CN114294630A - ORC-based carbon emission regulation system, method and medium for novel system - Google Patents

ORC-based carbon emission regulation system, method and medium for novel system Download PDF

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CN114294630A
CN114294630A CN202111448414.7A CN202111448414A CN114294630A CN 114294630 A CN114294630 A CN 114294630A CN 202111448414 A CN202111448414 A CN 202111448414A CN 114294630 A CN114294630 A CN 114294630A
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energy
orc
consumed
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heat
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张佳钰
纪捷
秦泾鑫
王赓勉
王夫诚
朱跃伍
周孟雄
苏姣月
汤健康
郭仁威
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Huaiyin Institute of Technology
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    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A30/00—Adapting or protecting infrastructure or their operation
    • Y02A30/27—Relating to heating, ventilation or air conditioning [HVAC] technologies
    • Y02A30/274—Relating to heating, ventilation or air conditioning [HVAC] technologies using waste energy, e.g. from internal combustion engine
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B30/00—Energy efficient heating, ventilation or air conditioning [HVAC]
    • Y02B30/62—Absorption based systems
    • Y02B30/625—Absorption based systems combined with heat or power generation [CHP], e.g. trigeneration
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/40—Solar thermal energy, e.g. solar towers
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/70—Wind energy
    • Y02E10/72—Wind turbines with rotation axis in wind direction
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
    • Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E70/00—Other energy conversion or management systems reducing GHG emissions
    • Y02E70/30—Systems combining energy storage with energy generation of non-fossil origin
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/80—Management or planning
    • Y02P90/84—Greenhouse gas [GHG] management systems
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
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    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

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Abstract

The present invention provides carbon emission regulation systems, methods and media for a novel ORC-based system, the system comprising: the biomass boiler and the solar thermal collector are used as heat sources and connected with the evaporator, and the evaporator is sequentially connected with the expansion machine, the generator, the electric energy storage and the power grid to realize a power supply function; the expander is sequentially connected with the condenser, the liquid storage device, the working medium pump and the evaporator in a closed loop manner; the biomass boiler is connected with the first-stage flue gas-water heat exchanger, the absorption heat pump, the second-stage flue gas-water heat exchanger, the heat supply network return water and the absorption heat pump in sequence, and the waste heat type lithium bromide water chilling unit is connected with the absorption heat pump to realize the cold supply function; the solar heat collector is connected with the second-stage flue gas-water heat exchanger to realize the heat supply function. And the main energy consumed by each energy supply system is optimized by combining the improved particle swarm optimization, so that the carbon dioxide emission is reduced. The system and the method can effectively improve the energy utilization rate and the stability of the power generation efficiency, and have practical and feasible application prospects.

Description

ORC-based carbon emission regulation system, method and medium for novel system
Technical Field
The present invention relates to the field of spectral information processing, and in particular to carbon emission regulation systems, methods and media for novel systems based on ORC.
Background
With the continuous development of scientific technology, the energy consumption of China is increasing day by day, and the excessive use of fossil energy such as petroleum, coal, natural gas and the like faces the dilemma of energy shortage and environmental pollution. Therefore, the method has important significance for the research of clean energy or renewable energy, including biomass energy, solar energy, wind energy and other energy sources. Meanwhile, waste gas generated in the operation process is secondarily utilized, the energy utilization rate can be effectively improved, and the energy consumed by the system is optimized by utilizing an improved algorithm.
The traditional combined cooling heating and power system mainly uses fossil energy such as natural gas and the like as fuel, although the energy utilization rate is improved, the system is not beneficial to environmental indexes.
The traditional ORC circulating system mainly uses low-temperature waste heat as a heat source, but the low-temperature heat source has high instability and is difficult to ensure the efficient operation of the system.
Disclosure of Invention
The purpose of the invention is as follows: the invention aims to provide a carbon emission regulation and control system and a method of a novel ORC-based system, which can effectively improve the energy utilization rate, reduce the emission of carbon dioxide and improve the stability of the power generation efficiency.
The technical scheme is as follows: a carbon emissions regulation system for a novel ORC-based system, the system comprising: the biomass boiler and the solar thermal collector are used as heat sources and connected with the evaporator, the evaporator is sequentially connected with the expansion machine, the generator, the electric energy storage and the power grid, and the electric quantity generated by the generator is supplied to the power grid so as to realize the power supply function; the expander is sequentially connected with the condenser, the liquid storage device, the working medium pump and the evaporator in a closed loop manner; the biomass boiler is connected with the first-stage flue gas-water heat exchanger, the absorption heat pump, the second-stage flue gas-water heat exchanger, the heat supply network return water and the absorption heat pump in sequence, and the waste heat type lithium bromide water chilling unit is connected with the absorption heat pump to realize the cold supply function; the solar heat collector is connected with the second-stage flue gas-water heat exchanger to realize the heat supply function.
Furthermore, the wind power generation system is connected with the generator and used for increasing the rated rotating speed of the generator.
An improved particle swarm algorithm is combined to optimize the energy consumed by each energy supply system, and the improved particle swarm algorithm comprises the following steps:
step 1: initializing parameters, and inputting main energy parameters consumed by each energy supply system;
step 2: calculating the fitness value of each individual, and updating the global optimal position;
and step 3: entering a main loop, and updating the adjustment factor and the self-adaptive weight;
and 4, step 4: judging whether the individual fitness value is smaller than the population average fitness value, if so, performing cross operation according to a random proportion, otherwise, performing cross operation according to a certain rule as a proportion coefficient to update the optimal individual position;
and 5: randomly generating a pre-judgment variation probability, judging whether the variation probability is smaller than the pre-judgment variation probability, if so, performing variation operation, otherwise, entering the step 6;
step 6: forming a next generation population and updating the global optimal position;
and 7: judging whether the maximum iterative evolution times is reached, if not, returning to the step 3, otherwise, outputting the optimal energy solution consumed by each energy supply system;
and 8: and substituting the energy solution consumed by the optimal energy supply systems into the carbon emission index.
Further, step 3 specifically includes:
the adjustment factor Q is related by:
Figure BDA0003384679810000021
the adaptive weight W is given by:
Figure BDA0003384679810000022
wherein m is a random parameter, the numeric area is [0,2], the iteration time T is 1, and the maximum iteration evolution time is G.
Further, step 4 specifically includes:
the random cross relationship is:
Figure BDA0003384679810000023
the regular cross relation is:
Figure BDA0003384679810000024
ε＝fm/(fm+fm)
wherein the content of the first and second substances,
Figure BDA0003384679810000025
is an individual in the population of the human,
Figure BDA0003384679810000026
as another individual at random, fnIs composed of
Figure BDA0003384679810000027
Fitness value of the individual, fmIs composed of
Figure BDA0003384679810000028
The fitness value of an individual mu is a random parameter and the value range is [0, 1%]And epsilon is a proportionality coefficient for performing cross operation according to a certain rule.
Further, in step 5, the variation probability M is expressed as:
Figure BDA0003384679810000029
further, step 8 specifically includes:
the carbon emission index formula is:
Figure BDA00033846798100000210
Figure BDA00033846798100000211
in order to reduce the amount of carbon dioxide,
Figure BDA00033846798100000212
is the carbon dioxide emission coefficient, PabsFor reference to the main energy consumed by the system, PORCTo simulate the main energy consumed by the ORC system,
reference system consumption of main energy PabsComprises the following steps:
Pabs＝Ph+Pc+Pout
Phenergy consumed for heating systems, PcFor the energy consumed by the cooling system, PoutIn order to export the energy consumed by the power supply system from the grid,
simulating main energy P consumed by ORC systemORCComprises the following steps:
PORC＝Pout-Pin
wherein, PinThe energy consumed by the power supply system of the power grid is input.
Further, the air conditioner is provided with a fan,
Figure BDA0003384679810000031
the carbon dioxide emission coefficient is 0.39.
A computer-readable storage medium comprising one or more programs for execution by one or more processors, the one or more programs comprising instructions for performing the method of any of claims 3-7.
The working principle of the system is as follows: an ORC-based combined cooling, heating and power system for hybrid utilization of solar energy, wind energy and biomass energy comprises: the system comprises a biomass boiler, an evaporator, an expander, a generator, a condenser, a liquid storage device, a working medium pump, a wind generating set, a solar thermal collector, a second-stage flue gas-water heat exchanger, an absorption heat pump, an electric energy storage device, an electric network and a waste heat type lithium bromide water chilling unit. The biomass boiler and the solar heat collector are connected with the evaporator and serve as heat sources, and the heat energy obtained by absorbing the organic working medium in the evaporator is converted into high-temperature high-pressure steam. The evaporator, the expander and the generator are connected in sequence, and high-temperature and high-pressure steam discharged from the evaporator enters the expander to do work so as to drive the generator to generate electricity. The wind generating set is connected with the generator, the rated rotating speed of the generator is increased, and the generator is driven to generate electricity. Meanwhile, the generator, the electric energy storage and the power grid are sequentially connected, the electric quantity generated by the generator is mutually supplied to the power grid, when the power supply quantity of the generator is insufficient, the electric energy is supplied from the power grid, and when the power supply quantity of the generator is sufficient, the redundant electric quantity is stored in the electric energy storage and is supplied to the power grid. The expander is connected with the condenser, and the steam discharged by the expander and subjected to pressure reduction and temperature reduction is condensed by the condenser. The condenser, the liquid storage device and the working medium pump are sequentially connected, and the liquid working medium flowing out of the condenser enters the liquid storage device and then enters the working medium pump for pressurization. The working medium pump is connected with the evaporator, and the organic working medium pressurized by the working medium pump returns to the evaporator again.
The solar heat collector is connected with the second-stage flue gas-water heat exchanger in the flue gas waste heat recovery system, so that the heat supply function is realized.
The waste heat type lithium bromide water chilling unit is connected with an absorption heat pump in the flue gas waste heat recovery system to realize a cold supply function, so that the link of the ORC-based combined cooling heating and power system for mixed utilization of solar energy, wind energy and biomass energy is completed.
Has the advantages that: compared with the prior art, the invention has the following remarkable advantages:
(1) the invention mixes and utilizes solar energy and biomass energy, is more stable than only one low-temperature energy source as a heat source, ensures that the organic working medium is completely evaporated, does not have the phenomenon of coexistence of gas and liquid, and improves the working efficiency of the expansion machine;
(2) the system combines wind energy, is more effective than power generation only by one energy source, and ensures the power generation efficiency;
(3) waste heat recovery is carried out on waste gas generated by the biomass boiler, so that the energy utilization rate of the system is improved;
(4) the main energy parameters consumed by each energy supply system are optimized by combining the improved particle swarm optimization, so that the carbon dioxide emission can be reduced, and the environmental index is improved.
Drawings
FIG. 1 is a schematic diagram of the system of the present invention;
FIG. 2 is a flow chart of an optimization algorithm of the present invention;
FIG. 3 is a graph comparing the energy utilization of the present invention;
FIG. 4 is a graph comparing the power generation efficiency of the present invention;
FIG. 5 is a graph comparing carbon dioxide emissions according to the present invention.
Detailed Description
The technical scheme of the invention is further explained by combining the attached drawings.
As shown in fig. 1, an ORC-based combined cooling, heating and power system includes a biomass boiler 1, an evaporator 2, an expander 3, a generator 4, a wind generating set 5, an electric energy storage 6, a power grid 7, a condenser 8, a liquid storage 9, a working medium pump 10, a solar heat collector 11, a first-stage flue gas-water heat exchanger 12, an absorption heat pump 13, a second-stage flue gas-water heat exchanger 14, a heat grid backwater 15, and a waste heat type lithium bromide water chiller 16; the ORC-based combined cooling heating and power system and the flue gas waste heat recovery system which utilize solar energy, wind energy and biomass energy in a mixed mode are formed, and finally the main energy consumed by each energy supply system is optimized by combining the improved particle swarm optimization, so that the carbon dioxide emission is reduced.
In the combined cooling heating and power system based on ORC, which utilizes solar energy, wind energy and biomass energy in a mixed manner, a biomass boiler 1 and a solar heat collector 11 are used as heat sources, heat energy obtained by absorbing organic working media in an evaporator 2 is converted into high-temperature high-pressure steam, the high-temperature high-pressure steam discharged from the evaporator 2 enters an expander 3 to do work, so that a generator 4 is driven to generate electricity, and a wind generating set 5 is increased to the rated rotating speed of the generator 4 to drive the generator 4 to generate electricity. Meanwhile, the electric quantity generated by the generator 4 and the power grid 7 are mutually supplied, when the power supply quantity of the generator 4 is insufficient, the electric quantity is supplied from the power grid 7, and when the power supply quantity of the generator 4 is sufficient, the redundant electric quantity is stored in the electric energy storage 6 and is supplied to the power grid 7. The steam discharged by the expansion machine 3 after being decompressed and cooled is condensed by the condenser 8, and the liquid working medium flowing out of the condenser 8 enters the liquid storage device 9 and then enters the working medium pump 10 for pressurization. The organic working medium pressurized by the working medium pump 10 returns to the evaporator 2 again.
The solar heat collector 11 is connected with a second-stage flue gas-water heat exchanger 14 in the flue gas waste heat recovery system to realize the heat supply function.
In the flue gas waste heat recovery system, waste gas generated by the biomass boiler 1 passes through the first-stage flue gas-water heat exchanger 12 to obtain high-temperature hot water, the high-temperature hot water flowing out of the first-stage flue gas-water heat exchanger 12 drives the absorption heat pump 13 to work, the heat supply network backwater 15 absorbs heat by using the absorption heat pump 13 and then enters the second-stage flue gas-water heat exchanger 14 to be further heated, and therefore the link of the flue gas waste heat recovery system is completed. The waste heat type lithium bromide water chilling unit 16 is connected with the absorption heat pump 13 in the flue gas waste heat recovery system, so that the cooling function is realized. Therefore, the link of the ORC-based combined cooling heating and power system for hybrid utilization of solar energy, wind energy and biomass energy is completed.
As shown in fig. 2, the improved particle swarm optimization optimizes the main energy consumed by each energy supply system, and the improved particle swarm optimization comprises the following steps:
step 1: initializing parameters including random initialization of population size S, S value range of [20, 50%]Random position is Xi，XiThe value range is [0, S]Inputting main energy parameters consumed by each energy supply system, wherein the maximum iterative evolution time is G;
step 2: calculating the fitness value of each individual, updating the optimal position, and enabling the iteration time T to be 1;
and step 3: entering a main loop, updating the adjusting factor Q and the self-adaptive weight W,
the adjustment factor Q is related by:
Figure BDA0003384679810000051
the adaptive weight W is given by:
Figure BDA0003384679810000052
wherein m is a random parameter, and the value range of m is [0,2 ];
and 4, step 4: judging whether the individual fitness value F is smaller than the population average fitness value F (x), if the F is smaller than F (x), performing cross operation according to a random proportion, otherwise, performing cross operation as a proportion coefficient according to a certain rule to update the optimal individual position, wherein the random cross relation formula is as follows:
Figure BDA0003384679810000053
the regular cross relation is:
Figure BDA0003384679810000054
ε＝fm/(fn+fm)
wherein the content of the first and second substances,
Figure BDA0003384679810000055
is an individual in the population of the human,
Figure BDA0003384679810000056
as another individual at random, fnIs composed of
Figure BDA0003384679810000057
Fitness value of the individual, fmIs composed of
Figure BDA0003384679810000058
The fitness value of an individual mu is a random parameter and the value range is [0, 1%]And ε is the ratio of the crossing operations performed according to a certain ruleExample coefficients;
and 5: randomly generating pre-judging variation probability MsThe value range is (0, G), and whether the variation probability M is smaller than the pre-judged variation probability M is judgedsIf M is less than MsIf yes, performing mutation operation, otherwise, entering the step 6;
the variation probability relation is:
Figure BDA0003384679810000059
step 6: forming a next generation population and updating the optimal position;
and 7: judging whether the maximum iterative evolution times G are reached, if not, returning to the step 3, otherwise, outputting the optimal energy solution consumed by each energy supply system;
and 8: substituting the energy solution consumed by each optimal energy supply system into a carbon emission index formula,
the carbon emission index formula is:
Figure BDA00033846798100000510
Figure BDA0003384679810000061
in order to reduce the amount of carbon dioxide,
Figure BDA0003384679810000062
the carbon dioxide emission coefficient is 0.39, PabsFor reference to the main energy consumed by the system, PORCTo simulate the main energy consumed by the ORC system,
reference system consumption of main energy PabsComprises the following steps:
Pabs＝Ph+Pc+Pout
Phenergy consumed for heating systems, PcFor the energy consumed by the cooling system, PoutIn order to export the energy consumed by the power supply system from the grid,
simulating main energy P consumed by ORC systemORCComprises the following steps:
PORC＝Pout-Pin
wherein, PinThe energy consumed by the power supply system of the power grid is input.
As shown in fig. 3, comparing the combined cooling heating and power system based on ORC with the conventional combined cooling heating and power system and the conventional ORC system, it is found that the energy utilization rate of the present invention is significantly higher than that of the other two energy systems.
As shown in fig. 4, comparing the combined cooling heating and power system based on the ORC with the conventional combined cooling heating and power system and the conventional ORC system, the present invention significantly improves the power generation efficiency of the system.
Comparing the system using the improved particle swarm algorithm with the system without the algorithm, as shown in fig. 5, it is found that the carbon dioxide emission is obviously reduced after the algorithm is used for optimization.
Embodiments of the present invention may be provided as methods or computer program products and, thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The embodiments of the present invention are not described in detail, but are known in the art, and can be implemented by referring to the known techniques.

Claims (9)

1. A carbon emissions regulation system for a novel ORC-based system, the system comprising: the biomass boiler (1) and the solar heat collector (11) are used as heat sources to be connected with the evaporator (2), the evaporator (2) is sequentially connected with the expansion machine (3), the generator (4), the electric energy storage (6) and the power grid (7), and the electric quantity generated by the generator (4) is mutually supplemented with the power grid (7) to realize the power supply function; the expansion machine (3) is sequentially connected with the condenser (8), the liquid storage device (9), the working medium pump (10) and the evaporator (2) in a closed loop manner; the biomass boiler (1) is sequentially connected with the first-stage flue gas-water heat exchanger (12), the absorption heat pump (13), the second-stage flue gas-water heat exchanger (14), the heat supply network backwater (15) and the absorption heat pump (13), and the waste heat type lithium bromide water chilling unit (16) is connected with the absorption heat pump (13) to realize the cold supply function; the solar heat collector (11) is connected with the second-stage flue gas-water heat exchanger (14) to realize the heat supply function.
2. The carbon emission control system of the novel ORC-based system according to claim 1, further comprising a wind turbine generator (5) connected to the generator (4), the wind turbine generator (5) being configured to increase a rated rotational speed of the generator (4).
3. The method for controlling carbon emissions from a novel ORC-based system according to claim 1, wherein the method is combined with a modified particle swarm algorithm to optimize the energy consumed by each energy supply system, the modified particle swarm algorithm comprising the steps of:
step 1: initializing parameters, and inputting main energy parameters consumed by each energy supply system;
step 2: calculating the fitness value of each individual, and updating the global optimal position;
and step 3: entering a main loop, and updating the adjustment factor and the self-adaptive weight;
and 4, step 4: judging whether the individual fitness value is smaller than the population average fitness value, if so, performing cross operation according to a random proportion, otherwise, performing cross operation according to a certain rule as a proportion coefficient to update the optimal individual position;
and 5: randomly generating a pre-judgment variation probability, judging whether the variation probability is smaller than the pre-judgment variation probability, if so, performing variation operation, otherwise, entering the step 6;
step 6: forming a next generation population and updating the global optimal position;
and 7: judging whether the maximum iterative evolution times is reached, if not, returning to the step 3, otherwise, outputting the optimal energy solution consumed by each energy supply system;
and 8: and substituting the energy solution consumed by the optimal energy supply systems into the carbon emission index.
4. The method for carbon emission control of a novel ORC based system according to claim 3, wherein said step 3 specifically comprises:
the adjustment factor Q is related by:
Figure FDA0003384679800000011
the adaptive weight W is given by:
Figure FDA0003384679800000012
wherein m is a random parameter, the numeric area is [0,2], the iteration time T is 1, and the maximum iteration evolution time is G.
5. The method for carbon emission control of a novel ORC based system according to claim 3, wherein said step 4 specifically comprises:
the random cross relationship is:
Figure FDA0003384679800000021
the regular cross relation is:
Figure FDA0003384679800000022
ε＝fm/(fn+fm)
wherein the content of the first and second substances,
Figure FDA0003384679800000023
is an individual in the population of the human,
Figure FDA0003384679800000024
as another individual at random, fnIs composed of
Figure FDA0003384679800000025
Fitness value of the individual, fmIs composed of
Figure FDA0003384679800000026
The fitness value of an individual mu is a random parameter and the value range is [0, 1%]And epsilon is a proportionality coefficient for performing cross operation according to a certain rule.
6. The method for carbon emission control of a novel ORC based system according to claim 3, wherein said variation probability M in step 5 is related by:
Figure FDA0003384679800000027
7. the method for carbon emission control of a novel ORC-based system according to claim 3, wherein said step 8 comprises in particular:
the carbon emission index formula is:
Figure FDA0003384679800000028
Figure FDA0003384679800000029
in order to reduce the amount of carbon dioxide,
Figure FDA00033846798000000210
is the carbon dioxide emission coefficient, PabsFor reference to the main energy consumed by the system, PORCTo simulate the main energy consumed by the ORC system,
reference system consumption of main energy PabsComprises the following steps:
Pabs＝Ph+Pc+Pout
Phenergy consumed for heating systems, PcFor the energy consumed by the cooling system, PoutIn order to export the energy consumed by the power supply system from the grid,
simulating main energy P consumed by ORC systemORCComprises the following steps:
PORC＝Pout-Pin
wherein, PinThe energy consumed by the power supply system of the power grid is input.
8. The method for carbon emissions control of a novel ORC based system according to claim 7, wherein said method is characterized by
Figure FDA00033846798000000211
The carbon dioxide emission coefficient is 0.39.
9. A computer readable storage medium comprising one or more programs for execution by one or more processors, the one or more programs comprising instructions for performing the method of any of claims 3-7.
CN202111448414.7A 2021-11-30 2021-11-30 ORC-based carbon emission regulation system, method and medium for novel system Pending CN114294630A (en)

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CN113485489A (en) * 2021-06-18 2021-10-08 淮阴工学院 Method for regulating and controlling outlet temperature of evaporator of ORC system
CN113541598A (en) * 2021-06-16 2021-10-22 淮阴工学院 Multi-stage utilization type cooling, heating and power energy supply system and system capacity configuration optimization method thereof
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CN202675723U (en) * 2012-06-28 2013-01-16 上海康诺能源技术有限公司 Energy-saving composite driven lithium bromide absorption-type air source heat pump
CN103762589A (en) * 2014-01-08 2014-04-30 河海大学 Method for optimizing new energy capacity ratio in layers in power grid
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