CN116228000A - Green certificate distribution method based on comprehensive energy system carbon emission degree rating - Google Patents
Green certificate distribution method based on comprehensive energy system carbon emission degree rating Download PDFInfo
- Publication number
- CN116228000A CN116228000A CN202310095792.4A CN202310095792A CN116228000A CN 116228000 A CN116228000 A CN 116228000A CN 202310095792 A CN202310095792 A CN 202310095792A CN 116228000 A CN116228000 A CN 116228000A
- Authority
- CN
- China
- Prior art keywords
- carbon emission
- node
- unit
- power
- formula
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 229910052799 carbon Inorganic materials 0.000 title claims abstract description 206
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 title claims abstract description 201
- 238000000034 method Methods 0.000 title claims abstract description 23
- 238000005192 partition Methods 0.000 claims abstract description 28
- 230000009467 reduction Effects 0.000 claims abstract description 16
- 238000011156 evaluation Methods 0.000 claims abstract description 7
- 238000010248 power generation Methods 0.000 claims description 50
- 239000011159 matrix material Substances 0.000 claims description 36
- 239000007789 gas Substances 0.000 claims description 33
- 239000002028 Biomass Substances 0.000 claims description 27
- 230000008878 coupling Effects 0.000 claims description 15
- 238000010168 coupling process Methods 0.000 claims description 15
- 238000005859 coupling reaction Methods 0.000 claims description 15
- 230000009194 climbing Effects 0.000 claims description 12
- 230000004907 flux Effects 0.000 claims description 12
- 238000005286 illumination Methods 0.000 claims description 12
- VNWKTOKETHGBQD-UHFFFAOYSA-N methane Chemical compound C VNWKTOKETHGBQD-UHFFFAOYSA-N 0.000 claims description 12
- 238000002347 injection Methods 0.000 claims description 9
- 239000007924 injection Substances 0.000 claims description 9
- 230000005611 electricity Effects 0.000 claims description 7
- 238000010521 absorption reaction Methods 0.000 claims description 6
- 238000010438 heat treatment Methods 0.000 claims description 6
- 238000012544 monitoring process Methods 0.000 claims description 6
- 239000003345 natural gas Substances 0.000 claims description 6
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 6
- 238000005485 electric heating Methods 0.000 claims description 4
- 238000005516 engineering process Methods 0.000 claims description 4
- NAWXUBYGYWOOIX-SFHVURJKSA-N (2s)-2-[[4-[2-(2,4-diaminoquinazolin-6-yl)ethyl]benzoyl]amino]-4-methylidenepentanedioic acid Chemical compound C1=CC2=NC(N)=NC(N)=C2C=C1CCC1=CC=C(C(=O)N[C@@H](CC(=C)C(O)=O)C(O)=O)C=C1 NAWXUBYGYWOOIX-SFHVURJKSA-N 0.000 claims description 3
- RZGZMLICFFEUIQ-UHFFFAOYSA-N 5-[(1-phenylcyclohexyl)amino]pentanoic acid Chemical compound C=1C=CC=CC=1C1(NCCCCC(=O)O)CCCCC1 RZGZMLICFFEUIQ-UHFFFAOYSA-N 0.000 claims description 3
- 238000012935 Averaging Methods 0.000 claims description 3
- 238000004364 calculation method Methods 0.000 claims description 3
- 239000003245 coal Substances 0.000 claims description 3
- 239000000446 fuel Substances 0.000 claims description 3
- 238000002309 gasification Methods 0.000 claims description 3
- 239000003550 marker Substances 0.000 claims description 3
- 238000005259 measurement Methods 0.000 claims description 3
- 238000011084 recovery Methods 0.000 claims description 3
- 238000005057 refrigeration Methods 0.000 claims description 3
- 230000011218 segmentation Effects 0.000 claims description 3
- 238000004088 simulation Methods 0.000 claims description 3
- 230000003746 surface roughness Effects 0.000 claims description 3
- 238000012360 testing method Methods 0.000 claims description 3
- 239000002918 waste heat Substances 0.000 claims description 3
- 230000001105 regulatory effect Effects 0.000 abstract description 2
- 230000009466 transformation Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- 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
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- 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
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
-
- 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
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- General Physics & Mathematics (AREA)
- Development Economics (AREA)
- Health & Medical Sciences (AREA)
- Educational Administration (AREA)
- Marketing (AREA)
- Entrepreneurship & Innovation (AREA)
- Theoretical Computer Science (AREA)
- Tourism & Hospitality (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Game Theory and Decision Science (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a green certificate distribution method based on comprehensive energy system carbon emission degree rating; the invention improves the grade assessment of the carbon emission degree of the user, can lead the user to have clear knowledge on the carbon emission condition of the user, and clear the emission reduction responsibility of the user, thereby actively guiding the user of the high emission partition and the ultra-high emission partition to purchase green responsibility certificates and reducing the equivalent carbon emission degree, and defines the carbon emission degree of the user as an evaluation index after comprehensively considering the carbon potential and the carbon emission amount. The standard value of the carbon emission degree is regulated according to the average carbon emission degree of the system and a local government emission reduction plan, and the carbon emission degree level of the power user is defined according to the difference degree between the average carbon emission degree of the user and the standard value to construct a green certificate distribution model, so that the distribution problem of the green power certificate at the user side is solved, and the user is guided to actively take charge of carbon emission reduction responsibility.
Description
Technical Field
The invention relates to the technical field of low-carbon economic operation of a comprehensive energy system, in particular to a green certificate distribution method based on carbon emission degree rating of the comprehensive energy system.
Background
Along with the increase of extreme weather, the reduction of carbon emission has become the consensus of countries around the world, the low-carbon transformation of the power system has become an important component of the low-carbon strategy of China, and the construction of a novel power system taking new energy as a main body plays a key role; the low-carbon power technology has become a source power for leading the transformation and the realization of low-carbon innovation development in the power energy industry, and although almost all carbon emission of a power system comes from a power generation side, the power system is a source follow-up system, and the energy utilization behavior of a user can obviously influence the dispatching result of the power system and further influence the carbon emission condition of the power system. The electricity consumption carbon emission factor is a key signal that transfers the electrical system carbon emission responsibility from the source side to the load side.
At present, the carbon emission degree of a user is not accurately rated, so that the user does not have clear knowledge on the carbon emission condition of the user, and the emission reduction responsibility of the user is not clear, so that the equivalent carbon emission degree cannot be effectively reduced, the control of the carbon emission is not facilitated, and the rating of the carbon emission degree of the user is not realized; therefore, there is a need to design a green certificate distribution method based on the carbon emission level rating of the integrated energy system.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a green certificate distribution method based on the carbon emission degree rating of a comprehensive energy system for better and effective solving of the problems, the method has the advantages of dividing 5 grades, namely green environment-friendly subareas, low-emission subareas, standard-emission subareas, high-emission subareas and ultra-high-emission subareas, respectively, carrying out grade assessment on the carbon emission degree of users, enabling the users to clearly know the carbon emission condition of the users and clearly know the emission reduction responsibility of the users, and actively guiding the users of the high-emission subareas and the ultra-high-emission subareas to purchase green responsibility certificates and reducing the equivalent carbon emission degree.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows:
the green certificate distribution method based on the carbon emission degree rating of the comprehensive energy system comprises the following steps,
step (A), a generator set model of the comprehensive energy system is established, the power generation power and the power generation cost of each generator set are obtained according to the established generator set model of the comprehensive energy system,
step (B), constructing an optimal output model of the generator set, so that the system can optimally output under the minimum power generation cost;
step (C), establishing a carbon flow tracking model to obtain node carbon potential and carbon emission of each node of the system;
step (D), establishing an electrothermal coupling network carbon emission flow model;
step (E), establishing a carbon emission degree rating model, formulating rating standards, and rating the carbon emission of the user;
and (F) establishing an allocation model of the green responsibility allocation certificate.
Preferably, step (A) is to construct a generator set model of the integrated energy system, wherein the generator set model comprises a wind generator set output model, a photovoltaic generator set output model, a biomass energy generator set output model, a thermal power generator set output model and a CHP generator set output model, and the specific steps are as follows,
step (A1), constructing a wind turbine output model, wherein the wind turbine output model is closely related to site conditions, and the actual wind speed at the hub is different from the high-degree wind speed of the monitoring point, specifically comprising the following steps of,
step (A11), converting the measured wind speed, as shown in formula (1),
wherein ,vref And v (k) are the measured wind speed of the monitoring point at the kth moment and the wind speed at the hub, H and H respectively ref The height of the hub and the height of the actual measurement point are respectively, and alpha is a surface roughness degree description factor;
step (A12), modeling the relation between the power output and the wind speed of the wind turbine by adopting a piecewise function, as shown in a formula (2),
wherein ,Prated V is the rated output power of the wind turbine generator min V is the minimum starting wind speed of the wind turbine generator max To cut off wind speed v rated The minimum wind speed required by rated power output of the wind turbine generator is set;
step (A13), calculating the power generation cost C of the wind turbine generator system WT As shown in the formula (3),
C WT ＝a wt ×P WT (3)
wherein ,awt Hair for wind turbine generatorAn electrical cost coefficient;
step (A2), constructing a photovoltaic unit output model, wherein the photovoltaic unit output model is formed by converting a photovoltaic cell into an equivalent circuit model, researching UI characteristics of the photovoltaic cell, specifically comprising the following steps of,
step (A21), constructing an accurate simulation model of the series-parallel resistor, and the output power of the photovoltaic array is related to the illumination intensity, the temperature and the standard test condition, wherein the output power of the photovoltaic cell is shown as a formula (4),
wherein ,PPV G is the output power of the photovoltaic cell when the illumination intensity is G stc Is the illumination intensity under STC working condition, T STC Is the surface temperature of the photovoltaic cell under STC working condition, P stc The maximum output power under STC working conditions is G, the illumination intensity is G, k is a power temperature coefficient, and T is the surface temperature of the photovoltaic cell;
step (A22), calculating the power generation cost C of the photovoltaic unit PV As shown in the formula (5),
C PV ＝a pv ×P PV (5)
wherein ,apv The power generation cost coefficient of the wind turbine generator is set;
step (A3), constructing an output model of the thermal power generating unit, wherein the thermal power generating unit heats water in a boiler by using coal, oil and combustible gas as fuels, heats the water, and then generates electricity by using steam to push a gas wheel,
step (A31), calculating the power supply E of the thermal power generator HOT (t) as shown in the formula (6),
E HOT (t)＝P HOT (t)△t (6)
wherein ,PHOT The power generation power of the thermal power generating unit;
step (A32), calculating the power generation cost C of the thermal power generating unit HOT As shown in the formula (7),
wherein ,ai 、b i and ci The power generation cost coefficient of the thermal power generating unit;
step (A4), constructing a biomass energy unit output model, wherein the biomass energy unit is converted into electric energy through gasification and storage and burning in a gas generator, and the specific steps are as follows,
step (A41), calculating the power supply E of the gas generator BIO (t) as shown in the formula (8),
E BIO (t)＝P BIO (t)△t (8)
wherein ,PBIO Generating power for the biomass energy unit;
step (A42), calculating the power generation cost C of the biomass energy unit BIO As shown in the formula (9),
wherein ,abg 、b bg and cbg The power generation cost coefficient of the biomass energy unit;
step (A5), a CHP unit output model is built, wherein the CHP unit is coupling equipment in an electrothermal network and can simultaneously generate electric energy and heat; the CHP unit consumes natural gas, and generates electricity through the gas generator directly, and the heat is utilized by the absorption heat pump and refrigeration technology, the specific steps are as follows,
step (A51), calculating the power supply and the heat supply of the CHP unit, wherein the power supply and the heat supply are respectively shown in a formula (10) and a formula (11),
P E ＝Qη e (10)
φ H ＝Qη h λ a (11)
wherein Q is consumption of primary natural gas energy of the CHP unit, and P E and φH Respectively the power supply quantity and the heat supply quantity of the CHP unit, eta e Is the power generation efficiency eta of the prime motor h Lambda is the efficiency of the waste heat recovery device a Is the coefficient of performance of the absorption heat pump;
step (A51), calculating the power generation cost of the CHP set, as shown in a formula (12),
wherein g is the number of the cogeneration unit, P E Power supply for cogeneration, phi E A, heat supply power for cogeneration g 、b g and cg Power cost coefficient, sigma, for cogeneration CHP C is the reduction value of electric power when extracting unit steam quantity under fixed steam inlet quantity CHP And the energy supply cost of the cogeneration unit is reduced.
Preferably, in the step (B), a generator set model of the comprehensive energy system is built, and the specific steps are as follows,
step (B1), on the basis of the completion of the establishment of the generator set model, establishing the optimal output of the set with the minimum generating cost as the target, wherein the objective function of the optimal output of the comprehensive energy system is shown as a formula (13):
wherein ,F1 The power generation cost of the system; n is n 1 、n 2 、n 3 、n 4 、n 5 and n6 Respectively the total number of a wind turbine generator system, a photovoltaic turbine generator system, a thermal power generation unit, a biomass energy unit, a CHP unit and a gas turbine generator system, C WT,i 、C PV,i 、C HOT,i 、C BIO,i 、C MT,i and CCHP,i The method is characterized by comprising the following steps of generating cost in t time periods of a wind turbine generator, a photovoltaic turbine generator, a thermal power generating unit, a biomass energy unit, a gas turbine generator and a CHP (gas turbine generator) unit respectively, wherein the constraint conditions comprise the following specific steps:
step (B2), load balancing constraints, as shown in equation (14),
wherein ,Plt An electrical load demand for period t; p (P) WT,it 、P PV,it 、P HOT,it 、P BIO,it 、P MT,it and PCHP,it The power generation system comprises power generation powers of a wind turbine generator system, a photovoltaic turbine generator system, a thermal power generating unit, a biomass energy unit and a CHP unit;
step (B3), thermal power generating unit constraint, as shown in formula (15),
wherein ,
and />
Maximum output and minimum output limit values of the unit i are respectively; />
and />
The maximum downward climbing rate and the maximum upward climbing rate of the unit i are respectively;
step (B4), the output constraint of the wind turbine generator is shown as a formula (16),
wherein ,
the predicted value of the wind turbine generator in the period t;
step (B5), the output constraint of the photovoltaic unit is shown as a formula (17),
wherein ,
the predicted value of the wind turbine generator in the period t;
step (B6), BIO unit output constraint, as shown in formula (18),
wherein ,
and />
Maximum output and minimum output limit values of the unit i are respectively;
step (B7), the gas unit output constraint is as shown in a formula (19),
wherein ,
and />
The upper limit and the lower limit of the output of the gas unit i are respectively; />
and />
The maximum downward climbing rate and the maximum upward climbing rate of the gas unit i are respectively;
step (B8), CHP unit output constraint, as shown in formula (20),
in the formula ,
the upper limit of the output of the CHP unit in the period t is set;
step (B9), direct current power flow constraint, as shown in formula (21),
wherein ,Pij,t Representing the active power flow, θ, between node i and node j i,t and θj,t Node voltage phase angles, x, respectively representing node i and node j ij Representing the reactance of the line i-j,
represents the upper capacity limit of line i-j, < >>
Represents the maximum value of the voltage phase angle of the node i, theta ref To balance node phase angles.
Preferably, step (C), the specific steps are as follows, the carbon flow tracking model is established to obtain the node carbon potential and the carbon emission amount of each node of the system, wherein the carbon emission flow is the relation between the carbon emission flow index and the energy flow in the electrothermal coupling network, the carbon emission flow index comprises the branch carbon emission flow F, the branch carbon emission flow rate R, the branch carbon emission flow density ρ and the node carbon potential E, the specific steps are as follows,
step (C1), unit injection distribution matrix is used for describing the connection relation between all generator units and a power system and the active power injected into the system by the unit, and is also used for describing the boundary condition of carbon emission flow generated by the generator units in the system;
step (C11), wherein the kth genset access nodej, and the active power flow injected into the node j from the kth node containing the generator is P, then P Gkj P, otherwise P Gkj ＝0；
Step (C12), for the comprehensive energy system, not only an electric network but also a thermal network are provided, the unit injection distribution matrix of the comprehensive energy system can be expanded into an energy matrix of an energy supply node injection network, and P is used for respectively G 、φ B A representation;
step (C2), load distribution matrix, using P L And the connection relation between all the electric loads and the electric power system and the active load quantity are described, so that the boundary condition of the consumption carbon emission flow of the electric power consumer in the system is described.
Step (C21), wherein node j is the mth node with load, and the active load is P, then P Lmj P, otherwise P Lmj ＝0；
Step (C22), for the comprehensive energy system, the node load matrix of the electrothermal network has both electric load and thermal load by P respectively L 、φ L A representation;
step (C3), node active flux matrix, using P N And (3) representing. In the calculation of carbon flow, this concept will be used to describe the contribution of the genset to the node and the node to node carbon potential in the system. The elements of the node active flux matrix are specifically defined as follows.
For node I, let I + Representing a set of branches with a flow of current into node i, p Bs The active power for branch s, as shown in equation (22),
wherein ,pGi For the generator set output of the access node i, if the node has no generator set or the generator set output is 0, p Gi =0, all off-diagonal elements P in the matrix Nij =0. For the comprehensive energy system, the energy flux matrix of the nodes is respectively P N 、φ N And (3) representing.
Preferably, step (D), establishing an electrothermal coupling network carbon emission flow model, wherein nodes are divided into two types, namely an energy supply node and an energy utilization node,
step (D1), the position and carbon emission intensity of the energy supply node are known, K generators exist in the power network, K heating devices exist in the heating network, the carbon potential of the node is calculated, as shown in a formula (23),
step (D11), the node carbon potential of the electrothermal coupling network is respectively shown in formula (24),
wherein ,PL 、φ L Node load matrix, P of electrothermal network G 、φ B Injecting energy matrix P of network into energy supply nodes respectively e 、φ h Respectively an energy flow distribution matrix, P N 、φ N Respectively node energy flux matrixes;
step (D111), the node carbon emission is shown in formula (25),
wherein ,ReL 、R hL And the carbon emission of the nodes of the electric heating network respectively.
Preferably, step (E), a carbon emission degree rating model is established, rating standards are formulated, the carbon emission of the user is rated, the average carbon potential and the total carbon emission of the nodes in the scheduling time are taken to carry out comprehensive evaluation according to a carbon flow tracking model, the specific steps are as follows,
step (E1), the carbon potential and the carbon emission amount are different in dimension, and the carbon potential and the carbon emission amount are subjected to a averaging treatment, as shown in a formula (26),
wherein f represents the degree of carbon emission; n is the number of system user nodes; w1 and w2 are the weights of the two evaluation components, w1+w2=1, respectively; r is R eLi and RhLi The carbon emission of the electric and thermal network of the load node i is respectively; r is R av and Eav The carbon emission and average carbon potential of the load node i are respectively; r is R av and Eav The average carbon emission and the carbon potential of the system are respectively; p (P) Li and φLi The electric load and the thermal load of the load node i are respectively;
step (E11), defining a carbon emission level marker value K as shown in a formula (27),
K＝[w 1 (R av /R av )+w 2 (E av /E av )]×(1-β)＝1-β (27)
wherein, beta is the emission reduction coefficient of the system, and the value of beta is 0.05.
Preferably, step (F), the carbon emission degree of the load node is used as a basis, and an allocation model of a green responsibility allocation certificate is established; the carbon emission degree standard pole value is divided into a green environment-friendly partition, a low emission partition, a standard emission partition, a high emission partition and an ultra-high emission partition, and the specific steps are as follows,
step (F1), constructing a minimum difference between users with different carbon emission levels as an objective function of the model, as shown in a formula (28),
f′ i ＝w 1 [(R i -G ci E av )/R av ]+w 2 [(R i -G ci E av )/P i /△t/E av ] (28)
wherein ,F2 Representing the difference of carbon emission degrees of users in the whole network; f's' i The carbon emission degree after green responsibility certification is distributed for the user i; mu is the average carbon emission degree of the whole network after the green responsibility certificate is distributed; g ci The number of green liability certificates allocated for user i; g p The total amount of green liability certificates to be distributed;
step (F11), constructing constraint conditions as the quantity constraint of certificate allocation, as shown in a formula (29),
G ci ≥0
step (F2), the green environment-friendly partition rating standard is shown in a formula (30),
f＜(1-2α)K (30)
step (F3), the low emission zone rating criteria is as shown in equation (31),
(1-2α)K≤f≤(1-α)K (31)
step (F4), standard emissions partition rating criteria are shown in equation (32),
(1-α)K＜f≤(1+α)K (32)
step (F5), the high emission partition rating criteria is shown in equation (33),
(1+α)K＜f≤(1+2α)K (33)
step (F6), the rating standard of the extra-high emission subarea is shown as a formula (34),
f＞(1+2α)K (34)
where α is the rating standard segmentation factor.
The beneficial effects of the invention are as follows: the green certificate distribution method based on the comprehensive energy system carbon emission degree rating improves the grade assessment of the carbon emission degree of the user, can enable the user to clearly know the carbon emission condition of the user and clearly determine the emission reduction responsibility of the user, thereby actively guiding the user of the high emission partition and the ultra-high emission partition to purchase the green responsibility certificate and reducing the equivalent carbon emission degree, and defines the carbon emission degree of the user as an evaluation index after comprehensively considering the carbon potential and the carbon emission amount. The standard value of the carbon emission degree is regulated according to the average carbon emission degree of the system and a local government emission reduction plan, and the carbon emission degree level of the power user is defined according to the difference degree between the average carbon emission degree of the user and the standard value to construct a green certificate distribution model, so that the distribution problem of the green power certificate at the user side is solved, and the user is guided to actively take charge of carbon emission reduction responsibility.
Drawings
FIG. 1 is a flow chart of a green certificate distribution method based on integrated energy system carbon emission rating of the present invention;
FIG. 2 is a 24-period unit output schematic diagram of the present invention;
FIG. 3 is a graph showing the comparison of the carbon emission levels of nodes before and after the distribution of certificates according to the present invention.
Detailed Description
The invention will be further described with reference to the drawings.
As shown in fig. 1, the green certificate allocation method based on the carbon emission degree rating of the integrated energy system of the present invention, comprises the steps of,
step (A), a generator set model of the comprehensive energy system is established, the power generation power and the power generation cost of each generator set are obtained according to the established generator set model of the comprehensive energy system,
step (B), constructing an optimal output model of the generator set, so that the system can optimally output under the minimum power generation cost;
step (C), establishing a carbon flow tracking model to obtain node carbon potential and carbon emission of each node of the system;
step (D), establishing an electrothermal coupling network carbon emission flow model;
step (E), establishing a carbon emission degree rating model, formulating rating standards, and rating the carbon emission of the user;
and (F) establishing an allocation model of the green responsibility allocation certificate.
Preferably, step (A) is to construct a generator set model of the integrated energy system, wherein the generator set model comprises a wind generator set output model, a photovoltaic generator set output model, a biomass energy generator set output model, a thermal power generator set output model and a CHP generator set output model, and the specific steps are as follows,
step (A1), constructing a wind turbine output model, wherein the wind turbine output model is closely related to site conditions, and the actual wind speed at the hub is different from the high-degree wind speed of the monitoring point, specifically comprising the following steps of,
step (A11), converting the measured wind speed, as shown in formula (1),
wherein ,vref And v (k) are the measured wind speed of the monitoring point at the kth moment and the wind speed at the hub, H and H respectively ref The height of the hub and the height of the actual measurement point are respectively, and alpha is a surface roughness degree description factor;
step (A12), modeling the relation between the power output and the wind speed of the wind turbine by adopting a piecewise function, as shown in a formula (2),
wherein ,Prated V is the rated output power of the wind turbine generator min V is the minimum starting wind speed of the wind turbine generator max To cut off wind speed v rated The minimum wind speed required by rated power output of the wind turbine generator is set;
step (A13), calculating the power generation cost C of the wind turbine generator system WT As shown in the formula (3),
C WT ＝a wt ×P WT (3)
wherein ,awt The power generation cost coefficient of the wind turbine generator is set;
step (A2), constructing a photovoltaic unit output model, wherein the photovoltaic unit output model is formed by converting a photovoltaic cell into an equivalent circuit model, researching UI characteristics of the photovoltaic cell, specifically comprising the following steps of,
step (A21), constructing an accurate simulation model of the series-parallel resistor, and the output power of the photovoltaic array is related to the illumination intensity, the temperature and the standard test condition, wherein the output power of the photovoltaic cell is shown as a formula (4),
wherein ,PPV G is the output power of the photovoltaic cell when the illumination intensity is G stc Is the illumination intensity under STC working condition, T STC Is the surface temperature of the photovoltaic cell under STC working condition, P stc The maximum output power under STC working conditions is G, the illumination intensity is G, k is a power temperature coefficient, and T is the surface temperature of the photovoltaic cell;
step (A22), calculating the power generation cost C of the photovoltaic unit PV As shown in the formula (5),
C PV ＝a pv ×P PV (5)
wherein ,apv The power generation cost coefficient of the wind turbine generator is set;
step (A3), constructing an output model of the thermal power generating unit, wherein the thermal power generating unit heats water in a boiler by using coal, oil and combustible gas as fuels, heats the water, and then generates electricity by using steam to push a gas wheel,
step (A31), calculating the power supply E of the thermal power generator HOT (t) as shown in the formula (6),
E HOT (t)＝P HOT (t)△t(6)
wherein ,PHOT The power generation power of the thermal power generating unit;
step (A32), calculating the power generation cost C of the thermal power generating unit HOT As shown in the formula (7),
wherein ,ai 、b i and ci The power generation cost coefficient of the thermal power generating unit;
step (A4), constructing a biomass energy unit output model, wherein the biomass energy unit is converted into electric energy through gasification and storage and burning in a gas generator, and the specific steps are as follows,
step (A41), calculating the power supply E of the gas generator BIO (t) as shown in the formula (8),
E BIO (t)＝P BIO (t)△t(8)
wherein ,PBIO Generating power for the biomass energy unit;
step (A42), calculating the power generation cost C of the biomass energy unit BIO As shown in the formula (9),
wherein ,abg 、b bg and cbg The power generation cost coefficient of the biomass energy unit;
step (A5), a CHP unit output model is built, wherein the CHP unit is coupling equipment in an electrothermal network and can simultaneously generate electric energy and heat; the CHP unit consumes natural gas, and generates electricity through the gas generator directly, and the heat is utilized by the absorption heat pump and refrigeration technology, the specific steps are as follows,
step (A51), calculating the power supply and the heat supply of the CHP unit, wherein the power supply and the heat supply are respectively shown in a formula (10) and a formula (11),
P E ＝Qη e (10)
φ H ＝Qη h λ a (11)
wherein Q is consumption of primary natural gas energy of the CHP unit, and P E and φH Respectively the power supply quantity and the heat supply quantity of the CHP unit, eta e Is the power generation efficiency eta of the prime motor h Lambda is the efficiency of the waste heat recovery device a Is the coefficient of performance of the absorption heat pump;
step (A51), calculating the power generation cost of the CHP set, as shown in a formula (12),
wherein g is a cogeneration machineNumbering of groups, P E Power supply for cogeneration, phi E A, heat supply power for cogeneration g 、b g and cg Power cost coefficient, sigma, for cogeneration CHP C is the reduction value of electric power when extracting unit steam quantity under fixed steam inlet quantity CHP And the energy supply cost of the cogeneration unit is reduced.
Preferably, in the step (B), a generator set model of the comprehensive energy system is built, and the specific steps are as follows,
step (B1), on the basis of the completion of the establishment of the generator set model, establishing the optimal output of the set with the minimum generating cost as the target, wherein the objective function of the optimal output of the comprehensive energy system is shown as a formula (13):
wherein ,F1 The power generation cost of the system; n is n 1 、n 2 、n 3 、n 4 、n 5 and n6 Respectively the total number of a wind turbine generator system, a photovoltaic turbine generator system, a thermal power generation unit, a biomass energy unit, a CHP unit and a gas turbine generator system, C WT,i 、C PV,i 、C HOT,i 、C BIO,i 、C MT,i and CCHP,i The method is characterized by comprising the following steps of generating cost in t time periods of a wind turbine generator, a photovoltaic turbine generator, a thermal power generating unit, a biomass energy unit, a gas turbine generator and a CHP (gas turbine generator) unit respectively, wherein the constraint conditions comprise the following specific steps:
step (B2), load balancing constraints, as shown in equation (14),
wherein ,Plt An electrical load demand for period t; p (P) WT,it 、P PV,it 、P HOT,it 、P BIO,it 、P MT,it and PCHP,it The power generation system comprises power generation powers of a wind turbine generator system, a photovoltaic turbine generator system, a thermal power generating unit, a biomass energy unit and a CHP unit;
step (B3), thermal power generating unit constraint, as shown in formula (15),
wherein ,
and />
Maximum output and minimum output limit values of the unit i are respectively; />
and />
The maximum downward climbing rate and the maximum upward climbing rate of the unit i are respectively;
step (B4), the output constraint of the wind turbine generator is shown as a formula (16),
wherein ,
the predicted value of the wind turbine generator in the period t;
step (B5), the output constraint of the photovoltaic unit is shown as a formula (17),
wherein ,
the predicted value of the wind turbine generator in the period t;
step (B6), BIO unit output constraint, as shown in formula (18),
wherein ,
and />
Maximum output and minimum output limit values of the unit i are respectively;
step (B7), the gas unit output constraint is as shown in a formula (19),
wherein ,
and />
The upper limit and the lower limit of the output of the gas unit i are respectively; />
and />
The maximum downward climbing rate and the maximum upward climbing rate of the gas unit i are respectively;
step (B8), CHP unit output constraint, as shown in formula (20),
in the formula ,
the upper limit of the output of the CHP unit in the period t is set;
step (B9), direct current power flow constraint, as shown in formula (21),
wherein ,Pij,t Representing the active power flow, θ, between node i and node j i,t and θj,t Node voltage phase angles, x, respectively representing node i and node j ij Representing the reactance of the line i-j,
represents the upper capacity limit of line i-j, < >>
Represents the maximum value of the voltage phase angle of the node i, theta ref To balance node phase angles.
As shown in fig. 2, step (C), the specific steps are as follows, a carbon flow tracking model is established to obtain node carbon potential and carbon emission of each node of the system, wherein the carbon emission flow is the relationship between the carbon emission flow index and the energy flow in the electrothermal coupling network, the carbon emission flow index includes branch carbon emission flow F, branch carbon emission flow rate R, branch carbon emission flow density ρ and node carbon potential E, the specific steps are as follows,
step (C1), unit injection distribution matrix is used for describing the connection relation between all generator units and a power system and the active power injected into the system by the unit, and is also used for describing the boundary condition of carbon emission flow generated by the generator units in the system;
step (C11), wherein the kth generator set is connected to the node j, and the active power flow injected into the node j from the kth node containing the generator is P, then P Gkj P, otherwise P Gkj ＝0；
Step (C12), for the comprehensive energy system, not only an electric network but also a thermal network are provided, the unit injection distribution matrix of the comprehensive energy system can be expanded into an energy matrix of an energy supply node injection network, and P is used for respectively G 、φ B A representation;
step (C2)Load distribution matrix, using P L And the connection relation between all the electric loads and the electric power system and the active load quantity are described, so that the boundary condition of the consumption carbon emission flow of the electric power consumer in the system is described.
Step (C21), wherein node j is the mth node with load, and the active load is P, then P Lmj P, otherwise P Lmj ＝0；
Step (C22), for the comprehensive energy system, the node load matrix of the electrothermal network has both electric load and thermal load by P respectively L 、φ L A representation;
step (C3), node active flux matrix, using P N And (3) representing. In the calculation of carbon flow, this concept will be used to describe the contribution of the genset to the node and the node to node carbon potential in the system. The elements of the node active flux matrix are specifically defined as follows.
For node I, let I + Representing a set of branches with a flow of current into node i, p Bs The active power for branch s, as shown in equation (22),
wherein ,pGi For the generator set output of the access node i, if the node has no generator set or the generator set output is 0, p Gi =0, all off-diagonal elements P in the matrix Nij =0. For the comprehensive energy system, the energy flux matrix of the nodes is respectively P N 、φ N And (3) representing.
Preferably, step (D), establishing an electrothermal coupling network carbon emission flow model, wherein nodes are divided into two types, namely an energy supply node and an energy utilization node,
step (D1), the position and carbon emission intensity of the energy supply node are known, K generators exist in the power network, K heating devices exist in the heating network, the carbon potential of the node is calculated, as shown in a formula (23),
step (D11), the node carbon potential of the electrothermal coupling network is respectively shown in formula (24),
wherein ,PL 、φ L Node load matrix, P of electrothermal network G 、φ B Injecting energy matrix P of network into energy supply nodes respectively e 、φ h Respectively an energy flow distribution matrix, P N 、φ N Respectively node energy flux matrixes;
step (D111), the node carbon emission is shown in formula (25),
wherein ,ReL 、R hL And the carbon emission of the nodes of the electric heating network respectively.
Preferably, step (E), a carbon emission degree rating model is established, rating standards are formulated, the carbon emission of the user is rated, the average carbon potential and the total carbon emission of the nodes in the scheduling time are taken to carry out comprehensive evaluation according to a carbon flow tracking model, the specific steps are as follows,
step (E1), the carbon potential and the carbon emission amount are different in dimension, and the carbon potential and the carbon emission amount are subjected to a averaging treatment, as shown in a formula (26),
wherein f represents the degree of carbon emission; n is the number of system user nodes; w1 and w2 are the weights of the two evaluation components, w1+w2=1, respectively; r is R eLi and RhLi The carbon emission of the electric and thermal network of the load node i is respectively; r is R av and Eav The carbon emission and average carbon potential of the load node i are respectively; r is R av and Eav The average carbon emission and the carbon potential of the system are respectively; p (P) Li and φLi The electric load and the thermal load of the load node i are respectively;
step (E11), defining a carbon emission level marker value K as shown in a formula (27),
K＝[w 1 (R av /R av )+w 2 (E av /E av )]×(1-β)＝1-β (27)
wherein, beta is the emission reduction coefficient of the system, and the value of beta is 0.05.
As shown in fig. 3, step (F), the load node carbon emission degree is used as a basis to establish a distribution model of green responsibility distribution certificates; the carbon emission degree standard pole value is divided into a green environment-friendly partition, a low emission partition, a standard emission partition, a high emission partition and an ultra-high emission partition, and the specific steps are as follows,
step (F1), constructing a minimum difference between users with different carbon emission levels as an objective function of the model, as shown in a formula (28),
f′ i ＝w 1 [(R i -G ci E av )/R av ]+w 2 [(R i -G ci E av )/P i /△t/E av ] (28)
wherein ,F2 Representing the difference of carbon emission degrees of users in the whole network; f's' i The carbon emission degree after green responsibility certification is distributed for the user i; mu is the average carbon emission degree of the whole network after the green responsibility certificate is distributed; g ci The number of green liability certificates allocated for user i; g p The total amount of green liability certificates to be distributed;
step (F11), constructing constraint conditions as the quantity constraint of certificate allocation, as shown in a formula (29),
G ci ≥0
the number of allocations is shown in table 1,
TABLE 1 Green license distribution quantity
Step (F2), the green environment-friendly partition rating standard is shown in a formula (30),
f＜(1-2α)K (30)
step (F3), the low emission zone rating criteria is as shown in equation (31),
(1-2α)K≤f≤(1-α)K (31)
step (F4), standard emissions partition rating criteria are shown in equation (32),
(1-α)K＜f≤(1+α)K (32)
step (F5), the high emission partition rating criteria is shown in equation (33),
(1+α)K＜f≤(1+2α)K (33)
step (F6), the rating standard of the extra-high emission subarea is shown as a formula (34),
f＞(1+2α)K (34)
where α is the rating standard segmentation factor. .
In summary, the green certificate distribution method based on the comprehensive energy system carbon emission degree rating has the advantages of dividing 5 grades, namely, green environment-friendly subareas, low emission subareas, standard emission subareas, high emission subareas and ultra-high emission subareas, carrying out grade assessment on the carbon emission degree of users, enabling the users to clearly know the carbon emission condition of the users, and defining the emission reduction responsibility of the users, thereby actively guiding the users of the high emission subareas and the ultra-high emission subareas to purchase green responsibility certificates, reducing the equivalent carbon emission degree, and having the advantages of scientific and reasonable method, strong applicability, good effect and the like.
The foregoing has outlined and described the basic principles, features, and advantages of the present invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, and that the above embodiments and descriptions are merely illustrative of the principles of the present invention, and various changes and modifications may be made without departing from the spirit and scope of the invention, which is defined in the appended claims. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (7)
1. The green certificate distribution method based on the carbon emission degree rating of the comprehensive energy system is characterized by comprising the following steps of: comprises the steps of,
step (A), a generator set model of a comprehensive energy system is established, and the power generation power and the power generation cost of each generator set are obtained according to the established generator set model of the comprehensive energy system;
step (B), constructing an optimal output model of the generator set, so that the system can optimally output under the minimum power generation cost;
step (C), establishing a carbon flow tracking model to obtain node carbon potential and carbon emission of each node of the system;
step (D), establishing an electrothermal coupling network carbon emission flow model;
step (E), establishing a carbon emission degree rating model, formulating rating standards, and rating the carbon emission of the user;
and (F) establishing an allocation model of the green responsibility allocation certificate.
2. The green certificate distribution method based on the carbon emission level rating of the integrated energy system according to claim 1, wherein: step (A), a generator set model of the comprehensive energy system is constructed, wherein the generator set model comprises a wind generator set output model, a photovoltaic generator set output model, a biomass energy set output model, a thermal power unit output model and a CHP generator set output model, and the specific steps are as follows,
step (A1), constructing a wind turbine output model, wherein the wind turbine output model is closely related to site conditions, and the actual wind speed at the hub is different from the high-degree wind speed of the monitoring point, specifically comprising the following steps of,
step (A11), converting the measured wind speed, as shown in formula (1),
wherein ,vref And v (k) are the measured wind speed of the monitoring point at the kth moment and the wind speed at the hub, H and H respectively ref The height of the hub and the height of the actual measurement point are respectively, and alpha is a surface roughness degree description factor;
step (A12), modeling the relation between the power output and the wind speed of the wind turbine by adopting a piecewise function, as shown in a formula (2),
wherein ,Prated V is the rated output power of the wind turbine generator min V is the minimum starting wind speed of the wind turbine generator max To cut off wind speed v rated The minimum wind speed required by rated power output of the wind turbine generator is set;
step (A13), calculating the power generation cost C of the wind turbine generator system WT As shown in the formula (3),
C WT ＝a wt ×P WT (3)
wherein ,awt The power generation cost coefficient of the wind turbine generator is set;
step (A2), constructing a photovoltaic unit output model, wherein the photovoltaic unit output model is formed by converting a photovoltaic cell into an equivalent circuit model, researching UI characteristics of the photovoltaic cell, specifically comprising the following steps of,
step (A21), constructing an accurate simulation model of the series-parallel resistor, and the output power of the photovoltaic array is related to the illumination intensity, the temperature and the standard test condition, wherein the output power of the photovoltaic cell is shown as a formula (4),
wherein ,PPV G is the output power of the photovoltaic cell when the illumination intensity is G stc Is the illumination intensity under STC working condition, T STC Is the surface temperature of the photovoltaic cell under STC working condition, P stc The maximum output power under STC working conditions is G, the illumination intensity is G, k is a power temperature coefficient, and T is the surface temperature of the photovoltaic cell;
step (A22), calculating the power generation cost C of the photovoltaic unit PV As shown in the formula (5),
C PV ＝a pv ×P PV (5)
wherein ,apv The power generation cost coefficient of the wind turbine generator is set;
step (A3), constructing an output model of the thermal power generating unit, wherein the thermal power generating unit heats water in a boiler by using coal, oil and combustible gas as fuels, heats the water, and then generates electricity by using steam to push a gas wheel,
step (A31), calculating the power supply E of the thermal power generator HOT (t) as shown in the formula (6),
E HOT (t)＝P HOT (t)△t(6)
wherein ,PHOT The power generation power of the thermal power generating unit;
step (A32), calculating the power generation cost C of the thermal power generating unit HOT As shown in the formula (7),
wherein ,ai 、b i and ci The power generation cost coefficient of the thermal power generating unit;
step (A4), constructing a biomass energy unit output model, wherein the biomass energy unit is converted into electric energy through gasification and storage and burning in a gas generator, and the specific steps are as follows,
step (A41), calculating the power supply E of the gas generator BIO (t) as shown in the formula (8),
E BIO (t)＝P BIO (t)△t(8)
wherein ,PBIO Generating power for the biomass energy unit;
step (A42), calculating the power generation cost C of the biomass energy unit BIO As shown in the formula (9),
wherein ,abg 、b bg and cbg The power generation cost coefficient of the biomass energy unit;
step (A5), a CHP unit output model is built, wherein the CHP unit is coupling equipment in an electrothermal network and can simultaneously generate electric energy and heat; the CHP unit consumes natural gas, and generates electricity through the gas generator directly, and the heat is utilized by the absorption heat pump and refrigeration technology, the specific steps are as follows,
step (A51), calculating the power supply and the heat supply of the CHP unit, wherein the power supply and the heat supply are respectively shown in a formula (10) and a formula (11),
P E ＝Qη e (10)
φ H ＝Qη h λ a (11)
wherein Q is consumption of primary natural gas energy of the CHP unit, and P E and φH Respectively the power supply quantity and the heat supply quantity of the CHP unit, eta e Is the power generation efficiency eta of the prime motor h Lambda is the efficiency of the waste heat recovery device a Is the coefficient of performance of the absorption heat pump;
step (A51), calculating the power generation cost of the CHP set, as shown in a formula (12),
wherein g is the number of the cogeneration unit, P E Power supply for cogeneration, phi E A, heat supply power for cogeneration g 、b g and cg Power cost coefficient, sigma, for cogeneration CHP C is the reduction value of electric power when extracting unit steam quantity under fixed steam inlet quantity CHP And the energy supply cost of the cogeneration unit is reduced.
3. The green certificate distribution method based on the carbon emission level rating of the integrated energy system according to claim 2, wherein: step (B), building a generator set model of the comprehensive energy system, specifically comprising the following steps of,
step (B1), on the basis of the completion of the establishment of the generator set model, establishing the optimal output of the set with the minimum generating cost as the target, wherein the objective function of the optimal output of the comprehensive energy system is shown as a formula (13):
wherein ,F1 The power generation cost of the system; n is n 1 、n 2 、n 3 、n 4 、n 5 and n6 Respectively the total number of a wind turbine generator system, a photovoltaic turbine generator system, a thermal power generation unit, a biomass energy unit, a CHP unit and a gas turbine generator system, C WT,i 、C PV,i 、C HOT,i 、C BIO,i 、C MT,i and CCHP,i The method is characterized by comprising the following steps of generating cost in t time periods of a wind turbine generator, a photovoltaic turbine generator, a thermal power generating unit, a biomass energy unit, a gas turbine generator and a CHP (gas turbine generator) unit respectively, wherein the constraint conditions comprise the following specific steps:
step (B2), load balancing constraints, as shown in equation (14),
wherein ,Plt An electrical load demand for period t; p (P) WT,it 、P PV,it 、P HOT,it 、P BIO,it 、P MT,it and PCHP,it The power generation system comprises power generation powers of a wind turbine generator system, a photovoltaic turbine generator system, a thermal power generating unit, a biomass energy unit and a CHP unit;
step (B3), thermal power generating unit constraint, as shown in formula (15),
wherein ,
and />
Maximum output and minimum output limit values of the unit i are respectively; />
and />
The maximum downward climbing rate and the maximum upward climbing rate of the unit i are respectively;
step (B4), the output constraint of the wind turbine generator is shown as a formula (16),
wherein ,
the predicted value of the wind turbine generator in the period t;
step (B5), the output constraint of the photovoltaic unit is shown as a formula (17),
wherein ,
the predicted value of the wind turbine generator in the period t;
step (B6), BIO unit output constraint, as shown in formula (18),
wherein ,
and />
Maximum output and minimum output limit values of the unit i are respectively;
step (B7), the gas unit output constraint is as shown in a formula (19),
wherein ,
and />
The upper limit and the lower limit of the output of the gas unit i are respectively; />
and />
The maximum downward climbing rate and the maximum upward climbing rate of the gas unit i are respectively;
step (B8), CHP unit output constraint, as shown in formula (20),
in the formula ,
the upper limit of the output of the CHP unit in the period t is set;
step (B9), direct current power flow constraint, as shown in formula (21),
wherein ,Pij,t Representing the active power flow, θ, between node i and node j i,t and θj,t Node voltage phase angles, x, respectively representing node i and node j ij Representing the reactance of the line i-j,
represents the upper capacity limit of line i-j, < >>
Represents the maximum value of the voltage phase angle of the node i, theta ref To balance node phase angles.
4. The green certificate distribution method based on the carbon emission level rating of the integrated energy system according to claim 3, wherein: step (C), the specific steps are as follows, a carbon flow tracking model is established to obtain the node carbon potential and the carbon emission amount of each node of the system, wherein the carbon emission flow is the relation between the carbon emission flow index and the energy flow in the electric heating coupling network, the carbon emission flow index comprises a branch carbon emission flow F, a branch carbon emission flow rate R, a branch carbon emission flow density ρ and a node carbon potential E, the specific steps are as follows,
step (C1), unit injection distribution matrix is used for describing the connection relation between all generator units and a power system and the active power injected into the system by the unit, and is also used for describing the boundary condition of carbon emission flow generated by the generator units in the system;
step (C11), wherein the kth generator set is connected to the node j, and the active power flow injected into the node j from the kth node containing the generator is P, then P Gkj P, otherwise P Gkj ＝0；
Step (C12), for the comprehensive energy system, not only an electric network but also a thermal network are provided, the unit injection distribution matrix of the comprehensive energy system can be expanded into an energy matrix of an energy supply node injection network, and P is used for respectively G 、φ B A representation;
step (C2), load distribution matrix, using P L And the connection relation between all the electric loads and the electric power system and the active load quantity are described, so that the boundary condition of the consumption carbon emission flow of the electric power consumer in the system is described.
Step (C21), wherein node j is the mth node with load, and the active load is P, then P Lmj P, otherwise P Lmj ＝0；
Step (C22), for the comprehensive energy system, the node load matrix of the electrothermal network has both electric load and thermal load by P respectively L 、φ L A representation;
step (C3), node active flux matrix, using P N In the carbon flow calculation, the contribution of the generator set to the node and the node to the node carbon potential in the system will be described by using the concept, and the elements of the node active flux matrix are specifically defined as follows.
For node I, let I + Representing a set of branches with a flow of current into node i, p Bs The active power for branch s, as shown in equation (22),
wherein ,pGi For the generator set output of the access node i, if the node has no generator set or the generator set output is 0, p Gi =0, all off-diagonal elements P in the matrix Nij =0. For the comprehensive energy system, the energy flux matrix of the nodes is respectively P N 、φ N And (3) representing.
5. The green certificate distribution method based on the comprehensive energy system carbon emission degree rating according to claim 4, wherein: step (D), establishing an electrothermal coupling network carbon emission flow model, wherein nodes are divided into two types, namely an energy supply node and an energy utilization node respectively,
step (D1), the position and carbon emission intensity of the energy supply node are known, K generators exist in the power network, K heating devices exist in the heating network, the carbon potential of the node is calculated, as shown in a formula (23),
step (D11), the node carbon potential of the electrothermal coupling network is respectively shown in formula (24),
wherein ,PL 、φ L Node load matrix, P of electrothermal network G 、φ B Injecting energy matrix P of network into energy supply nodes respectively e 、φ h Respectively an energy flow distribution matrix, P N 、φ N Respectively node energy flux matrixes;
step (D111), the node carbon emission is shown in formula (25),
wherein ,ReL 、R hL And the carbon emission of the nodes of the electric heating network respectively.
6. The green certificate distribution method based on the comprehensive energy system carbon emission degree rating of claim 5, wherein: a step (E) of establishing a carbon emission degree rating model, formulating a rating standard, rating the carbon emission of a user, taking the average carbon potential and the total carbon emission of nodes in the scheduling time to comprehensively evaluate according to a carbon flow tracking model, specifically comprising the following steps of,
step (E1), the carbon potential and the carbon emission amount are different in dimension, and the carbon potential and the carbon emission amount are subjected to a averaging treatment, as shown in a formula (26),
wherein f represents the degree of carbon emission; n is the number of system user nodes, w1 and w2 are the weights of two evaluation components, w1+w2=1, R eLi and RhLi The carbon emission of the electric and thermal network of the load node i is respectively; r is R av and Eav Carbon emissions and average carbon potential of load node i, R av and Eav Average carbon emission and carbon potential of the system, P Li and φLi The electric load and the thermal load of the load node i are respectively;
step (E11), defining a carbon emission level marker value K as shown in a formula (27),
K＝[w 1 (R av /R av )+w 2 (E av /E av )]×(1-β)＝1-β (27)
wherein, beta is the emission reduction coefficient of the system, and the value of beta is 0.05.
7. The green certificate distribution method based on the comprehensive energy system carbon emission degree rating of claim 6, wherein: step (F), building an allocation model of green responsibility allocation certificates according to the carbon emission degree of the load nodes; the carbon emission degree standard pole value is divided into a green environment-friendly partition, a low emission partition, a standard emission partition, a high emission partition and an ultra-high emission partition, and the specific steps are as follows,
step (F1), constructing a minimum difference between users with different carbon emission levels as an objective function of the model, as shown in a formula (28),
wherein ,F2 Representing the difference of carbon emission degrees of users in the whole network, f i ' carbon emission degree after green liability certificate is allocated to user i, μ is the average carbon emission degree of the whole network after green liability certificate is allocated, G ci The number of green liability certificates assigned to user i, G p The total amount of green liability certificates to be distributed;
step (F11), constructing constraint conditions as the quantity constraint of certificate allocation, as shown in a formula (29),
step (F2), the green environment-friendly partition rating standard is shown in a formula (30),
f＜(1-1α)K (30)
step (F3), the low emission zone rating criteria is as shown in equation (31),
(1-2α)K≤f≤(1-α)K (31)
step (F4), standard emissions partition rating criteria are shown in equation (32),
(1-α)K＜f≤(1+α)K (32)
step (F5), the high emission partition rating criteria is shown in equation (33),
(1+α)K＜f≤(1+2α)K (33)
step (F6), the rating standard of the extra-high emission subarea is shown as a formula (34),
f＞(1+2α)K (34)
where α is the rating standard segmentation factor.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310095792.4A CN116228000A (en) | 2023-01-29 | 2023-01-29 | Green certificate distribution method based on comprehensive energy system carbon emission degree rating |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310095792.4A CN116228000A (en) | 2023-01-29 | 2023-01-29 | Green certificate distribution method based on comprehensive energy system carbon emission degree rating |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116228000A true CN116228000A (en) | 2023-06-06 |
Family
ID=86580015
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310095792.4A Pending CN116228000A (en) | 2023-01-29 | 2023-01-29 | Green certificate distribution method based on comprehensive energy system carbon emission degree rating |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116228000A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117220346A (en) * | 2023-07-27 | 2023-12-12 | 河海大学 | Comprehensive energy service business electricity-carbon-green certificate double-layer distributed scheduling method |
-
2023
- 2023-01-29 CN CN202310095792.4A patent/CN116228000A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117220346A (en) * | 2023-07-27 | 2023-12-12 | 河海大学 | Comprehensive energy service business electricity-carbon-green certificate double-layer distributed scheduling method |
| CN117220346B (en) * | 2023-07-27 | 2024-04-16 | 河海大学 | Comprehensive energy service business electricity-carbon-green certificate double-layer distributed scheduling method |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Das et al. | Optimisation of stand-alone hybrid CHP systems meeting electric and heating loads | |
| Ghenai et al. | Design of solar-biomass hybrid microgrid system in Sharjah | |
| CN110659830A (en) | Multi-energy micro-grid planning method for comprehensive energy system | |
| Zhang et al. | Multi-objective particle swarm optimization (MOPSO) for a distributed energy system integrated with energy storage | |
| CN110417045A (en) | A kind of optimization method for alternating current-direct current mixing micro-capacitance sensor capacity configuration | |
| Song et al. | Multi-objective optimization of large-scale grid-connected photovoltaic-hydrogen-natural gas integrated energy power station based on carbon emission priority | |
| CN116228000A (en) | Green certificate distribution method based on comprehensive energy system carbon emission degree rating | |
| Pradhan et al. | Techno-economic evaluation of stand-alone hybrid renewable energy system for remote village using HOMER-pro software | |
| CN109255487A (en) | A kind of integrated energy system optimization method based on normalized matrix model | |
| Abunku et al. | Modelling of a CHP system with electrical and thermal storage | |
| Li et al. | Planning model of integrated energy system considering P2G and energy storage | |
| Matsubara et al. | Supply and demand control of dispersed type power sources in micro grid | |
| Abd El-Rahman | Optimization of renewable energy-based smart micro-grid system | |
| CN113131513B (en) | Method for optimizing operation of electric, thermal and gas conversion system with consideration of carbon emission and storage medium | |
| Ma et al. | Game theory method for multi-objective optimizing operation in microgrid | |
| Li et al. | Game theory method for multi-objective optimal operation of microgrid | |
| CN114386256A (en) | Regional electric heating system optimal scheduling method considering flexibility constraint of electric heating equipment and heat supply network characteristics | |
| Firdouse et al. | A hybrid energy storage system using GA and PSO for an islanded microgrid applications | |
| Syed et al. | Energy Management Strategy for Grid-Networks of Distributed Generation | |
| Kansara et al. | Penetration of renewable energy resources based dispatch strategies for isolated hybrid systems | |
| Surianu et al. | Comparative study of the opportunity to use Renewable Energy Sources to supply Residential Consumers | |
| Jun et al. | Comprehensive Evaluation of Microgrid Planning Scheme based on AHP-Entropy method | |
| Cui et al. | OPTIMAL OPERATION FOR COMBINED COOLING HEATING AND POWER MICROGRID CONSIDERING NONLINEAR MODELING AND LOAD SHIFTING | |
| Bachri et al. | Multi Objective Dynamic DC Optimal Power Flow Considering Batteries Output on Microgrid | |
| Du et al. | Coordinated optimal control strategy for multi-energy microgrids considering P2G technology and demand response |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |