CN116596123A - Low-carbon optimal scheduling method for park comprehensive energy system - Google Patents
Low-carbon optimal scheduling method for park comprehensive energy system Download PDFInfo
- Publication number
- CN116596123A CN116596123A CN202310428741.9A CN202310428741A CN116596123A CN 116596123 A CN116596123 A CN 116596123A CN 202310428741 A CN202310428741 A CN 202310428741A CN 116596123 A CN116596123 A CN 116596123A
- Authority
- CN
- China
- Prior art keywords
- gas
- energy
- heat
- power
- carbon
- 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 253
- 238000000034 method Methods 0.000 title claims abstract description 123
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 claims abstract description 227
- 238000006243 chemical reaction Methods 0.000 claims abstract description 161
- 230000008569 process Effects 0.000 claims abstract description 75
- 230000007246 mechanism Effects 0.000 claims abstract description 58
- 238000004146 energy storage Methods 0.000 claims abstract description 19
- 238000010977 unit operation Methods 0.000 claims abstract description 14
- 238000004364 calculation method Methods 0.000 claims abstract description 7
- 230000006870 function Effects 0.000 claims abstract description 7
- VNWKTOKETHGBQD-UHFFFAOYSA-N methane Chemical compound C VNWKTOKETHGBQD-UHFFFAOYSA-N 0.000 claims description 133
- 239000007789 gas Substances 0.000 claims description 122
- 229910052739 hydrogen Inorganic materials 0.000 claims description 38
- 239000001257 hydrogen Substances 0.000 claims description 38
- 238000005457 optimization Methods 0.000 claims description 36
- 239000003345 natural gas Substances 0.000 claims description 35
- UFHFLCQGNIYNRP-UHFFFAOYSA-N Hydrogen Chemical compound [H][H] UFHFLCQGNIYNRP-UHFFFAOYSA-N 0.000 claims description 32
- 238000005338 heat storage Methods 0.000 claims description 27
- 238000010438 heat treatment Methods 0.000 claims description 26
- 230000005611 electricity Effects 0.000 claims description 21
- 238000003860 storage Methods 0.000 claims description 21
- 238000007599 discharging Methods 0.000 claims description 20
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 20
- 238000005868 electrolysis reaction Methods 0.000 claims description 19
- 238000009826 distribution Methods 0.000 claims description 18
- 238000004519 manufacturing process Methods 0.000 claims description 18
- 230000009194 climbing Effects 0.000 claims description 16
- 238000012946 outsourcing Methods 0.000 claims description 16
- 239000002918 waste heat Substances 0.000 claims description 16
- 238000004458 analytical method Methods 0.000 claims description 14
- 230000000694 effects Effects 0.000 claims description 9
- 230000003993 interaction Effects 0.000 claims description 8
- 238000012423 maintenance Methods 0.000 claims description 8
- 238000010248 power generation Methods 0.000 claims description 7
- 238000011084 recovery Methods 0.000 claims description 6
- 238000009825 accumulation Methods 0.000 claims description 5
- 230000005484 gravity Effects 0.000 claims description 5
- 239000012528 membrane Substances 0.000 claims description 5
- 230000005540 biological transmission Effects 0.000 claims description 4
- 230000007717 exclusion Effects 0.000 claims description 4
- 238000012546 transfer 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
- UGFAIRIUMAVXCW-UHFFFAOYSA-N Carbon monoxide Chemical compound [O+]#[C-] UGFAIRIUMAVXCW-UHFFFAOYSA-N 0.000 claims description 3
- 239000003546 flue gas Substances 0.000 claims description 3
- 230000005251 gamma ray Effects 0.000 claims description 3
- 230000009467 reduction Effects 0.000 description 13
- 230000031700 light absorption Effects 0.000 description 7
- 230000008901 benefit Effects 0.000 description 6
- 238000011161 development Methods 0.000 description 4
- 238000010276 construction Methods 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 239000002699 waste material Substances 0.000 description 3
- 238000005286 illumination Methods 0.000 description 2
- 230000006872 improvement Effects 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 238000000342 Monte Carlo simulation Methods 0.000 description 1
- 238000010521 absorption reaction Methods 0.000 description 1
- 230000009471 action Effects 0.000 description 1
- 238000003491 array Methods 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000010835 comparative analysis Methods 0.000 description 1
- 230000008878 coupling Effects 0.000 description 1
- 238000010168 coupling process Methods 0.000 description 1
- 238000005859 coupling reaction Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 230000029087 digestion Effects 0.000 description 1
- 238000005485 electric heating Methods 0.000 description 1
- 238000004134 energy conservation Methods 0.000 description 1
- 238000005265 energy consumption Methods 0.000 description 1
- 230000005284 excitation Effects 0.000 description 1
- 150000002431 hydrogen Chemical class 0.000 description 1
- 238000006386 neutralization reaction Methods 0.000 description 1
- 238000005498 polishing Methods 0.000 description 1
- 230000001737 promoting effect Effects 0.000 description 1
- 230000005855 radiation Effects 0.000 description 1
- 230000000452 restraining effect Effects 0.000 description 1
- 238000010206 sensitivity analysis Methods 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000001360 synchronised effect Effects 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
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
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- 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
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2111/00—Details relating to CAD techniques
- G06F2111/04—Constraint-based CAD
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2111/00—Details relating to CAD techniques
- G06F2111/06—Multi-objective optimisation, e.g. Pareto optimisation using simulated annealing [SA], ant colony algorithms or genetic algorithms [GA]
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Economics (AREA)
- Physics & Mathematics (AREA)
- Strategic Management (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Marketing (AREA)
- Tourism & Hospitality (AREA)
- Quality & Reliability (AREA)
- Health & Medical Sciences (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Development Economics (AREA)
- Water Supply & Treatment (AREA)
- Public Health (AREA)
- Educational Administration (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Computer Hardware Design (AREA)
- Evolutionary Computation (AREA)
- Geometry (AREA)
- General Engineering & Computer Science (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The low-carbon optimized dispatching method of the park comprehensive energy system comprises the following steps of 1) establishing a unit operation model of a cogeneration unit, a gas boiler, energy storage equipment and the like contained in the park comprehensive energy system, thereby establishing an operation topological structure of the park comprehensive energy system; 2) Finely building an electric conversion gas two-stage operation model, and building a reaction heat energy supply model in the electric conversion gas methanation process based on the electric conversion gas two-stage operation model; 3) Establishing a park comprehensive energy system ladder-type carbon transaction mechanism model containing compensation coefficients based on the existing carbon transaction mechanism; 4) And (3) taking the minimum running cost, energy discarding power and carbon emission as objective functions, establishing a park comprehensive energy system optimizing running model based on the step (2) and the step (3), designing constraint conditions of the model, and carrying out multi-objective optimizing calculation on the model by using a CPLEX solver. Compared with the prior art, the method improves the utilization efficiency of the comprehensive energy system of the park to the energy, and effectively reduces the carbon emission of the park.
Description
Technical Field
The invention belongs to the technical field of energy scheduling, and particularly relates to a low-carbon optimal scheduling method for a park comprehensive energy system.
Background
The park comprehensive energy system (Community Integrated Energy System, CIES) is used as an energy utilization main body in the construction of a smart city, can realize the advantage complementation among different energy sources, and becomes a research hotspot in the field of low-carbon park construction under the 'two-carbon' strategic goal.
The problem of energy abandonment caused by the unstable characteristic of clean energy is particularly remarkable, and the improvement of the digestion capacity is an important work for the construction of a low-carbon park. The paper describes the basic idea of an electric Power to Gas (P2G) technology in detail in electric-Gas comprehensive energy system double-layer optimization scheduling taking electric Power into consideration to consume wind Power, and establishes a double-layer optimization scheduling model containing P2G and wind Power, and the result shows that surplus electric energy is converted into natural Gas by using the P2G, so that the wind Power consumption capability can be effectively improved. However, in this paper, the built P2G operation model is too simple, and the P2G two-stage operation model is not built in detail; and, the strongly exothermic reaction characteristics of the P2G methanation process were not considered and the heating capacity of the reaction characteristics was not further analyzed.
The introduction of carbon trading mechanisms is one of the effective means of reducing the amount of carbon emissions from a campus. The paper 'virtual power plant economic dispatch model considering electricity consumption behavior under a carbon transaction mechanism' proposes to participate a comprehensive energy system into a carbon transaction market, and verifies the improvement of the carbon emission reduction capability of the system after the carbon transaction mechanism is introduced. However, the paper only carries out transactions in a fixed carbon price mode, and the market guiding effect is limited; the paper "low-carbon economic dispatch of electric-gas-heat comprehensive energy system considering ladder carbon trade" further proposes a ladder carbon trade mechanism, and further constrains carbon emission of the comprehensive energy system by constructing ladder carbon price. However, the carbon transaction mechanism in the paper mainly aims at the situation of insufficient carbon quota, penalty measures with different degrees are designed, and the situation that the actual situation has carbon quota redundancy is not fully considered.
Disclosure of Invention
The invention aims to solve the technical problems that in the process of establishing a P2G operation model in the prior art, a P2G two-stage operation model is not finely established, and the heat supply of the reaction heat in the P2G methanation process is not considered, so that renewable energy sources such as wind, light and the like are easy to cause and cannot be utilized to the maximum extent; and in the existing carbon transaction mechanism model building process, the carbon quota redundancy existing in the actual situation is not fully considered, so that the technical problem that the carbon emission of a park cannot be fully restrained is easily caused, and the low-carbon optimal scheduling technology is provided.
In order to solve the technical problems, the invention adopts the following technical scheme:
a low-carbon optimal scheduling method for a park comprehensive energy system specifically comprises the following steps:
step 1: building a unit operation model of a cogeneration unit, a gas boiler, energy storage equipment and the like contained in the park comprehensive energy system, thereby building a park comprehensive energy system operation topological structure;
step 2: finely building an electric conversion gas two-stage operation model, and building a reaction heat energy supply model in the electric conversion gas methanation process based on the electric conversion gas two-stage operation model;
step 3: establishing a park comprehensive energy system ladder-type carbon transaction mechanism model containing compensation coefficients based on the existing carbon transaction mechanism;
Step 4: and (3) taking the minimum running cost, energy discarding power and carbon emission as objective functions, establishing a park comprehensive energy system optimizing running model based on the step (2) and the step (3), designing constraint conditions of the model, and carrying out multi-objective optimizing calculation on the model by using a CPLEX solver.
The step 1 specifically comprises the following steps:
step 1) building a cogeneration unit operation model
The cogeneration unit is composed of a gas turbine and a waste heat boiler, the gas turbine generates power by combusting natural gas, the discharged waste heat is recovered by the waste heat boiler and used for supplying heat, and the operation model is shown in the formula (1):
wherein: p (P) CHP,e (t)、P CHP,h (t) -power supply and heat supply power of the CHP unit at the moment t, and kW; q (Q) GT (t) -exhaust residual heat of the gas turbine at the moment t, kW; η (eta) e -power generation efficiency of the gas turbine; h L -natural gas low heating value, kWh/m 3 ，H L ＝9.7kWh/m 3 ；G CHP,g (t) -the gas consumption of the CHP unit at the moment t, m 3 ；η loss -heat dissipation loss rate; η (eta) h -waste heat recovery efficiency; c (C) OP,h -the flue gas recovery rate of the waste heat boiler;
step 2) establishing a gas boiler unit operation model
The gas boiler is used for supplying heat by combusting natural gas, and the operation model is shown as formula (2):
H gb (t)＝η gb,h H L G gb,g (t) (2)
wherein: h gb (t) -heating power of GB at t time, kW; η (eta) gb,h -heating efficiency of the gas boiler; g gb,g (t) -the gas consumption at time GB, m 3 ；
Step 3) establishing an operation model of the energy storage equipment unit
The energy storage device mainly comprises a storage battery and a heat storage tank, and an operation model is shown in formulas (3) - (4):
wherein:-state of charge of the battery at time t; />Charging and discharging power of the storage battery at the moment t, and kW; η (eta) bt,chg 、η bt,dis -the charge and discharge efficiency of the battery at time t; />-thermal energy of the thermal storage tank at time t; -heat storage and release power of the heat storage tank at the moment t and kW; gamma ray h -energy self-loss rate of the heat storage tank; η (eta) hst,chg 、η hst,dis -heat storage and release efficiency of the heat storage tank.
The step 2 specifically comprises the following steps:
step 1) analyzing an electric conversion gas two-stage operation mechanism, namely two stages of water electrolysis hydrogen production and methanation, wherein the water electrolysis hydrogen production is a front reaction of methanation, and an electric conversion gas two-stage operation model is built based on the front reaction;
step 2) paying attention to the strong exothermic reaction characteristic of the electric shift gas methanation process, analyzing the heat supply capacity of the reaction characteristic, and establishing a reaction heat energy supply model of the electric shift gas methanation process based on the heat supply capacity;
in the step 1), the steps for finely constructing the electric conversion gas two-stage operation model are as follows:
(1) establishing a proton exchange membrane electrolyzer device operation model:
Wherein:-hydrogen energy output by the electrolyzer at time t, m 3 ；P P2G (t) -the power consumption of electric conversion gas at the moment t, and kW; η (eta) EW Hydrogen production efficiency for water electrolysis; />-upper and lower limit values of power consumption of the electrolytic tank equipment, kW;
(2) and (3) establishing a methane reactor operation model:
wherein:-natural gas amount and hydrogen consumption amount output by methane reactor at time t, m 3 ；η MR -energy conversion efficiency of the methane reactor; />-upper and lower limit value of hydrogen consumption of methane reactor, m 3 ；
In the step 2), the steps for establishing the reaction heat energy supply model of the electric conversion gas methanation process are as follows:
(1) analyzing the thermochemical reaction formula of the methanation process, 165.01kJ of heat is released per 1mol of methane generated in the electric shift gas methanation process, as shown in formula (7):
wherein: ΔH-entropy of chemical reaction, kJ/mol, when the value is negative, represents exotherm;
(2) analysis of conversion relation between methane yield of electric conversion gas and methanation reaction heat release, and consideration of H generated by hydrogen production by water electrolysis 2 All the methods are used for methanation, and the conversion relation between the methane yield of the electric conversion gas and the heat release of the methanation reaction in unit time is shown in the following formulas (8) - (9):
wherein:——CH 4 yield, m 3 /MWh；/>Reaction heat power, kW, of methanation process under unit power consumption of electricity-to-gas conversion; / >——H 2 Yield of Nm 3 /kWh；/>——H 2 、CH 4 Density, g/Nm 3 ；/>——H 2 、CH 4 G/mol; />-specific gravity of heat release amount for heat supply during electric conversion gas methanation process, < ->
(3) Due to the heat accumulation of the heating power network, the methanation reaction heat is considered to be directly injected into the heating power network for heat supply. The energy supply model of the reaction heat of the electric conversion gas methanation process is shown as a formula (10):
wherein: h MR And (t), namely the reaction heat supply power in the electric gas-to-methanation process at the moment of t, and kW.
The step 3 specifically comprises the following steps:
step 1) initial amount of carbon emission of distribution system
And determining initial carbon emission quota of the system by adopting a datum line method, wherein a carbon emission source of the system mainly comprises outsourcing power, a gas boiler and a cogeneration unit. Because the cogeneration unit can provide electric energy and heat energy simultaneously, the generated energy of the cogeneration unit is converted into equivalent generated heat to perform initial allocation of carbon emission, and the initial allocation model of the carbon emission of the system is as follows:
wherein: e (E) a -systemInitial carbon quota, kg; delta e 、δ h -unit power supply, heat supply output carbon quota coefficient, kg/kWh; delta e ＝0.728kg/kWh，δ h ＝0.367kg/kWh；P grid (t) -the system outsourcing electric power at the moment t, kW; lambda-conversion coefficient of converting the generated energy of the cogeneration unit into heat supply quantity, MJ/kWh, wherein lambda=6MJ/kWh;
Step 2) determining the actual carbon emissions of the system
Methane reactors consume CO 2 Therefore, the carbon emission is negative, and the actual carbon emission model of the system is shown in the formula (12):
wherein: e (E) P -actual carbon emissions of the system, kg;grid emission factor, kg/kWh,and updating in real time according to the latest numerical value issued by the ecological environment part; />-the carbon emission coefficient of the natural gas,omega-methane reactor for absorbing CO 2 Coefficients of (2);
step 3) establishing a carbon emission transaction model
Based on the existing carbon transaction mechanism, in order to further restrict the carbon emission of the park, a compensation coefficient alpha is introduced, namely, when the actual carbon emission is lower than the initial quota, a certain subsidy is given to the market as compensation, and based on the compensation coefficient alpha, a stepped carbon transaction mechanism model containing the compensation coefficient is constructed, as shown in a formula (13):
wherein: e (E) IES Actual carbon emission E of the system P And initial carbon quota E a Kg of the difference in (d);-the carbon transaction cost of the system at time t, wherein the element represents the purchase amount positively and the element represents the utilization of the residual amount negatively to obtain the subsidy; k-carbon trade basal price on the same day, yuan/kg; alpha-compensation coefficient; beta-price increase amplitude; d-the carbon emission interval length corresponding to each step, kg.
The step 4 specifically comprises the following steps:
step 1) converting the abandoned energy power and the carbon emission into abandoned energy punishment cost and carbon transaction cost respectively by introducing unit abandoned energy punishment cost and a stepped carbon transaction mechanism containing compensation coefficients, and constructing to run cost F 1 Cost F of discarding energy penalty 2 Carbon trade cost F 3 Optimizing an operational model for the targeted campus integrated energy system to convert the multi-objective optimization to a single-objective optimization, as shown in equation (15):
min F＝F 1 +F 2 +F 3 (15)
step 2) designing constraint conditions of an optimization operation model of the park comprehensive energy system;
step 3) combining the step 1) and the step 2), and solving the park comprehensive energy system optimization operation model by using an optimization solver CPLEX.
Running cost F 1 Cost F of discarding energy penalty 2 Carbon trade cost F 3 The method comprises the following steps:
1) Running cost F 1
Running cost F 1 Energy interaction cost C comprising system and power grid and natural gas company buy Cost of equipment operation and maintenance C O Energy selling cost C sell As shown in formula (16):
wherein: c (C) e -price per unit purchase of electricity, yuan/kWh; c (C) g 、C gs Unit purchase, gas selling price, yuan/m 3 ；G g,buy (t)、G g,sell (t) -the outsourcing and gas selling amount of the system at the moment of t, m 3 ；K i Unit operation maintenance cost of equipment i, yuan/kWh; p (P) i (t) -working power of equipment i at time t, kW;
2) Cost of discarding energy F 2
The energy discarding cost comprises two parts of wind discarding cost and light discarding cost, as shown in a formula (17):
wherein: p (P) WT (t)、P PV (t) -output power of wind power and photovoltaic at t moment, kW; p (P) wt (t)、P pv (t) -the actual wind power and photovoltaic power generation power at the moment t, kW;-unit wind-abandoning and light-abandoning punishment costs, yuan/kWh;
3) Carbon trade cost F 3
Wherein:the detail is shown in formula (13).
Designing constraint conditions of an optimization operation model of a park comprehensive energy system, which specifically comprises the following steps:
1) Operation constraint of cogeneration unit
In actual operation, the cogeneration unit needs to meet the operation power constraint and the climbing rate constraint, as shown in formula (19):
wherein:-upper and lower limit values of power supply quantity of the cogeneration unit, kW; />-upper and lower limit values of climbing rate of the cogeneration unit, kWh;
2) Gas boiler operation constraint
The gas boiler operation constraint is as shown in formula (20):
wherein:-upper and lower limit values of heat supply quantity of the gas boiler, kW;
3) Energy storage device operation constraints
When the storage battery and the heat storage tank run, capacity constraint, mutual exclusion constraint, charging and discharging constraint and climbing constraint are required to be met, as shown in formulas (21) - (22):
wherein:-0-1 variable representing the charge and discharge status flag bits of the battery and the thermal storage tank; And->Charging and discharging energy power upper limit value and discharging energy power lower limit value of the storage battery and the heat storage tank, and kW; />And-upper and lower limit values of the charging and discharging climbing rate of the storage battery and the heat storage tank, kWh;
4) Electric power balance constraint
In consideration of the fact that the power distribution network does not allow reverse power transmission in the actual industry, the system does not sell electricity to a power grid company. The electric power balance constraint is as shown in formula (23):
wherein:-the limit value of the outsourcing electric quantity of the system in each period of time, kW; p (P) eload And (t) -the power consumption of the user at the moment t, and kW.
5) Thermal power balance constraint
H gb (t)+P CHP,h (t)+H MR (t)+P hst,dis (t)＝H load (t)+P hst,chg (t) (24)
Wherein: h load And (t) -heat used by a user at the moment t, and kW.
The nonlinear part related to the carbon transaction mechanism in the step 3 is subjected to relaxation treatment by adopting a large M method, so that the nonlinear part is converted into a mixed integer linear model; then, establishing a park comprehensive energy system optimization operation model by utilizing a Matlab environment and utilizing a Yalmip tool box; and finally, calling an optimization solver CPLEX to solve.
The method for establishing the reaction heat energy supply model in the electric conversion gas methanation process comprises the following steps:
step 1) analyzing an electric conversion gas two-stage operation mechanism, namely two stages of water electrolysis hydrogen production and methanation, wherein the water electrolysis hydrogen production is a front reaction of methanation, and an electric conversion gas two-stage operation model is built based on the front reaction;
And 2) paying attention to the strong exothermic reaction characteristic of the electric transfer gas methanation process, analyzing the heat supply capacity of the reaction characteristic, and establishing a reaction heat energy supply model of the electric transfer gas methanation process based on the heat supply capacity.
In the step 1), the steps for finely constructing the electric conversion gas two-stage operation model are as follows:
(1) establishing a proton exchange membrane electrolyzer device operation model:
wherein:-hydrogen energy output by the electrolyzer at time t, m 3 ；P P2G (t) -the power consumption of electric conversion gas at the moment t, and kW; η (eta) EW Hydrogen production efficiency for water electrolysis; />-upper and lower limit values of power consumption of the electrolytic tank equipment, kW;
(2) and (3) establishing a methane reactor operation model:
wherein:-natural gas amount and hydrogen consumption amount output by methane reactor at time t, m 3 ；η MR Methane (M)The energy conversion efficiency of the reactor; />-upper and lower limit value of hydrogen consumption of methane reactor, m 3 ；
In the step 2), the steps for establishing the reaction heat energy supply model of the electric conversion gas methanation process are as follows:
(1) analyzing the thermochemical reaction formula of the methanation process, 165.01kJ of heat is released per 1mol of methane generated in the electric shift gas methanation process, as shown in formula (3):
wherein: ΔH-entropy of chemical reaction, kJ/mol, when the value is negative, represents exotherm;
(2) analysis of conversion relation between methane yield of electric conversion gas and methanation reaction heat release, and consideration of H generated by hydrogen production by water electrolysis 2 All the methods are used for methanation, and the conversion relation between the methane yield of the electric conversion gas and the heat release of the methanation reaction in unit time is shown in the following formulas (4) - (5):
wherein:——CH 4 yield, m 3 /MWh；/>Reaction heat power, kW, of methanation process under unit power consumption of electricity-to-gas conversion; />——H 2 Yield of Nm 3 /kWh；/>——H 2 、CH 4 Density, g/Nm 3 ；/>——H 2 、CH 4 G/mol; />-specific gravity of heat release amount for heat supply during electric conversion gas methanation process, < ->
(3) Due to the heat accumulation of the heating power network, the methanation reaction heat is considered to be directly injected into the heating power network for heat supply. The energy supply model of the reaction heat of the electric conversion gas methanation process is shown as a formula (6):
wherein: h MR And (t), namely the reaction heat supply power in the electric gas-to-methanation process at the moment of t, and kW.
A method for establishing a stepped carbon transaction mechanism model of a park comprehensive energy system comprises the following steps:
step 1) distributing initial amount of carbon emission of a system;
step 2) determining the actual carbon emission of the system;
step 3) establishing a carbon emission transaction model;
in step 1), at the initial amount of carbon emissions of the distribution system;
and determining initial carbon emission quota of the system by adopting a datum line method, wherein a carbon emission source of the system mainly comprises outsourcing power, a gas boiler and a cogeneration unit. Because the cogeneration unit can provide electric energy and heat energy simultaneously, the generated energy of the cogeneration unit is converted into equivalent generated heat to perform initial allocation of carbon emission, and the initial allocation model of the carbon emission of the system is as follows:
Wherein: e (E) a -initial carbon quota of system, kg; delta e 、δ h -unit power supply, heat supply output carbon quota coefficient, kg/kWh; delta e ＝0.728kg/kWh，δ h ＝0.367kg/kWh；P grid (t) -the system outsourcing electric power at the moment t, kW; lambda-conversion coefficient of converting the generated energy of the cogeneration unit into heat supply quantity, MJ/kWh, wherein lambda=6MJ/kWh;
determining the actual carbon emissions of the system in step 2);
methane reactors consume CO 2 Therefore, the carbon emission is negative, and the actual carbon emission model of the system is shown in the formula (12):
wherein: e (E) P -actual carbon emissions of the system, kg;grid emission factor, kg/kWh,and updating in real time according to the latest numerical value issued by the ecological environment part; />-the carbon emission coefficient of the natural gas,omega-methane reactor for absorbing CO 2 Coefficients of (2);
establishing a carbon emission trading model in the step 3);
based on the existing carbon transaction mechanism, in order to further restrict the carbon emission of the park, a compensation coefficient alpha is introduced, namely, when the actual carbon emission is lower than the initial quota, a certain subsidy is given to the market as compensation, and based on the compensation coefficient alpha, a stepped carbon transaction mechanism model containing the compensation coefficient is constructed, as shown in a formula (9):
E IES ＝E P -E a (10)
wherein: e (E) IES Actual carbon emission E of the system P And initial carbon quota E a Kg of the difference in (d);-the carbon transaction cost of the system at time t, wherein the element represents the purchase amount positively and the element represents the utilization of the residual amount negatively to obtain the subsidy; k-carbon trade basal price on the same day, yuan/kg; alpha-compensation coefficient; beta-price increase amplitude; d-the carbon emission interval length corresponding to each step, kg.
A method for solving an optimization operation model of a park comprehensive energy system,
the method specifically comprises the following steps:
step 1) converting the abandoned energy power and the carbon emission into abandoned energy punishment cost and carbon transaction cost respectively by introducing unit abandoned energy punishment cost and a stepped carbon transaction mechanism containing compensation coefficients, and constructing to run cost F 1 Cost F of discarding energy penalty 2 Carbon trade cost F 3 Optimizing an operational model for the targeted campus integrated energy system to convert the multi-objective optimization to a single-objective optimization, as shown in equation (11):
min F＝F 1 +F 2 +F 3 (11)
step 2) designing constraint conditions of an optimization operation model of the park comprehensive energy system;
step 3) combining the step 1) and the step 2), and solving the park comprehensive energy system optimization operation model by using an optimization solver CPLEX.
Running cost F 1 Cost F of discarding energy penalty 2 Carbon trade cost F 3 The method comprises the following steps:
1) Running cost F 1
Running cost F 1 Energy interaction cost C comprising system and power grid and natural gas company buy Cost of equipment operation and maintenance C O Energy selling cost C sell As shown in formula (12):
wherein: c (C) e -price per unit purchase of electricity, yuan/kWh; c (C) g 、C gs Unit purchase, gas selling price, yuan/m 3 ；G g,buy (t)、G g,sell (t) -the outsourcing and gas selling amount of the system at the moment of t, m 3 ；K i Unit operation maintenance cost of equipment i, yuan/kWh; p (P) i (t) -working power of equipment i at time t, kW;
2) Cost of discarding energy F 2
The energy discarding cost comprises two parts of wind discarding cost and light discarding cost, as shown in formula (13):
wherein: p (P) WT (t)、P PV (t) -output power of wind power and photovoltaic at t moment, kW; p (P) wt (t)、P pv (t) -the actual wind power and photovoltaic power generation power at the moment t, kW;-unit wind-abandoning and light-abandoning punishment costs, yuan/kWh;
3) Carbon trade cost F 3
Wherein:the details are shown in formula (9).
Designing constraint conditions of an optimization operation model of a park comprehensive energy system, which specifically comprises the following steps:
1) Operation constraint of cogeneration unit
In actual operation, the cogeneration unit needs to meet the operation power constraint and the climbing rate constraint, as shown in formula (15):
wherein:-upper and lower limit values of power supply quantity of the cogeneration unit, kW; />-upper and lower limit values of climbing rate of the cogeneration unit, kWh;
2) Gas boiler operation constraint
The gas boiler operation constraint is as shown in formula (16):
wherein:-upper and lower limit values of heat supply quantity of the gas boiler, kW;
3) Energy storage device operation constraints
When the storage battery and the heat storage tank run, capacity constraint, mutual exclusion constraint, charging and discharging constraint and climbing constraint are required to be met, as shown in formulas (17) - (18):
wherein:-0-1 variable representing the charge and discharge status flag bits of the battery and the thermal storage tank;and->Charging and discharging energy power upper limit value and discharging energy power lower limit value of the storage battery and the heat storage tank, and kW; />And-upper and lower limit values of the charging and discharging climbing rate of the storage battery and the heat storage tank, kWh;
4) Electric power balance constraint
In consideration of the fact that the power distribution network does not allow reverse power transmission in the actual industry, the system does not sell electricity to a power grid company. The electric power balance constraint is as shown in formula (19):
wherein:-the limit value of the outsourcing electric quantity of the system in each period of time, kW; p (P) eload And (t) -the power consumption of the user at the moment t, and kW.
5) Thermal power balance constraint
H gb (t)+P CHP,h (t)+H MR (t)+P hst,dis (t)＝H load (t)+P hst,chg (t) (20)
Wherein: h load And (t) -heat used by a user at the moment t, and kW.
The nonlinear part related to the carbon transaction mechanism in the step 3 is subjected to relaxation treatment by adopting a large M method, so that the nonlinear part is converted into a mixed integer linear model; then, establishing a park comprehensive energy system optimization operation model by utilizing a Matlab environment and utilizing a Yalmip tool box; and finally, calling an optimization solver CPLEX to solve.
Compared with the prior art, the invention has the following technical effects:
1) According to the basic idea of the P2G technology, a P2G two-stage operation model is finely constructed, the P2G is considered to effectively improve clean energy consumption capacity, meanwhile, the strong heat release characteristic of the methanation process is focused, and the influence of heat supply of reaction in the methanation process on CIES operation scheduling results is further analyzed;
2) According to the invention, on the basis of the existing stepwise mechanism, the compensation coefficient is introduced, the CIES stepwise carbon transaction mechanism model containing the compensation coefficient is established, the economy of the system is ensured, the carbon emission of the system is strictly controlled, and the development of carbon neutralization in a park is promoted.
Drawings
The invention is further illustrated by the following examples in conjunction with the accompanying drawings:
FIG. 1 is a CIES operating topology;
FIG. 2 is a P2G operation process;
FIG. 3 is a model solving flow;
FIG. 4 is a graph of a typical daily load and wind and light output prediction;
FIG. 5 is a graph of cell output in different scenarios;
FIG. 6 is a graph of power rejection in different scenarios;
FIG. 7 is a graph showing the electrical load scheduling results for the scenario of the present invention;
FIG. 8 is a thermal load scheduling result for the scenario of the present invention;
FIG. 9 is an illustration of the effect of natural gas price fluctuations on low carbon operation of the system;
FIG. 10 is an effect of carbon trade price on carbon emissions;
FIG. 11 is a graph showing the effect of different compensation factors on carbon trade costs.
Detailed Description
The invention discloses a low-carbon optimal scheduling method for a park comprehensive energy system, which aims to realize low-carbon development of the park. The method specifically comprises the following steps:
step 1: building a unit operation model of a cogeneration unit, a gas boiler, energy storage equipment and the like contained in the park comprehensive energy system, thereby building a park comprehensive energy system operation topological structure;
step 2: finely building an electric conversion gas two-stage operation model, and building a reaction heat energy supply model in the electric conversion gas methanation process based on the electric conversion gas two-stage operation model;
step 3: establishing a park comprehensive energy system ladder-type carbon transaction mechanism model containing compensation coefficients based on the existing carbon transaction mechanism;
step 4: and (3) taking the minimum running cost, energy discarding power and carbon emission as objective functions, establishing a park comprehensive energy system optimizing running model based on the step (2) and the step (3), designing constraint conditions of the model, and carrying out multi-objective optimizing calculation on the model by using a CPLEX solver.
Step 5: and carrying out calculation analysis by considering actual conditions, and verifying the correctness of the proposed scheme and the established model.
In addition, the invention also comprises a method for establishing a reaction heat energy supply model in the electric conversion gas methanation process; a method for establishing a ladder-type carbon transaction mechanism model of a park comprehensive energy system; a method for solving a park comprehensive energy system optimization operation model;
the steps are discussed in detail below:
the CIES operating topology is described as follows:
the CIES operating topology studied by the present invention is shown in FIG. 1. As can be seen from fig. 1, the park mainly uses clean energy devices such as Wind Turbines (WTs), photovoltaic arrays (Photovoltaic System, PV) and the like for power supply. And the combined heat and power (Combined heating and power, CHP) unit consisting of a Gas Turbine (GT) and a waste heat boiler (Waste heat boiler, WHB) is used as a coupling link to combine electricity, heat and Gas, and when the CHP unit does not meet the requirement of system heat supply, the GB provides insufficient parts. The P2G can convert surplus electric energy in the wind-light output peak period into natural gas which is easy to store and sell the natural gas to a natural gas company, and wind-light absorption is promoted so as to improve the energy utilization efficiency of the system. The storage Battery (BT) and the heat storage tank (Heat storage tank, HST) are used as energy storage devices in the system, and the peak-valley time difference is utilized to cooperate with the operation requirement to realize the coordinated optimization of the system so as to improve the energy efficiency of the system.
1) The CHP unit operation model is described as follows:
the CHP unit is composed of GT and WHB, the GT generates electricity by combusting natural gas, the discharged waste heat is recovered by the WHB for heat supply, and the operation model is shown as formula (1):
wherein: p (P) CHP,e (t)、P CHP,h (t) -power supply and heat supply power of the CHP unit at the moment t, and kW; q (Q) GT (t) -exhaust residual heat of the gas turbine at the moment t, kW; η (eta) e -power generation efficiency of the gas turbine; h L -natural gas low heating value, kWh/m 3 ，H L ＝9.7kWh/m 3 ；G CHP,g (t) -the gas consumption of the CHP unit at the moment t, m 3 ；η loss -heat dissipation loss rate; η (eta) h -waste heat recovery efficiency; c (C) OP,h -the flue gas recovery rate of the waste heat boiler.
2) The gas boiler operation model is described as follows:
GB is used for heat supply by combusting natural gas, and an operation model is shown as a formula (2):
H gb (t)＝η gb,h HLG gb,g (t) (2)
wherein: h gb (t) -heating power of GB at t time, kW; η (eta) gb,h -heating efficiency of the gas boiler; g gb,g (t) -the gas consumption at time GB, m 3 。
3) The energy storage device operational model is described as follows:
the energy storage device mainly comprises BT and HST, and the operation model is shown in formulas (3) - (4):
wherein:-state of charge of BT at time t; />Charging and discharging power of BT at t time, kW; η (eta) bt,chg 、η bt,dis -charging and discharging efficiency of BT at time t; />-thermal energy storage of HST at time t; / >-the heat storage and release power of the HST at time t, kW; gamma ray h -HST energy self-loss rate; η (eta) hst,chg 、η hst,dis Storage and release efficiencies of HST.
The two-stage operation process and the heat supply mechanism of the P2G are described as follows:
the P2G two-stage operation process studied by the present invention is shown in fig. 2. As can be seen from FIG. 2, the electrolysis of water to produce hydrogen is a pre-reaction for methanation. For the process of producing hydrogen by electrolyzing water, the electrolytic tank is a main device and can be powered by surplus wind and light output. The operation model of the proton exchange membrane-based electrolytic cell device is shown as a formula (5):
wherein:-hydrogen energy output by the electrolyzer at time t, m 3 ；P P2G (t) -power consumption of P2G at time t, kW; η (eta) EW Hydrogen production efficiency for water electrolysis; />-upper and lower limit values of power consumption of the electrolytic tank equipment, kW.
Natural gas produced by the reaction of hydrogen has a unit energy density 4 times that of hydrogen, and hydrogen is difficult to store and dangerous to transport. Therefore, a methanation process is generally adopted, and the operation model of the methane reactor is shown as a formula (6):
wherein:-natural gas amount and hydrogen consumption amount output by methane reactor at time t, m 3 ；η MR -energy conversion efficiency of the methane reactor; />-upper and lower limit value of hydrogen consumption of methane reactor, m 3 。
In the P2G methanation process, 165.01kJ heat is released every 1mol of methane is generated, and a thermochemical reaction formula in the methanation process is shown as a formula (7):
Wherein: ΔH-entropy of chemical reaction, kJ/mol, and exothermic when the value is negative.
The invention considers H generated by hydrogen production by water electrolysis 2 All are used in the methanation process, and the conversion relation between the yield of the P2G methane and the heat release of the methanation reaction in unit time is shown in the following formulas (8) - (9):
wherein:——CH 4 yield, m 3 /MWh；/>-methanation process reaction heat power, kW, under power consumption per unit of P2G; />——H 2 Yield of Nm 3 /kWh；/>——H 2 、CH 4 Density, g/Nm 3 ；/>——H 2 、CH 4 G/mol; />-specific gravity of exothermic amount of P2G methanation process for heat supply, < ->
Because of the heat accumulation property of the heating power network, the heat of reaction in the P2G methanation process is directly injected into the heating power network for heat supply, and an energy supply model for taking the heat of reaction in the P2G methanation process into account is shown in a formula (10):
wherein: h MR (t) -reaction heat heating power of P2G methanation process at t time and kW.
3. Establishing a CIES stepped carbon transaction mechanism model containing compensation coefficients:
the carbon transaction mechanism is in a comprehensive development stage in China, and the sound carbon transaction mechanism is helpful for realizing a low-carbon emission reduction target to a certain extent, and mainly comprises 3 links of initial allocation of the system carbon emission, determination of the system actual carbon emission and carbon emission transaction.
1) The initial allocation of system carbon emissions is described as follows:
and determining initial carbon emission quota of the system by adopting a datum line method, wherein a carbon emission source of the system mainly comprises outsourcing power, GB and CHP units. Because the CHP unit can provide electric energy and heat energy simultaneously, the generated energy of the CHP unit is converted into equivalent generated heat to perform initial allocation of carbon emission. The initial allocation model of the system carbon emission is as follows:
wherein: e (E) a -initial carbon quota of system, kg; delta e 、δ h -unit power supply, heat supply output carbon quota coefficient, kg/kWh; delta e ＝0.728kg/kWh，δ h ＝0.367kg/kWh；P grid (t) -the system outsourcing electric power at the moment t, kW; lambda-conversion coefficient of the generated energy of the CHP unit into heat supply quantity, MJ/kWh, and lambda=6MJ/kWh.
2) The actual carbon emissions of the system are determined as follows:
methane reactors consume CO 2 Therefore, the carbon number is negative, and the actual carbon emission model is shown in the formula (12):
wherein: e (E) P -actual carbon emissions of the system, kg;grid emission factor, kg/kWh,and updating in real time according to the latest numerical value issued by the ecological environment part; />-the carbon emission coefficient of the natural gas,omega-methane reactor for absorbing CO 2 Is a coefficient of (a).
3) The carbon emission trading model is set up as follows:
Based on the existing carbon transaction mechanism, a compensation coefficient alpha is introduced for further restraining the carbon emission of the park, namely, when the actual carbon emission is lower than the initial quota, a certain subsidy is given to the market as compensation. Based on the above, a stepwise carbon transaction mechanism model containing compensation coefficients is constructed, as shown in formula (13):
E IES ＝E P -E a (14)
wherein: e (E) IES Actual carbon emission E of the system P And initial carbon quota E a Kg of the difference in (d);-the carbon transaction cost of the system at time t, wherein the element represents the purchase amount positively and the element represents the utilization of the residual amount negatively to obtain the subsidy; k-carbon trade basal price on the same day, yuan/kg; alpha-compensation coefficient; beta-price increase amplitude; d-the carbon emission interval length corresponding to each step, kg.
The CIES optimized operation model is described as follows:
1) The objective function establishment of the CIES optimized operation model is described as follows:
the energy discarding punishment cost and the carbon emission are respectively converted into the energy discarding punishment cost and the carbon transaction cost by introducing a unit energy discarding punishment cost and a stepped carbon transaction mechanism containing compensation coefficients, so as to construct the running cost F 1 Cost F of discarding energy penalty 2 Carbon trade cost F 3 Optimizing an objective function of an operational model for the targeted campus integrated energy system to convert the multi-objective optimization to a single-objective optimization, as shown in equation (15):
min F＝F 1 +F 2 +F 3 (15)
(1) Running cost F 1 The description is as follows:
running cost F 1 Energy interaction cost C comprising system and power grid and natural gas company buy Cost of equipment operation and maintenance C O Energy selling cost C sell As shown in formula (16):
wherein: c (C) e -price per unit purchase of electricity, yuan/kWh; c (C) g 、C gs Unit purchase, gas selling price, yuan/m 3 ；G g,buy (t)、G g,sell (t) -the outsourcing and gas selling amount of the system at the moment of t, m 3 ；K i Unit operation maintenance cost of equipment i, yuan/kWh; p (P) i (t) -the operating power of the equipment i at the moment t, kW.
(2) Cost of discarding energy F 2 The description is as follows:
the energy discarding cost comprises two parts of wind discarding cost and light discarding cost, as shown in a formula (17):
wherein: p (P) WT (t)、P PV (t) -output power of wind power and photovoltaic at t moment, kW; p (P) wt (t)、P pv (t) -the actual wind power and photovoltaic power generation power at the moment t, kW;unit wind-abandoning and light-abandoning punishment costs, yuan/kWh.
(3) Carbon trade cost F 3 The description is as follows:
wherein:the detail is shown in formula (13).
2) Constraint condition design description of CIES optimization operation model is as follows:
(1) CHP unit operation constraints are described as follows:
in actual operation, the CHP unit is required to meet the operation power constraint and the climbing rate constraint, as shown in formula (19):
wherein:-upper and lower limit values of power supply quantity of the CHP unit, kW; / >-upper and lower limit values of the climbing rate of the CHP unit and kWh.
(2) The gas boiler operating constraints are described as follows:
the GB operating constraint is shown in formula (20):
wherein:-upper and lower limit values of heat supply quantity of GB, kW.
(3) The energy storage device operational constraints are described as follows:
the BT and HST run-times need to satisfy capacity constraints and mutual exclusion constraints, and charge-discharge constraints and climbing constraints, as shown in equations (21) - (22):
/>
wherein:-0-1 variable representing the charge and discharge status flag bits of BT and HST; />And->Charging and discharging energy upper limit value and discharging energy lower limit value of BT and HST, kW; />And->Charging BT and HST,The upper limit value and the lower limit value of the energy release climbing rate are kWh.
(4) The electric power balance constraint is described as follows:
in consideration of the fact that the power distribution network does not allow reverse power transmission in the actual industry, the system does not sell electricity to a power grid company. The electric power balance constraint is as shown in formula (23):
wherein:-the limit value of the outsourcing electric quantity of the system in each period of time, kW; p (P) eload And (t) -the power consumption of the user at the moment t, and kW.
(5) The thermal power balance constraint is described as follows:
H gb (t)+P CHP,h (t)+H MR (t)+P hst,dis (t)＝H load (t)+P hst,chg (t) (24)
wherein: h load And (t) -heat used by a user at the moment t, and kW.
3) The optimal running model solution is described as follows:
for the nonlinear part related to the carbon transaction mechanism in the formula (13), performing relaxation treatment on the nonlinear part by adopting a large M method so as to convert the nonlinear part into a mixed integer linear model; then establishing a CIES optimizing operation model by utilizing a Yalmip tool box by means of Matlab environment; and finally, calling an optimization solver CPLEX to solve. A detailed solution flow for the optimization run model is shown in fig. 3.
5. Calculation analysis and verification
1) Source-to-charge uncertainty handling
Considering that clean energy (wind energy and solar energy) and user load (electric load and thermal load) have obvious randomness, the optimization problem is difficult to directly solve, and the invention adopts a multi-scene method to construct a deterministic scene so as to analyze the uncertainty problem. It is widely considered by the prior researches and industries that the wind speed is compliant with classical dual-parameter Weibull distribution, and the illumination radiation intensity is compliant with Beta distributionThe corresponding probability distribution density functions are shown in (25) - (26); the user load generally obeys a normal distribution N (mu, theta) 2 ) Mu is the expected value of the distribution, θ 2 Is the variance of the distribution.
/>
Wherein: v (t) -the actual wind speed of the WT at the hub height at time t, m/s; lambda (lambda) 1 、k 1 -dimensional parameters and shape parameters of Weibull distribution; mu (mu) 1 、θ 1 -expected value, standard deviation of Weibull distribution; i (t) -the actual illumination intensity at the moment t, lux; alpha 2 、β 2 -shape parameters of Beta distribution; mu (mu) 2 、Expected value, variance of Beta distribution.
Based on the probability distribution, the method adopts the Monte Carlo method to generate multiple scenes, adopts heuristic synchronous back substitution reduction method to reduce the generated scenes, and finally obtains the typical scenes and the probability thereof after reduction.
2) The underlying data is described as follows:
taking a small industrial park in Jiangsu province of China as an example for carrying out calculation simulation analysis, and obtaining a typical daily load and WT (WT) and PV output prediction curve of the park by using a multi-scene generation method as shown in figure 4. The operating parameters of each device and stored energy are shown in tables 1 and 2; the electricity price adopts the time-sharing electricity price of the government of Jiangsu province, and the electricity price and the gas purchase and selling price in each period are shown in a table 3; carbon trade basic price k=0.252 yuan/kg, interval length d=500 kg, compensation coefficient alpha=0.2, price increase range beta=0.25, unit wind abandon, abandon light punishment cost all take 0.3 yuan/kWh.
Table1 energy device parameters Table1 Parameters for equipment
Table2 energy storage device parameters Table2 Parameters for energy storage equipment
The initial electricity storage amount of BT is 90kW, and the charging and discharging efficiencies are all 0.95; the initial heat storage capacity of the HST is 80kW, the heat charging and discharging efficiency is 0.98, and the energy self-loss rate is 0.02.
TABLE3 Time of day and gas price Table3 Time-of-use tariffs and gas prices
The scene setting is described as follows:
in order to verify the effectiveness of the optimized operation model provided by the invention, the following 4 scenes are set for comparison analysis:
the invention has the following scene: considering the strong exothermic characteristic of the P2G methanation process, and introducing CIES day-ahead optimal scheduling of a stepwise carbon transaction mechanism containing compensation coefficients.
Scene one: P2G is not considered, and CIES day-ahead optimal scheduling under the action of a carbon transaction mechanism is not considered;
scene II: P2G is considered on the basis of a scene one, but CIES day-ahead optimal scheduling with strong heat release characteristic in the methanation process is not considered;
scene III: based on a second scene, taking the CIES day-ahead optimal scheduling of the strong heat release characteristic of the P2G methanation process into consideration;
scene four: introducing CIES day-ahead optimal scheduling of a stepwise carbon transaction mechanism without considering compensation coefficients on the basis of a third scenario.
4) The results and analysis of the optimized run are described as follows:
(1) the analysis of wind-light absorbing capacity of park with P2G heat supply is described as follows:
in different scenarios, the cell output curve and the waste energy power are shown in fig. 5 and 6.
As can be seen from analysis of fig. 5 and fig. 6, the energy rejection phenomenon of the first scene is most serious, and the energy rejection power in one day is about 3112.5kW, because P2G is not introduced into the first scene, and the first scene does not have the capability of wind and light absorption. Compared with the first scene, due to the existence of P2G in other scenes, in the period of rich wind and light resources (8:00-10:00), the surplus wind and light output gradually increases the output of the electrolytic cell, and in the period of very abundant wind and light resources (10:00-15:00), the electrolytic cell is in a full-power state, so that most wind and light is absorbed to a certain extent. In the second scene, the waste energy power in one day is about 145.4kW, and the waste energy power is reduced by 95.3% compared with the first scene. Compared with the second scene, the third scene and the fourth scene have higher scene wind and light absorption level. In the third, fourth and the invention, as the P2G methanation process reaction heat is injected into the heat supply network for system heat supply, a part of heat load is shared, thereby reducing the heat supply pressure of the CHP unit, so that the electric output of the CHP unit is adjusted downwards within a certain range, a space is reserved for wind and light output, and the wind and light absorption is further promoted. The energy rejection power in one day of the scene III, the scene IV and the scene of the invention is about 53.6kW, which is reduced by 98.3 percent and 63.1 percent compared with the scene I and the scene II in sequence. Therefore, the heat of reaction in the P2G methanation process is considered, and the wind and light absorption capability of CIES can be effectively improved.
(2) The comparison analysis of the scheduling operation results of different scenes is described as follows:
the results of the operation without scene scheduling are shown in Table 4.
Table4 scheduling operation results Table4 Results of scheduling runs for five scenarios for five scenarios
As can be seen from table4, compared with the first scenario, since the second scenario introduces P2G, the system equipment operation cost is increased by 18.3%; but the energy rejection cost of the second scene is reduced by 95.3 percent compared with that of the second scene, and the carbon emission is reduced by 4.9 percent. Therefore, the scene II effectively improves the wind and light absorption capacity and the carbon emission reduction capacity of the system. And by selling CH4 synthesized by P2G, the system is also benefited while reducing the air source investment.
Similarly, as the strong heat release characteristic of the P2G methanation process is considered in the third scene, the heat supply requirements of the CHP unit and the GB are reduced, the purchase amount of natural gas is reduced, and the carbon emission reduction level of a park is improved. In addition, due to the reduction of the heat supply requirement of the CHP unit, the electric energy output level is reduced, the output of a wind-solar system is promoted, and the energy discarding cost is further reduced. Compared with the second scene, the three-energy interaction cost, the energy discarding cost and the carbon emission are respectively reduced by 4.6%,63.1% and 2.9%. And in the third scene, the P2G methanation reaction heat is considered, so that the wind and light absorption can be promoted, the carbon emission of the system is reduced, and the energy interaction cost of the system is effectively reduced.
The carbon emissions and total cost for scenario four were reduced by 6.2% and 10.4%, respectively, compared to scenario three. The reason is that the traditional stepped carbon transaction mechanism is considered in the fourth scene, in order to avoid excessive carbon quota payment, the system strictly controls the thermal power output with higher carbon emission level, improves the output proportion of the clean energy unit, and excites the CHP unit and GB with relatively lower carbon emission level to increase the output so as to reduce the carbon emission of the system. And the free carbon quota obtained by the system greatly reduces the carbon transaction cost, and the total cost of the system is also reduced.
Compared with the fourth scene, the total cost and the system carbon emission of the scene are respectively reduced by 2.3 percent and 7.4 percent. Because the compensation coefficient is introduced into the stepwise carbon transaction mechanism model constructed in the scene of the invention, when the actual carbon emission is lower than the free carbon quota, a certain market subsidy can be obtained, thereby improving the enthusiasm of energy conservation and emission reduction in the park, and promoting the system to increase the output ratio of the low-carbon units so as to reduce the carbon emission; meanwhile, under the compensation mechanism, the park is willing to sell redundant carbon quota to obtain market subsidies.
Compared with the first scene, the energy interaction cost, the energy rejection cost, the carbon emission and the total cost of the scene are respectively reduced by 3.2%, 98.3%, 19.7% and 15.4%. Because the scene of the invention considers P2G heat supply, the energy interaction cost and the energy discarding cost of the system are obviously reduced; meanwhile, a stepped carbon transaction mechanism containing compensation coefficients is introduced, a park is encouraged to implement a low-carbon strategy, the carbon emission reduction capability of the system is obviously improved, and the mechanism is combined with carbon benefits, so that the total cost is reduced.
As shown by the comparative analysis, the CIES can effectively improve the utilization efficiency of energy sources and reduce the carbon emission by considering the reaction heat supply and the stepwise carbon transaction mechanism containing the compensation coefficient in the P2G methanation process.
(3) The scene scheduling result analysis of the invention is described as follows:
and (3) taking the reaction heat supply and the stepwise carbon transaction mechanism containing the compensation coefficient in the P2G methanation process into consideration, and carrying out optimal scheduling on CIES to obtain electric and thermal load scheduling results in the scene of the invention, wherein the electric and thermal load scheduling results are respectively shown in figures 7 and 8.
As can be seen from fig. 7, at the time of 00:00-07:00, the electricity price is low, the PV has no output, and the electricity demand of the user is jointly satisfied by the WT, the CHP unit and the grid electricity purchasing; at the moment 08:00-17:00, the electricity price is high, under the excitation of a stepped carbon transaction mechanism with compensation coefficients, clean energy units such as WT (WT), PV (photovoltaic) and the like and a CHP (chemical mechanical polishing) unit with low unit carbon emission level are mainly utilized by the system for supplying power, wind and light resources are rich at the moment, the energy supply is far greater than the load demand, and redundant power is utilized by P2G equipment to realize electrical conversion, so that the wind and light absorption level of the system is greatly improved while gas selling benefits are brought to the system; BT is applied to store electric energy at the time of 12:00-15:00 full of wind and light; at the moment of 18:00-21:00, no PV output is generated, the output of the CHP unit is gradually increased, and the power balance is met together with the electric quantity stored by the WT and the BT and the power grid purchase; at the moment of 22:00-24:00, the night wind energy resources are rich, WT is used as a power supply main force and the CHP unit to meet the power consumption requirement of a user, and redundant electric energy is used for electric conversion of P2G equipment and BT electric energy storage.
As can be seen from fig. 8, at the time of 00:00-05:00, the heat demand at night is low, the system provides heat energy by using GB with higher heating efficiency, and the surplus heat energy is provided for HST for heat storage. At the moment of 06:00-07:00, the heat load gradually rises, the CHP unit is started to be in a working mode of heat electricity fixation, the system mainly comprises the CHP unit and GB to meet the heat demand of users, and the insufficient part is complemented by heat energy stored by the HST; at the moment 08:00-17:00, the daytime heat load is in the peak period, the main heating force of the system is still CHP units and GB, and the P2G equipment performs electric conversion at the moment, and reaction waste heat released in the methanation process is also injected into a heat supply network for heating, so that the heating pressure of the CHP units and GB is relieved to a certain extent; meanwhile, the HST releases the heat energy stored previously in the time period, and all units cooperatively operate to meet the heat energy requirement of a user; at the moment of 18:00-22:00, the power consumption of a user is large, the CHP unit is started in an electric heating working mode, most of heat load is provided by the CHP unit, the rest is supplemented by GB, and redundant heat energy is used for HST heat storage; at the moment of 23:00-24:00, the requirements on electric and thermal loads are low, electric energy and heat energy required by users are mainly provided by WT and GB, and the output of the CHP unit is greatly reduced.
5) The sensitivity analysis is described as follows:
(1) the impact of natural gas price fluctuations on low carbon operation of the system is described as follows:
FIG. 9 is a graph showing the effect of natural gas price fluctuations on low carbon operation of the system in the context of the present invention. As can be seen from fig. 9, as the price of natural gas increases, the carbon emissions from the system tend to decrease, and the carbon return to the campus increases. The system mainly utilizes the gas consumption equipment CHP unit and GB as energy supply main force when the price of the natural gas is low, and the system needs to limit the output of the thermal power unit with higher carbon emission under the low-carbon emission reduction strategy of a park along with the fact that the price of the natural gas is far higher than the power supply cost, so that the output ratio of the clean energy unit can be improved, and the carbon emission of the system is reduced. Furthermore, when the price of natural gas increases to 2.5 yuan m 3-1 When the natural gas price is continuously increased, the carbon emission amount of the system is not obviously changed any more.
(2) The effect of carbon trade prices on low carbon operation of the system is described as follows:
the carbon trade price is equivalent to the weight of a low-carbon target in CIES optimization operation, and the change of the carbon trade price influences the output of a unit, thereby influencing the carbon emission of the system. As can be seen from fig. 10, as the price of carbon trade increases, the cost of carbon trade increases in proportion to the total cost, which results in a system that enforces a limit on carbon emissions, which results in a gradual decrease in carbon emissions. In addition, the carbon emission in the scene of the invention is lower than that in the scene four, because the stepped carbon transaction mechanism introducing the compensation coefficient has the advantage of emission reduction compared with the traditional carbon transaction mechanism, and the carbon emission of the system can be better limited.
(3) The effect of the compensation factor on the low carbon operation of the system is described as follows:
according to the invention, the compensation coefficient alpha is introduced to improve the low-carbon emission reduction enthusiasm of the park, and as shown by the analysis of fig. 11, when the carbon transaction cost of the system is positive, namely the park needs to pay carbon transaction fees, the compensation coefficient cannot play a role. When the carbon transaction cost of the system is negative, and the park starts to obtain carbon benefits, the larger the compensation coefficient is, the more the carbon benefits are obtained, namely the more considerable the carbon emission of the system is reduced, because the system takes the low-carbon unit CHP units and GB as energy sources under the low-carbon emission reduction strategy, and the carbon emission is effectively reduced. In addition, when the carbon trade price increases to 0.25 yuan kg -1 And when the carbon trade income trend is slow, the low-carbon unit output reaches the full-distribution state, and if the carbon trade price is continuously increased, the system carbon emission is not obviously changed.
By adopting the method, firstly, the operation process of the P2G is fully analyzed, a P2G two-stage operation model is finely constructed, the strong exothermic reaction characteristic of the P2G methanation process is focused, the heat supply capacity of the reaction characteristic is analyzed, and the influence of the heat supply of the reaction in the P2G methanation process on CIES operation scheduling results is researched and considered; secondly, aiming at the situation of carbon quota redundancy existing in the actual situation, a compensation coefficient is introduced on the basis of the existing stepped carbon transaction mechanism, and a CIES stepped carbon transaction mechanism model containing the compensation coefficient is established. Based on the method, the CIES low-carbon optimized scheduling method considering the P2G methanation process reaction heat supply and the stepwise carbon transaction mechanism with the compensation system is provided, the method can effectively improve the utilization efficiency of CIES on energy, fully reduce carbon emission of a park, and realize low-carbon development of the park.
Claims (10)
1. A low-carbon optimal scheduling method for a park comprehensive energy system is characterized by comprising the following steps:
step 1: building a unit operation model of a cogeneration unit, a gas boiler, energy storage equipment and the like contained in the park comprehensive energy system, thereby building a park comprehensive energy system operation topological structure;
step 2: finely building an electric conversion gas two-stage operation model, and building a reaction heat energy supply model in the electric conversion gas methanation process based on the electric conversion gas two-stage operation model;
step 3: establishing a park comprehensive energy system ladder-type carbon transaction mechanism model containing compensation coefficients based on the existing carbon transaction mechanism;
step 4: and (3) taking the minimum running cost, energy discarding power and carbon emission as objective functions, establishing a park comprehensive energy system optimizing running model based on the step (2) and the step (3), designing constraint conditions of the model, and carrying out multi-objective optimizing calculation on the model by using a CPLEX solver.
2. The method according to claim 1, characterized in that: the step 1 specifically comprises the following steps:
step 1) building a cogeneration unit operation model
The cogeneration unit is composed of a gas turbine and a waste heat boiler, the gas turbine generates power by combusting natural gas, the discharged waste heat is recovered by the waste heat boiler and used for supplying heat, and the operation model is shown in the formula (1):
Wherein: p (P) CHP,e (t)、P CHP,h (t) -power supply and heat supply power of the CHP unit at the moment t, and kW; q (Q) GT (t) -exhaust residual heat of the gas turbine at the moment t, kW; η (eta) e -power generation effect of gas turbineA rate; h L -natural gas low heating value, kWh/m 3 ，H L ＝9.7kWh/m 3 ；G CHP,g (t) -the gas consumption of the CHP unit at the moment t, m 3 ；η loss -heat dissipation loss rate; η (eta) h -waste heat recovery efficiency; c (C) OP,h -the flue gas recovery rate of the waste heat boiler;
step 2) establishing a gas boiler unit operation model
The gas boiler is used for supplying heat by combusting natural gas, and the operation model is shown as formula (2):
H gb (t)＝η gb,h H L G gb,g (t) (2)
wherein: h gb (t) -heating power of GB at t time, kW; η (eta) gb,h -heating efficiency of the gas boiler; g gb,g (t) -the gas consumption at time GB, m 3 ；
Step 3) establishing an operation model of the energy storage equipment unit
The energy storage device mainly comprises a storage battery and a heat storage tank, and an operation model is shown in formulas (3) - (4):
wherein:-state of charge of the battery at time t; />Charging and discharging power of the storage battery at the moment t, and kW; η (eta) bt,chg 、η bt,dis -the charge and discharge efficiency of the battery at time t; />-thermal energy of the thermal storage tank at time t; -heat storage and release power of the heat storage tank at the moment t and kW; gamma ray h -energy self-loss rate of the heat storage tank; η (eta) hst,chg 、η hst,dis -heat storage and release efficiency of the heat storage tank.
3. The method according to claim 1, characterized in that: the step 2 specifically comprises the following steps:
step 1) analyzing an electric conversion gas two-stage operation mechanism, namely two stages of water electrolysis hydrogen production and methanation, wherein the water electrolysis hydrogen production is a front reaction of methanation, and an electric conversion gas two-stage operation model is built based on the front reaction;
step 2) paying attention to the strong exothermic reaction characteristic of the electric shift gas methanation process, analyzing the heat supply capacity of the reaction characteristic, and establishing a reaction heat energy supply model of the electric shift gas methanation process based on the heat supply capacity;
in the step 1), the steps for finely constructing the electric conversion gas two-stage operation model are as follows:
(1) establishing a proton exchange membrane electrolyzer device operation model:
wherein:-hydrogen energy output by the electrolyzer at time t, m 3 ；P P2G (t) -the power consumption of electric conversion gas at the moment t, and kW; η (eta) EW Hydrogen production efficiency for water electrolysis; />-upper and lower limit values of power consumption of the electrolytic tank equipment, kW;
(2) and (3) establishing a methane reactor operation model:
wherein:-natural gas amount and hydrogen consumption amount output by methane reactor at time t, m 3 ；η MR -energy conversion efficiency of the methane reactor; />-upper and lower limit value of hydrogen consumption of methane reactor, m 3 ；
In the step 2), the steps for establishing the reaction heat energy supply model of the electric conversion gas methanation process are as follows:
(1) Analyzing the thermochemical reaction formula of the methanation process, 165.01kJ of heat is released per 1mol of methane generated in the electric shift gas methanation process, as shown in formula (7):
wherein: ΔH-entropy of chemical reaction, kJ/mol, when the value is negative, represents exotherm;
(2) analysis of conversion relation between methane yield of electric conversion gas and methanation reaction heat release, and consideration of H generated by hydrogen production by water electrolysis 2 All the methods are used for methanation, and the conversion relation between the methane yield of the electric conversion gas and the heat release of the methanation reaction in unit time is shown in the following formulas (8) - (9):
wherein:——CH 4 yield, m 3 /MWh；/>Reaction heat power, kW, of methanation process under unit power consumption of electricity-to-gas conversion; />——H 2 Yield of Nm 3 /kWh；/>——H 2 、CH 4 Density, g/Nm 3 ；/>——H 2 、CH 4 G/mol; />-specific gravity of heat release amount for heat supply during electric conversion gas methanation process, < ->
(3) Because of the heat accumulation property of the heating power network, the methanation reaction heat is directly injected into the heating power network for heat supply, and then the energy supply model of the electric conversion gas methanation process reaction heat is shown as a formula (10):
wherein: h MR And (t), namely the reaction heat supply power in the electric gas-to-methanation process at the moment of t, and kW.
4. The method according to claim 1, characterized in that: the step 3 specifically comprises the following steps:
step 1) initial amount of carbon emission of distribution system
The initial carbon emission quota of the system is determined by adopting a datum line method, the carbon emission source of the system mainly comprises outsourcing power, a gas boiler and a cogeneration unit, and the cogeneration unit can provide electric energy and heat energy simultaneously, so that the generated energy of the cogeneration unit is converted into equivalent generated heat to perform initial allocation of carbon emission, and the initial allocation model of the carbon emission of the system is as follows:
wherein: e (E) a -initial carbon quota of system, kg; delta e 、δ h -unit power supply, heat supply output carbon quota coefficient, kg/kWh; delta e ＝0.728kg/kWh，δ h ＝0.367kg/kWh；P grid (t) -the system outsourcing electric power at the moment t, kW; lambda-conversion coefficient of converting the generated energy of the cogeneration unit into heat supply quantity, MJ/kWh, wherein lambda=6MJ/kWh;
step 2) determining the actual carbon emissions of the system
Methane reactors consume CO 2 Therefore, the carbon emission is negative, and the actual carbon emission model of the system is shown in the formula (12):
wherein: e (E) P -actual carbon emissions of the system, kg;-grid emission factor, kg/kWh,/->And updating in real time according to the latest numerical value issued by the ecological environment part; />-carbon emission coefficient of natural gas, +.>Omega-methane reactor for absorbing CO 2 Coefficients of (2);
step 3) establishing a carbon emission transaction model
Based on the existing carbon transaction mechanism, in order to further restrict the carbon emission of the park, a compensation coefficient alpha is introduced, namely, when the actual carbon emission is lower than the initial quota, a certain subsidy is given to the market as compensation, and based on the compensation coefficient alpha, a stepped carbon transaction mechanism model containing the compensation coefficient is constructed, as shown in a formula (13):
E IES ＝E P -E a (14)
Wherein: e (E) IES Actual carbon emission E of the system P And initial carbon quota E a Kg of the difference in (d);-the carbon transaction cost of the system at time t, wherein the element represents the purchase amount positively and the element represents the utilization of the residual amount negatively to obtain the subsidy; k-carbon trade basal price on the same day, yuan/kg; alpha-compensation coefficient; beta-price increase amplitude; d-the carbon emission interval length corresponding to each step, kg.
5. The method according to claim 1, characterized in that: the step 4 specifically comprises the following steps:
step 1) converting the abandoned energy power and the carbon emission into abandoned energy punishment cost and carbon transaction cost respectively by introducing unit abandoned energy punishment cost and a stepped carbon transaction mechanism containing compensation coefficients, and constructing to run cost F 1 Cost F of discarding energy penalty 2 Carbon trade cost F 3 Optimizing an operational model for the targeted campus integrated energy system to convert the multi-objective optimization to a single-objective optimization, as shown in equation (15):
min F＝F 1 +F 2 +F 3 (15)
step 2) designing constraint conditions of an optimization operation model of the park comprehensive energy system;
step 3) combining the step 1) and the step 2), and solving the park comprehensive energy system optimization operation model by using an optimization solver CPLEX.
6. The method according to claim 5, wherein: running cost F 1 Cost F of discarding energy penalty 2 Carbon trade cost F 3 The method comprises the following steps:
1) Running cost F 1
Running cost F 1 Energy interaction cost C comprising system and power grid and natural gas company buy Cost of equipment operation and maintenance C O Energy selling cost C sell As shown in formula (16):
wherein: c (C) e -price per unit purchase of electricity, yuan/kWh; c (C) g 、C gs Unit purchase, gas selling price, yuan/m 3 ；G g,buy (t)、G g,sell (t) -the outsourcing and gas selling amount of the system at the moment of t, m 3 ；K i Unit operation maintenance cost of equipment i, yuan/kWh; p (P) i (t) -working power of equipment i at time t, kW;
2) Cost of discarding energy F 2
The energy discarding cost comprises two parts of wind discarding cost and light discarding cost, as shown in a formula (17):
wherein: p (P) WT (t)、P PV (t) -output power of wind power and photovoltaic at t moment, kW; p (P) wt (t)、P pv (t) -the actual wind power and photovoltaic power generation power at the moment t, kW;-unit wind-abandoning and light-abandoning punishment costs, yuan/kWh;
3) Carbon trade cost F 3
Wherein:the detail is shown in formula (13).
7. The method according to claim 5, wherein: designing constraint conditions of an optimization operation model of a park comprehensive energy system, which specifically comprises the following steps:
1) Operation constraint of cogeneration unit
In actual operation, the cogeneration unit needs to meet the operation power constraint and the climbing rate constraint, as shown in formula (19):
Wherein:the power supply quantity of the cogeneration unit is up and downLimit value, kW; />-upper and lower limit values of climbing rate of the cogeneration unit, kWh;
2) Gas boiler operation constraint
The gas boiler operation constraint is as shown in formula (20):
wherein:-upper and lower limit values of heat supply quantity of the gas boiler, kW;
3) Energy storage device operation constraints
When the storage battery and the heat storage tank run, capacity constraint, mutual exclusion constraint, charging and discharging constraint and climbing constraint are required to be met, as shown in formulas (21) - (22):
wherein:-0-1 variable representing the charge and discharge status flag bits of the battery and the thermal storage tank; />And->Storage battery and heat storage tankCharging and discharging power upper and lower limit values, kW; />And->-upper and lower limit values of the charging and discharging climbing rate of the storage battery and the heat storage tank, kWh;
4) Electric power balance constraint
Considering the situation that the power distribution network does not allow reverse power transmission in the actual industry, the invention does not consider the situation that the system sells electricity to a power grid company, and the electric power balance constraint is as shown in a formula (23):
wherein:-the limit value of the outsourcing electric quantity of the system in each period of time, kW; p (P) eload (t) -user electricity consumption at the moment t, kW;
5) Thermal power balance constraint
H gb (t)+P CHP,h (t)+H MR (t)+P hst,dis (t)＝H load (t)+P hst,chg (t) (24)
Wherein: h load And (t) -heat used by a user at the moment t, and kW.
8. The method according to claim 5, wherein: the nonlinear part related to the carbon transaction mechanism in the step 3 is subjected to relaxation treatment by adopting a large M method, so that the nonlinear part is converted into a mixed integer linear model; then, establishing a park comprehensive energy system optimization operation model by utilizing a Matlab environment and utilizing a Yalmip tool box; and finally, calling an optimization solver CPLEX to solve.
9. The method for establishing the reaction heat energy supply model in the electric conversion gas methanation process is characterized by comprising the following steps of:
step 1) analyzing an electric conversion gas two-stage operation mechanism, namely two stages of water electrolysis hydrogen production and methanation, wherein the water electrolysis hydrogen production is a front reaction of methanation, and an electric conversion gas two-stage operation model is built based on the front reaction;
and 2) paying attention to the strong exothermic reaction characteristic of the electric transfer gas methanation process, analyzing the heat supply capacity of the reaction characteristic, and establishing a reaction heat energy supply model of the electric transfer gas methanation process based on the heat supply capacity.
10. The method according to claim 9, wherein in step 1), the steps in the fine-building of the electric-to-gas two-stage operation model are as follows:
(1) establishing a proton exchange membrane electrolyzer device operation model:
wherein: -hydrogen energy output by the electrolyzer at time t, m 3 ；P P2G (t) -the power consumption of electric conversion gas at the moment t, and kW; η (eta) EW Hydrogen production efficiency for water electrolysis; />-upper and lower limit values of power consumption of the electrolytic tank equipment, kW;
(2) and (3) establishing a methane reactor operation model:
wherein:-time tNatural gas amount and hydrogen consumption amount output by methane reactor, m 3 ；η MR -energy conversion efficiency of the methane reactor; />-upper and lower limit value of hydrogen consumption of methane reactor, m 3 ；
In the step 2), the steps for establishing the reaction heat energy supply model of the electric conversion gas methanation process are as follows:
(1) analyzing the thermochemical reaction formula of the methanation process, 165.01kJ of heat is released per 1mol of methane generated in the electric shift gas methanation process, as shown in formula (3):
wherein: ΔH-entropy of chemical reaction, kJ/mol, when the value is negative, represents exotherm;
(2) analysis of conversion relation between methane yield of electric conversion gas and methanation reaction heat release, and consideration of H generated by hydrogen production by water electrolysis 2 All the methods are used for methanation, and the conversion relation between the methane yield of the electric conversion gas and the heat release of the methanation reaction in unit time is shown in the following formulas (4) - (5):
wherein:——CH 4 yield, m 3 /MWh；/>Reaction heat power, kW, of methanation process under unit power consumption of electricity-to-gas conversion; / >——H 2 Yield of Nm 3 /kWh；/>——H 2 、CH 4 Density, g/Nm 3 ；/>——H 2 、CH 4 G/mol; />-specific gravity of heat release amount for heat supply during electric conversion gas methanation process, < ->
(3) Because of the heat accumulation property of the heating power network, the methanation reaction heat is directly injected into the heating power network for heat supply, and then the energy supply model of the electric conversion gas methanation process reaction heat is shown as a formula (6):
wherein: h MR And (t), namely the reaction heat supply power in the electric gas-to-methanation process at the moment of t, and kW.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310428741.9A CN116596123A (en) | 2023-04-20 | 2023-04-20 | Low-carbon optimal scheduling method for park comprehensive energy system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310428741.9A CN116596123A (en) | 2023-04-20 | 2023-04-20 | Low-carbon optimal scheduling method for park comprehensive energy system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116596123A true CN116596123A (en) | 2023-08-15 |
Family
ID=87603554
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310428741.9A Pending CN116596123A (en) | 2023-04-20 | 2023-04-20 | Low-carbon optimal scheduling method for park comprehensive energy system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116596123A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117094745A (en) * | 2023-09-06 | 2023-11-21 | 天津大学 | Comprehensive energy system optimization control method and device based on IGDT-utility entropy |
-
2023
- 2023-04-20 CN CN202310428741.9A patent/CN116596123A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117094745A (en) * | 2023-09-06 | 2023-11-21 | 天津大学 | Comprehensive energy system optimization control method and device based on IGDT-utility entropy |
| CN117094745B (en) * | 2023-09-06 | 2024-03-12 | 天津大学 | Comprehensive energy system optimization control method and device based on IGDT-utility entropy |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113315242B (en) | Virtual wind abandoning-hydrogen production combination for promoting wind abandoning consumption based on hydrogen energy economy | |
| CN114091913B (en) | Low-carbon economic dispatching method considering heat supply network and P2G multi-park comprehensive energy system | |
| CN106372742A (en) | Power-to-gas multi-source energy storage type microgrid day-ahead optimal economic dispatching method | |
| CN103544372B (en) | A kind of Unit Selection method of miniature gas turbine cogeneration system | |
| CN104537443A (en) | Co-generation type micro-grid economy coordination and optimization dispatching method | |
| CN112598224A (en) | Interactive game scheduling method for park comprehensive energy system group and power grid | |
| CN110930073B (en) | Day-ahead scheduling method for wind-light-photo-thermal combined power generation system considering price type demand response | |
| CN113078687B (en) | Energy optimization scheduling method for island multi-energy complementary electricity-gas coupling system | |
| CN111668878A (en) | Optimal configuration method and system for renewable micro-energy network | |
| CN113159407A (en) | Multi-energy storage module capacity optimal configuration method based on regional comprehensive energy system | |
| CN115238987A (en) | Energy efficiency improvement scheduling method considering multi-type low-carbon factors and demand response | |
| Qin et al. | Robust optimal dispatching of integrated electricity and gas system considering refined power-to-gas model under the dual carbon target | |
| CN114936720A (en) | Low-carbon economic dispatching method for source-load-storage coordinated comprehensive energy system | |
| CN110957722B (en) | Day-ahead optimal scheduling method for micro energy network with electricity-to-gas equipment | |
| CN116596123A (en) | Low-carbon optimal scheduling method for park comprehensive energy system | |
| CN217215988U (en) | Hydrogen-electricity deep-coupling intelligent micro-grid bionic energy system | |
| Li et al. | Capacity coordinated optimization of battery, thermal and hydrogen storage system for multi-energy complementary power system | |
| CN114676897A (en) | Optimal scheduling method for comprehensive energy system of park containing CHP-P2G-hydrogen energy | |
| CN115660142A (en) | Source-load-storage coordination optimization scheduling method for park comprehensive energy system | |
| CN115986833A (en) | Low-carbon economic scheduling method for combined heat and power micro-grid considering two-stage demand response | |
| CN115860178A (en) | Regional comprehensive energy operation strategy considering waste treatment and methanol synthesis | |
| CN114530882A (en) | Energy supply cooperative autonomous method and system for multiple distributed energy sources | |
| Li et al. | Energy system and scheduling strategies of electric–gas networks deeply coupled under dual electricity prices | |
| Chen et al. | An integrated energy system optimization model coupled with power-to-gas and carbon capture | |
| Liu et al. | Day-ahead economic dispatch of integrated energy system including power to gas |
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 |