000023
Wherein, SCi,jThe scrapping cost of the jth production equipment of the ith energy source is saved.
3. The method of claim 1, wherein in step S1,
defining an environment evaluation index model ENI, reflecting the environment-friendly degree of the system on the comprehensive utilization of the multi-energy coordination and the clean energy by using a climate change index ENIC and a pollutant emission index ENIP, and evaluating the environmental benefit brought by the alternative application of renewable energy and the high-efficiency energy of the multi-energy coordination;
determining a climate change index ENIC by a carbon trading price CP, an annual energy supply P, an annual average load rate ξ, an equivalent carbon dioxide emission CEP per energy yield, and an equivalent carbon dioxide emission CE per energy supply
Figure FDA0002493898940000024
Figure FDA0002493898940000031
Wherein N is the energy variety; miThe total number of production equipment of the ith energy source; kMiTotal amount of greenhouse gas species produced by the jth production facility for the ith energy source; the equivalent factor of the carbon dioxide is converted for the whole greenhouse gas emission; GGE is the emission of greenhouse gases;
determining a calculation method of a pollutant emission index ENIP, and measuring the environmental benefit of the system by calculating the equivalent economic value of pollutant emission reduced by the system; through the environmental value VM of pollutants, the PEP of pollutant emission per unit energy supply in the reference energy system, and the ENIP of pollutant emission PE under the unit energy supply
Figure FDA0002493898940000032
Wherein L isMiTotal amount of pollutant species produced by the jth production facility for the ith energy source.
4. The method of claim 1, wherein in step S1,
defining an energy evaluation index model RUI to comprehensively describe renewable energy utilization REU and cascade energy utilization ECU, and passing through annual average total load demand W of various energy sourcesiLoad demand W satisfied by renewable energy and cascade energyREU、WECUAnd the contribution thereof
Figure FDA0002493898940000033
Determining energy evaluation index
Figure FDA0002493898940000034
Wherein N is the energy type quantity.
5. The method of claim 1, wherein in step S1,
defining a system evaluation index model RSE to describe the coupling and mutual substitution characteristics of multi-energy flows in the comprehensive energy system;
coupling indexes of system multi-energy flows in the comprehensive energy system comprise equipment coupling rate CRE and secondary energy interconversion rate CRSE; device coupling refers to the ability to produce more than two sources of energy simultaneously, the device coupling ratio CRE of the ith source of energyiEnergy quantity w converted for coupled equipmenti,jAmount of energy W ultimately supplied to the user year by yeariRatio of (1)
Figure FDA0002493898940000041
Wherein, CN represents the coupling index of the equipment, namely the number of the energy types produced by the coupling equipment; a. theiThe total number of devices with energy coupling in the ith energy production device;
interconversion rate CRSE of secondary energy of each energy formiEnergy quantity d converted from secondary energyiAmount of energy W supplied to the end useriRatio of (1) to (b)
Figure FDA0002493898940000042
Wherein d isi,jThe quantity of the ith energy source converted from the jth energy source; b isiThe quantity of other energy sources which can be converted into the ith energy source;
coupling rate CRE of each energy form deviceiBy weighted form integration intoThe system equipment coupling ratio CRE, and the weighted weight of each energy system equipment coupling ratio is determined by the following method: for energy type G capable of directly measuring total energy amount, the energy measuring unit is GJ, the total amount is G, and the weight is WDCgThe amount of energy supplied W by the energy kindgDetermining the proportion of the total energy which can be directly measured; for the energy types H which can not directly measure the total energy amount, the total number is H, and the weight WIC ishBy calculating the total energy consumption of the primary energy source for producing the energy source as a ratio of the total energy consumption of the primary energy source for all production sources for which direct energy metering is not possible, i.e.
Figure FDA0002493898940000043
Figure FDA0002493898940000044
G+H＝N
Wherein A ishThe number of primary energy types for generating the energy types h which can not directly measure the total energy amount; n is the energy variety;
weighting WDCgAnd WIChRespectively normalizing under the respective sets of { WDC }, and { WIC } to obtain normalized weight WDCPgAnd WICPhThen all weights in the two sets are normalized together to obtain WDCSgAnd WICShAnd establishing weight constraint conditions
Figure FDA0002493898940000051
Then the system device coupling ratio CRE is
Figure FDA0002493898940000052
Interconversion rate CRSE of secondary energy of each energy formiIntegration into the interconversion rate CRSE of the system secondary energy by means of weighting, the weighting weights of which are coupled to the equipmentThe weights in the calculation of the total rate are the same, and the interconversion rate CRSE of the secondary energy of the system is described as
Figure FDA0002493898940000053
Finally, integrating the system equipment coupling rate CRE and the interconversion rate CRSE of the system secondary energy into the system secondary energy coupling rate RSE
RSE＝p1·CRSE+p2·CRE
Wherein the weight index satisfies
p1+p2＝1
And the numerical value is determined by expert evaluation or according to the specific structure, operation condition and construction target of the system.
6. The method of claim 1, wherein the step S2 specifically comprises: describing and making reasonable assumption for the multi-objective optimization problem of energy supply and demand configuration,
the multi-objective optimization problem of energy supply and demand configuration is expressed as follows: in a planning region of the comprehensive energy system, in order to meet the requirements of heat supply load and refrigeration load of users planned or calculated in a simulation mode and match the energy grades of different load requirements, the load supply amounts of various energy equipment in the region are configured so as to obtain an optimal load distribution mode and an optimal combination of the various energy equipment and realize a set optimization target;
reasonable assumptions are made on the comprehensive energy system optimization planning model, and the assumptions comprise: the investment cost and installed capacity of various energy equipment are positively correlated, and the operation cost and the actual load output power of the energy equipment are positively correlated; the total load demand constraint during the planning period remains unchanged; the prices of various energy sources in the planning period are kept unchanged; the investment cost coefficients of various energy devices in the planning period are not changed along with market conditions; various types of energy devices exist which directly or indirectly emit greenhouse gases and pollutants, and the emission amount of the energy devices is in a linear relationship with the running time under rated power.
7. The method of claim 6, wherein in step S2, a multi-objective optimization function for the integrated energy system is established;
constructing an economic benefit objective function, and considering economic investment including initial equipment investment cost EC in a planning and designing stageINVAnd the equipment operation and maintenance cost ECOPRCost EC associated with operation energy consumptionFUETo obtain
Figure FDA0002493898940000061
Figure FDA0002493898940000062
ECOPR,i,j＝λi,j·ECINI,i,j
ECFUE,i,j＝FCi,j·hi,j
Figure FDA0002493898940000063
Wherein N is the energy variety; miThe total number of production equipment of the ith energy source; lambda is the equipment operation maintenance investment coefficient; EC (EC)INIInitial purchase and installation costs for the equipment; subscript i, j is the jth production facility for the ith energy source; subscript sto is energy storage equipment; li,jThe service life of the jth production equipment of the ith energy source;
constructing an environmental benefit objective function, wherein the environmental benefit objective function is independent of economic benefit, is determined by pollutant emission E, energy annual load total W and equivalent emission coefficient gamma of pollutants, and can be expressed as
Figure FDA0002493898940000064
Wherein L isMiTotal amount of pollutant species produced by the jth production facility for the ith energy source.
8. The method of claim 1, wherein the step S3 specifically comprises:
on the basis of the step S2, aiming at a specific comprehensive energy system, energy utilization demand analysis is carried out, technical route analysis is carried out by combining the technical characteristics of different energy sources, and different possible combinations of energy source utilization and conversion are given;
constructing a supply and demand balance constraint condition through load simulation calculation, and setting equipment capacity limit constraint and energy structure constraint conditions; establishing heat load supply and demand balance constraints including steam demand and hot water demand, and simultaneously establishing cold load supply and demand balance constraints; constructing equipment capacity limit constraints which are determined by the upper limit and the lower limit of the energy supply capacity of various energy equipment, and simultaneously constraining the occupied space of all the equipment to comprehensively constrain the equipment capacity; and setting energy structure constraint conditions, and mutually restricting the heating and refrigerating design capacities of the equipment under the heating and refrigerating coupling conditions of the equipment.
9. The method of the optimization planning technique for multiple evaluation indexes of the integrated energy system according to claim 1, wherein the step S5 is: and on the basis of the step S4, performing sensitivity analysis on all indexes to obtain five indexes with the maximum sensitivity, and performing quantitative analysis by using an evaluation index model of the comprehensive energy system to serve as a key factor for planning and optimizing scheme decision.
10. A multi-evaluation index optimization planning system of an integrated energy system is characterized by comprising an integrated energy system evaluation index calculation module, an energy supply and demand configuration mechanism model module based on integrated evaluation indexes, a model constraint condition establishment module, an integrated energy system optimization planning method module based on an optimization algorithm and an integrated energy system optimization scheme decision module;
the modules are respectively subjected to module packaging, a data transmission channel is established, so that data processed by the comprehensive energy system evaluation index calculation module, the comprehensive evaluation index-based energy supply and demand configuration mechanism model module and the model constraint condition establishment module are extracted in time by the comprehensive energy system optimization planning method module and the comprehensive energy system optimization scheme decision module based on the optimization algorithm, and a decision is solved to obtain the optimal optimization planning scheme of the comprehensive energy system;
the comprehensive energy system evaluation index calculation module is used for measuring and calculating a comprehensive energy system by using four indexes of economy, environment, energy and system, and comprises an economy evaluation index model ECI, an environment evaluation index model ENI, an energy evaluation index model RUI and a system evaluation index model RSE;
the energy supply and demand configuration mechanism model module based on the comprehensive evaluation indexes takes economy, environment and energy income as optimization targets, takes different grade energy supply quantities of each energy device as optimization variables, and establishes a multi-objective optimization function of comprehensive energy system planning;
the model constraint condition establishing module establishes a boundary constraint condition by combining the energy technical characteristics of different energy requirements according to the specific working conditions of a specific area, and sets an energy structure, equipment capacity limitation and supply and demand balance constraint;
the comprehensive energy system optimization planning method module based on the optimization algorithm utilizes the global search capability of the heuristic algorithm to carry out optimization solution on the comprehensive energy system multi-target optimization model so as to obtain a pareto solution set;
the comprehensive energy system optimization scheme decision module carries out sensitivity analysis on indexes and utilizes an energy structure evaluation system to carry out quantitative analysis, so that the sensitivity analysis is used as a key factor for planning optimization scheme decision.
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Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112348276A (en) * 2020-11-18 2021-02-09 河海大学 Comprehensive energy system planning optimization method based on multiple elements and three levels
CN112508280A (en) * 2020-12-11 2021-03-16 山东大学 Method and system for evaluating and optimizing real-time performance of comprehensive energy system
CN113222350A (en) * 2021-04-19 2021-08-06 建科环能科技有限公司 Comprehensive evaluation method and system for multi-energy system based on sensitivity analysis
CN113298351A (en) * 2021-04-27 2021-08-24 东南大学 Cold, heat and electricity energy system planning method based on time domain analysis dynamic controllability index
CN113780768A (en) * 2021-08-26 2021-12-10 浙江大学常州工业技术研究院 Chaotic variation inheritance high-dimensional multi-index comprehensive energy system planning method and system
CN114035434A (en) * 2021-11-22 2022-02-11 西南石油大学 Operation optimization method of gas-steam combined cycle power generation system
CN114997538A (en) * 2022-08-02 2022-09-02 杭州经纬信息技术股份有限公司 Dynamic planning and visualization method for industrial process upgrading based on carbon emission data

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105183991A (en) * 2015-09-07 2015-12-23 国网天津市电力公司 Planning and design method of regional integrated energy system
CN109062151A (en) * 2018-05-08 2018-12-21 国网山东省电力公司青岛供电公司 Integrated energy system multiple target Integrated design and optimal control method
CN110428103A (en) * 2019-07-31 2019-11-08 广东电网有限责任公司 A kind of renewable energy energy-storage system collaborative planning method in integrated energy system
CN110518583A (en) * 2019-08-23 2019-11-29 贵州电网有限责任公司 A kind of integrated energy system reliability estimation method considering dynamic characteristic
EP3582359A1 (en) * 2018-04-20 2019-12-18 Northeastern University Stability evaluation and static control method for electricity-heat-gas integrated energy system

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105183991A (en) * 2015-09-07 2015-12-23 国网天津市电力公司 Planning and design method of regional integrated energy system
EP3582359A1 (en) * 2018-04-20 2019-12-18 Northeastern University Stability evaluation and static control method for electricity-heat-gas integrated energy system
CN109062151A (en) * 2018-05-08 2018-12-21 国网山东省电力公司青岛供电公司 Integrated energy system multiple target Integrated design and optimal control method
CN110428103A (en) * 2019-07-31 2019-11-08 广东电网有限责任公司 A kind of renewable energy energy-storage system collaborative planning method in integrated energy system
CN110518583A (en) * 2019-08-23 2019-11-29 贵州电网有限责任公司 A kind of integrated energy system reliability estimation method considering dynamic characteristic

Cited By (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112348276A (en) * 2020-11-18 2021-02-09 河海大学 Comprehensive energy system planning optimization method based on multiple elements and three levels
CN112508280A (en) * 2020-12-11 2021-03-16 山东大学 Method and system for evaluating and optimizing real-time performance of comprehensive energy system
CN113222350A (en) * 2021-04-19 2021-08-06 建科环能科技有限公司 Comprehensive evaluation method and system for multi-energy system based on sensitivity analysis
CN113298351A (en) * 2021-04-27 2021-08-24 东南大学 Cold, heat and electricity energy system planning method based on time domain analysis dynamic controllability index
CN113298351B (en) * 2021-04-27 2023-10-24 东南大学 Cold-hot electric energy system planning method based on time domain analysis dynamic controllability index
CN113780768A (en) * 2021-08-26 2021-12-10 浙江大学常州工业技术研究院 Chaotic variation inheritance high-dimensional multi-index comprehensive energy system planning method and system
CN114035434A (en) * 2021-11-22 2022-02-11 西南石油大学 Operation optimization method of gas-steam combined cycle power generation system
CN114035434B (en) * 2021-11-22 2023-09-01 西南石油大学 Operation optimization method of gas-steam combined cycle power generation system
CN114997538A (en) * 2022-08-02 2022-09-02 杭州经纬信息技术股份有限公司 Dynamic planning and visualization method for industrial process upgrading based on carbon emission data

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