CN117035331A - Dynamic energy efficiency considered hydrogen-containing comprehensive energy optimization scheduling method and device - Google Patents
Dynamic energy efficiency considered hydrogen-containing comprehensive energy optimization scheduling method and device Download PDFInfo
- Publication number
- CN117035331A CN117035331A CN202311035665.1A CN202311035665A CN117035331A CN 117035331 A CN117035331 A CN 117035331A CN 202311035665 A CN202311035665 A CN 202311035665A CN 117035331 A CN117035331 A CN 117035331A
- Authority
- CN
- China
- Prior art keywords
- energy
- hydrogen
- power
- efficiency
- load
- 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
- UFHFLCQGNIYNRP-UHFFFAOYSA-N Hydrogen Chemical compound [H][H] UFHFLCQGNIYNRP-UHFFFAOYSA-N 0.000 title claims abstract description 173
- 229910052739 hydrogen Inorganic materials 0.000 title claims abstract description 172
- 239000001257 hydrogen Substances 0.000 title claims abstract description 170
- 238000005457 optimization Methods 0.000 title claims abstract description 60
- 238000000034 method Methods 0.000 title claims abstract description 38
- VNWKTOKETHGBQD-UHFFFAOYSA-N methane Chemical compound C VNWKTOKETHGBQD-UHFFFAOYSA-N 0.000 claims abstract description 146
- 239000003345 natural gas Substances 0.000 claims abstract description 38
- 239000007789 gas Substances 0.000 claims description 78
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 claims description 60
- 229910052799 carbon Inorganic materials 0.000 claims description 60
- 238000004146 energy storage Methods 0.000 claims description 54
- 239000000446 fuel Substances 0.000 claims description 31
- 239000002918 waste heat Substances 0.000 claims description 24
- 238000006243 chemical reaction Methods 0.000 claims description 22
- 238000003860 storage Methods 0.000 claims description 21
- 230000005611 electricity Effects 0.000 claims description 20
- 238000010521 absorption reaction Methods 0.000 claims description 18
- 238000010248 power generation Methods 0.000 claims description 15
- 238000010438 heat treatment Methods 0.000 claims description 14
- 238000011084 recovery Methods 0.000 claims description 9
- 238000005057 refrigeration Methods 0.000 claims description 8
- 238000007599 discharging Methods 0.000 claims description 7
- 230000008569 process Effects 0.000 claims description 7
- 238000005265 energy consumption Methods 0.000 claims description 6
- 238000012423 maintenance Methods 0.000 claims description 6
- 230000009194 climbing Effects 0.000 claims description 5
- 238000005338 heat storage Methods 0.000 claims description 5
- 238000010276 construction Methods 0.000 claims description 4
- 238000005868 electrolysis reaction 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
- 238000006073 displacement reaction Methods 0.000 claims description 3
- 238000005485 electric heating Methods 0.000 claims description 3
- 239000003546 flue gas Substances 0.000 claims description 3
- 238000004519 manufacturing process Methods 0.000 description 6
- 230000001737 promoting effect Effects 0.000 description 4
- 230000007547 defect Effects 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 230000008878 coupling Effects 0.000 description 2
- 238000010168 coupling process Methods 0.000 description 2
- 238000005859 coupling reaction Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 230000007613 environmental effect Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000000052 comparative effect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 239000002803 fossil fuel Substances 0.000 description 1
- 239000005431 greenhouse gas Substances 0.000 description 1
- 230000001105 regulatory effect Effects 0.000 description 1
- 239000002699 waste material Substances 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
- G06Q10/06—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
- G06Q10/06312—Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
-
- 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
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Human Resources & Organizations (AREA)
- General Physics & Mathematics (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- General Engineering & Computer Science (AREA)
- Marketing (AREA)
- Entrepreneurship & Innovation (AREA)
- Health & Medical Sciences (AREA)
- Tourism & Hospitality (AREA)
- Data Mining & Analysis (AREA)
- General Business, Economics & Management (AREA)
- Mathematical Physics (AREA)
- Public Health (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Computer Hardware Design (AREA)
- Evolutionary Computation (AREA)
- Geometry (AREA)
- Quality & Reliability (AREA)
- Algebra (AREA)
- Computational Mathematics (AREA)
- Educational Administration (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- Development Economics (AREA)
- Pure & Applied Mathematics (AREA)
- Databases & Information Systems (AREA)
- Software Systems (AREA)
- Fuel Cell (AREA)
Abstract
The invention discloses a hydrogen-containing comprehensive energy optimization scheduling method considering dynamic energy efficiency, which comprises the following steps: constructing a hydrogen-containing comprehensive energy system model; based on the hydrogen-containing comprehensive energy system model, constructing a hydrogen-containing comprehensive energy optimization scheduling model considering dynamic energy efficiency; the hydrogen-containing comprehensive energy optimization scheduling model comprises an objective function and constraint conditions; according to the purchased power, the wind power output power and the purchased natural gas quantity obtained at different moments in a day, solving the hydrogen-containing comprehensive energy optimization scheduling model to obtain a hydrogen-containing comprehensive energy optimization scheduling scheme; the invention builds the comprehensive energy operation model based on the energy cascade utilization principle, and improves the optimal comprehensive energy scheduling method of the system operation efficiency.
Description
Technical Field
The invention relates to a hydrogen-containing comprehensive energy optimization scheduling method and device considering dynamic energy efficiency, and belongs to the technical field of energy.
Background
Compared with a single energy system, the renewable energy integrated power grid, multi-energy coupling and energy cascade utilization integrated energy system can effectively improve energy utilization efficiency, promote renewable energy consumption, reduce emission of greenhouse gases and alleviate fossil fuel energy consumption, and is an important direction for the development of the future energy field. The hydrogen energy is used as high-efficiency clean energy, has high heat value, no carbon emission and is convenient to store.
Currently, offshore wind power resources in coastal areas are rich, an integrated energy system containing hydrogen can utilize electric conversion equipment to convert surplus wind power into hydrogen energy for utilization, and the problem of wind abandoning caused by wind power output fluctuation is solved; meanwhile, the hydrogen energy can be transmitted to the hydrogen fuel cell and the methane reactor to generate electric energy, heat energy and natural gas for the comprehensive energy system to use, so that the flexibility of energy utilization and the comprehensive energy operation efficiency of the industrial park containing the electricity for hydrogen production are improved. The operation efficiency of the comprehensive energy system equipment is easily influenced by the load rate and the environmental change, and in the aspect of modeling the comprehensive energy system equipment, the operation efficiency of the equipment is mostly set as a constant, the influence of the equipment efficiency change on the comprehensive energy system is neglected, and the technical invention patent CN115660142A is a method for coordinated and optimized scheduling of the energy and the charge storage of the comprehensive energy system in a park, which provides an electric-thermal-gas park comprehensive energy system comprising electric conversion gas, wind turbines, photovoltaic turbines, cogeneration units and energy storage equipment.
Aiming at the problems, a hydrogen-containing comprehensive energy system considering dynamic energy efficiency is established in an offshore industrial park, so that the problem of excessive wind power absorption is solved, and the system operation efficiency and the energy utilization rate are improved.
Disclosure of Invention
The invention aims to provide a hydrogen-containing comprehensive energy optimization scheduling method and device considering dynamic energy efficiency, so as to solve the defects of insufficient utilization of offshore wind power and low utilization efficiency of a comprehensive energy system in the prior art.
A hydrogen-containing comprehensive energy optimization scheduling method considering dynamic energy efficiency, the method comprising:
constructing a hydrogen-containing comprehensive energy system model;
based on the hydrogen-containing comprehensive energy system model, constructing a hydrogen-containing comprehensive energy optimization scheduling model considering dynamic energy efficiency; the hydrogen-containing comprehensive energy optimization scheduling model comprises an objective function and constraint conditions;
and solving the hydrogen-containing comprehensive energy optimization scheduling model according to the purchased electric power, the wind power output power and the purchased natural gas quantity which are acquired at different moments in one day to obtain a hydrogen-containing comprehensive energy optimization scheduling scheme.
Further, the hydrogen-containing comprehensive energy system model comprises electric energy, natural gas, an energy utilization module and an energy storage module, wherein the energy utilization module comprises a gas turbine, a waste heat boiler, a gas boiler, an absorption refrigerator, a peak heater, an electric heat pump, an electric refrigerator, an electrolytic tank, a methane reactor and a hydrogen fuel cell, and the energy storage module comprises an electric storage device, a heat storage module, a cold storage device and a hydrogen storage device, and divides an energy load into an electric load, a cold load, a steam load, a heating load, a high-temperature load and a medium-temperature load according to the energy grade;
the gas turbine burns natural gas to generate electric energy and steam heat energy, and the waste heat boiler heats part of waste heat flue gas to supply medium-temperature load; the gas boiler burns natural gas to generate steam heat energy; the absorption refrigerator consumes steam to generate cold energy, and the peak heater supplies high-temperature load by using the steam; the traditional P2G equipment is divided into an electrolytic tank and a methane reactor, wherein the electrolytic tank is used for electrolyzing electric energy into hydrogen for a hydrogen storage tank, a hydrogen fuel cell and the methane reactor; and (3) recovering waste heat of the methanation reaction of the hydrogen and supplying heat for heating load by using a hydrogen fuel cell.
The electrolysis process of the electrolytic cell for electrolyzing electric energy into hydrogen is shown as a formula (1):
wherein:outputting hydrogen power for the t-period electrolytic cell; q (Q) P2G (t) is t period P2G waste heat recovery power; η (eta) EL (t) and eta hP2G Respectively the conversion efficiency and the waste heat recovery efficiency of the electrolytic cell; p (P) EL,in (t) is the electric power at the input side of the electrolytic cell, P ELN Rated power generation power for the gas turbine; i represents the polynomial fitting order of the efficiency function; n represents the highest degree of the efficiency function polynomial; beta EL,i Representing the ith order polynomial fit factor of the cell.
The electrolytic hydrogen is used for a fuel cell and a methane reactor, and the electrolytic hydrogen is used for the methane reactor and is shown as a formula (2):
wherein:natural gas power generated for the methane reactor for the period t; />Consuming hydrogen power for the methane reactor t period; η (eta) MR The conversion efficiency between the hydrogen and the natural gas of the methane reactor is improved;
the electrolytic hydrogen gas is used for a hydrogen fuel cell as shown in formula (3):
wherein: p (P) HFC (t) and Q HFC (t) the output electric power and the output thermal power of the hydrogen fuel cell at the time t respectively;inputting hydrogen power for the hydrogen fuel cell at the time t; />Generating efficiency for hydrogen fuel cell, +.>Is the heat generating efficiency of the hydrogen fuel cell.
Further, the model formula of the gas turbine is:
wherein: η (eta) GT (t) is the power generation efficiency of the gas turbine at the moment t; p (P) GT (t) outputting electric power for the moment t of the gas turbine; p (P) GTN Rated power generation power for the gas turbine; i represents the polynomial fitting order of the efficiency function; n represents the highest degree of the efficiency function polynomial; beta GT,i The ith order polynomial fitting factor of the gas turbine is represented, and h is the lower calorific value of the natural gas; g GT (t) is the air inflow of the gas turbine at the moment t;
the model formula of the system gas boiler is as follows:
wherein: η (eta) GB (t) is the heating efficiency of the gas boiler at the moment t; q (Q) GB (t) is the thermal power output by the gas turbine at the moment t, Q GBN Rated power value of the gas boiler; g GB (t) is the air inflow of the gas turbine at the moment t; beta GB,i Representing an ith order polynomial fitting factor of the gas boiler;
the model formula of the electric refrigerator is as follows:
wherein: h EC (t) outputting cold power for the electric refrigerator at the moment t; h EC (t) is the refrigerating power of the electric refrigerator at the moment t, H ECN Rated refrigeration power at t moment of the electric refrigerator; p (P) EC (t) consuming electricity for the time t of the electric refrigeratorA power; beta EC,i An ith order polynomial fitting factor for the refrigeration energy efficiency ratio function;
the energy storage module equipment model formula is:
wherein: s is S x (t) and S x (t-1) is the energy storage state of the x-th type energy storage equipment at the time of t and t-1; beta is the self-loss coefficient of the energy storage equipment; p (P) x,c (t) is the charging power of the x-th type energy storage device at the time t, P x,d (t) discharging power at t for the x-th type energy storage device; η (eta) x,c Charge efficiency eta of the equipment x, d is the energy release efficiency of the equipment, delta t is a time interval, x is the type of the energy storage module equipment, and concretely comprises an electricity storage equipment ES, a heat storage equipment QS, a cold storage equipment HS and a hydrogen storage equipment H 2 S。
Further, the constraint conditions of the hydrogen-containing comprehensive energy optimization scheduling model considering dynamic energy efficiency include:
the requirements of electric load, cold load, steam load, heating load, high temperature load and medium temperature load supply and demand balance, natural gas balance and hydrogen balance are satisfied, as shown in formula (8):
wherein: p (P) EL,in (t) is an electrical load, H H,L (t) is a cold load, Q st,L (t) is steam load, Q W,L (t) is heating load, Q M,L (t) is a high temperature load, Q m,L (t) is a medium temperature load; p (P) WT (t) wind power generation; p (P) g,buy (t) is the natural gas purchase amount in the period t, P e,buy (t) is the electricity purchasing amount in the t period; q (Q) WHB,out (t) outputting heat power for the time t of the waste heat boiler; q (Q) EHP (t) is the output thermal power of the electrothermal pump at the moment t, P EHP (t) inputting electric power for the moment t of the electric heating pump; q (Q) PCA,out (t) peak heater output thermal power, Q PCA,in (t) peak heater output heatA power; q (Q) P2G (t) is the waste heat recovery power in the t period; h AB (t) and Q AB (t) outputting cold power and consumed hot power for the absorption refrigeration unit at the moment t; q (Q) GT,st (t) is the steam heat power output by the gas turbine;
the energy consumption module device operation constraint is as shown in formula (9):
wherein: s represents different energy using module equipment types, P s (t) and P s (t-1) represents the power of the device s at time t and time t-1, P s max For the upper limit of the output power of the device s, P s min A lower limit for the output power of the device s; ΔP s,up For the upper limit of the climbing rate of the equipment s, delta P s,down A lower limit on the climbing rate for the device s;
the energy storage constraint is shown in formula (10):
wherein: energy storage S at initial moment of scheduling device x (0) Energy storage S at the end time x (T) is the same, T is a scheduling period; s is S x,max Is the upper limit of the energy storage state, S x,min Is the lower limit of the energy storage state;representing an upper limit of charging power of the energy storage system; />Representing an upper limit of energy discharging power of the energy storage system; λ=1 represents energy storage system charging; λ=0 represents energy storage system disabling.
Further, the objective function of the hydrogen-containing comprehensive energy optimization scheduling model is shown as a formula (11):
wherein: f is the total objective function; c is the total cost of actual operation of the system, C 0 Running the total cost for the reference number; c (C) cut Punishment is carried out on the system; c (C) cut,o Punishment for the parametric number of wind curtailment; η is the actual energy utilization rate of the system; η (eta) o The energy utilization rate is the reference number; lambda (lambda) 1 、λ 2 And lambda (lambda) 3 Is the weight coefficient of each target, and lambda 1 +λ 2 +λ 3 ＝1。
Further, the actual running total cost is represented by formula (12):
C＝C om +C buy +C car (12) Wherein: c (C) om The operation and maintenance cost of the system; c (C) buy The energy purchasing cost is the external energy purchasing cost of the system; c (C) car Cost for carbon trade;
the external electricity purchase is provided for the coal-fired unit, and the total carbon emission quota in the carbon transaction cost is represented by formulas (13) - (14):
E IES ＝E e,buy +E GTU (13)
wherein: e (E) IES Is the total carbon emission allowance; e (E) e,buy For external grid carbon emission quota, E GTU Carbon emission quota for gas system; delta e,p Carbon emission allocation amount delta for unit electric quantity h,p Allocate carbon emissions per unit of heat; p (P) e,buy (t) is the electricity purchasing amount in the t period; p (P) GTU (t) outputting electric energy for the gas unit system in the period of t, Q GTU (t) outputting heat energy for the t-period gas turbine system;is an electro-thermal conversion coefficient;
in the actual carbon emission model, the methane reactor can absorb a part of CO in the process of converting hydrogen into natural gas 2 Actual carbon emission amount and carbon emissionThe cost is represented by the following formula:
E IES,a ＝E e,buy,a +E GTU,a -E MR,a (15)
E IES,t ＝E IES,a -E IES (18)
C car ＝P car E IES,t (19) Wherein: e (E) IES,a Actual carbon emission for the park; e (E) e,buy,a The actual carbon emission is the actual carbon emission of the external power grid; e (E) GTU,a E is the actual carbon emission of the gas unit system MR,a Carbon displacement for methane reactor absorption, a 1 、b 1 、c 1 Calculating parameters for carbon emission of the coal-fired unit; a, a 2 、b 2 、c 2 For the carbon emission calculation parameters of the gas turbine unit, P (T) is the equivalent output power of the gas turbine unit in the period T, T is the scheduling period, omega is the absorption of CO in the process of converting hydrogen energy into natural gas by MR equipment 2 Parameters, E IES,t For carbon emission trade amount, P car Trade price per carbon;
the wind curtailment cost is represented by formula (20):
wherein: delta cut Punishment cost for unit abandoned wind; p (P) WT,cut And (t) the wind power is abandoned in the period t.
Further, the system actual energy utilization rate is represented by formula (21):
wherein: lambda (lambda) e Is the electrical load energy coefficient; lambda (lambda) q Is the thermal load energy coefficient; lambda (lambda) h Is the cold load energy coefficient; lambda (lambda) E Is the conversion coefficient of the electric energy; lambda (lambda) G Is the conversion coefficient of natural gas; lambda (lambda) W The conversion coefficient of wind energy; l (L) Q And (t) is a thermal load.
Compared with the prior art, the invention has the beneficial effects that:
aiming at the problems of insufficient utilization of offshore wind power and low utilization efficiency of a comprehensive energy system, the invention provides an optimal scheduling method of hydrogen-containing comprehensive energy in consideration of dynamic energy efficiency, constructs a comprehensive energy operation model based on an energy cascade utilization principle, comprises an electric hydrogen production module, establishes an equipment model in consideration of the dynamic efficiency, and considers optimization targets of system operation cost, wind abandon punishment and energy utilization efficiency to form the optimal comprehensive energy scheduling method for promoting wind power consumption and improving the system operation efficiency.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a block diagram of an integrated energy system based on energy cascade utilization in accordance with the present invention;
FIG. 3 is a block diagram of hydrogen energy utilization in accordance with the present invention;
FIG. 4 is a graph of the optimization results of the hydrogen-containing integrated energy system of the present invention, taking into account dynamic efficiency;
FIG. 5 is a graph of hydrogen energy utilization in the optimization result of the hydrogen-containing integrated energy system taking dynamic efficiency into consideration;
FIG. 6 is a comparative view of the present invention in terms of wind curtailment.
Detailed Description
The invention is further described in connection with the following detailed description, in order to make the technical means, the creation characteristics, the achievement of the purpose and the effect of the invention easy to understand.
Aiming at the defects of the prior art, the invention provides the hydrogen-containing comprehensive energy optimal scheduling method considering dynamic energy efficiency aiming at the defects of insufficient utilization of offshore wind power and low utilization efficiency of a comprehensive energy system. An integrated energy operation system based on energy cascade utilization is built, and meanwhile, an electric hydrogen production module is contained, so that a dynamic efficiency model of the equipment is built. And the optimal comprehensive energy scheduling method for promoting wind power consumption and improving the system operation efficiency is formed by taking the system operation cost, the wind abandoning punishment and the energy utilization efficiency as optimization targets.
Referring to fig. 1, the invention firstly discloses a hydrogen-containing comprehensive energy optimization scheduling method considering dynamic energy efficiency, which comprises the following steps:
constructing a hydrogen-containing comprehensive energy system model;
based on the hydrogen-containing comprehensive energy system model, constructing a hydrogen-containing comprehensive energy optimization scheduling model considering dynamic energy efficiency; the hydrogen-containing comprehensive energy optimization scheduling model comprises an objective function and constraint conditions; the objective function is optimized by minimizing the total cost of the system, minimizing the abandoned wind cost and maximizing the energy utilization rate, and the constraint conditions comprise equilibrium relation constraint, equipment operation constraint and energy storage constraint of the hydrogen-containing comprehensive energy system;
and solving the hydrogen-containing comprehensive energy optimization scheduling model according to the purchased electric power, the wind power output power and the purchased natural gas quantity which are acquired at different moments in one day to obtain a hydrogen-containing comprehensive energy optimization scheduling scheme.
The method specifically comprises the following steps:
step one: on the premise of considering the energy cascade utilization principle, a comprehensive energy system model containing electric hydrogen is established, and the model is based on different energy coupling relations, so that the wind power consumption level is improved;
step two: an equipment model in the comprehensive energy system model is established in consideration of dynamic efficiency, so that a comprehensive energy system with multiple energy requirements of electricity, heat, cold, hydrogen and the like is formed;
step three: constructing a comprehensive energy system balance relation according to renewable energy output and multi-energy load information, external energy and different energy equipment parameter information;
step four: further considering the running cost, the wind discarding cost and the energy utilization efficiency optimization target, converting the multi-objective function into a single objective function, establishing a hydrogen-containing comprehensive energy optimization scheduling model considering dynamic energy efficiency, and solving the hydrogen-containing comprehensive energy optimization scheduling model by adopting a yalminip call cplex according to the purchased electric power, the wind power output power and the purchased natural gas quantity acquired at the same time to obtain a hydrogen-containing comprehensive energy optimization scheduling scheme.
Referring to fig. 2, the integrated energy system model based on energy cascade utilization of the present invention is shown:
further, the integrated energy system model is constructed as follows: the energy-saving system comprises electric energy and natural gas, wherein an energy utilization module comprises a gas turbine, a waste heat boiler, a gas boiler, an absorption refrigerator, a peak heater, an electric heat pump, an electrolytic tank, a methane reactor, a hydrogen fuel cell and the like, and the energy load is divided into an electric load, a cold load, a steam load, a heating load, a high-temperature load and a medium-temperature load according to the energy grade. The gas turbine burns natural gas to generate electric energy and steam heat energy, and the waste heat boiler heats part of waste heat flue gas to supply medium-temperature load; the gas boiler burns natural gas to generate steam heat energy; the absorption refrigerator consumes steam to generate cold energy, and the peak heater supplies high-temperature load by using the steam; the traditional P2G equipment is divided into an electrolytic tank and a methane reactor, wherein the electrolytic tank is used for electrolyzing electric energy into hydrogen for a hydrogen storage tank, a hydrogen fuel cell and the methane reactor; and (3) recovering waste heat of the methanation reaction of the hydrogen and supplying heat for heating load by using a hydrogen fuel cell.
Further, the conventional P2G device is divided into an electrolytic tank and a methane reactor, the electrolytic tank is used for electrolyzing electric energy into hydrogen for a hydrogen storage tank, a hydrogen fuel cell and the methane reactor, and the electrolytic process is shown as a formula (1):
wherein:outputting hydrogen power for the t-period electrolytic cell; q (Q) P2G (t) is the waste heat recovery power in the t period; η (eta) EL (t) and eta hP2G Respectively, electrolysis ofTank conversion efficiency and waste heat recovery efficiency; p (P) EL,in (t) is the electric power at the input side of the electrolytic cell, P ELN Rated power generation power for the gas turbine; i represents the polynomial fitting order of the efficiency function; n represents the highest degree of the efficiency function polynomial; beta EL,i Representing the i-th order polynomial fit factor.
The electrolytic hydrogen is used for a fuel cell and a methane reactor, and the electrolytic hydrogen is used for the methane reactor and is shown as a formula (2):
wherein:natural gas power generated for the methane reactor for the period t; />Consuming hydrogen power for the methane reactor t period; η (eta) MR Is the conversion efficiency between the hydrogen and the natural gas of the methane reactor.
The electrolytic hydrogen gas is used for a hydrogen fuel cell as shown in formula (3):
wherein: p (P) HFC (t) and Q HFC (t) the output electric power and the output thermal power of the hydrogen fuel cell at the time t respectively;inputting hydrogen power for the hydrogen fuel cell at the time t; />And->The electricity generating efficiency and the heat generating efficiency of the hydrogen fuel cell are respectively.
Further, taking dynamic efficiency into consideration, a system equipment model is established, wherein the system gas turbine equipment model is shown in formula (4):
wherein: η (eta) GT (t) is the power generation efficiency of the gas turbine at the moment t; p (P) GT (t) outputting electric power for the moment t of the gas turbine; p (P) GTN Rated power generation power for the gas turbine; i represents the polynomial fitting order of the efficiency function; n represents the highest degree of the efficiency function polynomial; beta GT,i Represents the i-th order polynomial fitting factor, h is the lower calorific value of natural gas, and is usually 9.73 KW.h/m 3 ；G GT And (t) is the air inflow of the gas turbine at the moment t.
The system gas boiler equipment model is shown as (5):
wherein: η (eta) GB (t) is the power generation efficiency of the gas boiler at the moment t; q (Q) GB (t) and Q GBN Outputting heat power and rated power value of a gas boiler for the moment t of the gas turbine; g GB (t) is the air inflow of the gas turbine at the moment t; p (P) GTN Rated power generation power for the gas turbine; beta GB,i Representing the i-th order polynomial fit factor.
The system electric refrigerator equipment model is shown as (6):
wherein: h EC (t) outputting cold power for the electric refrigerator at the moment t; h EC (t) and H ECN The refrigerating power and rated refrigerating power of the electric refrigerator at the moment t; p (P) EC (t) consuming electric power for the electric refrigerator at time t; beta EC,i And the ith order fitting factor of the refrigeration energy efficiency ratio function.
The efficiency of other devices is in linear relation with the load rate, and the output power is the input power multiplied by the conversion efficiency of the corresponding devices.
The energy storage device model is represented by formula (7):
wherein: s is S x (t) and S x (t-1) is the energy storage state of the x-th type energy storage equipment at the time of t and t-1; beta is the self-loss coefficient of the energy storage equipment; p (P) x,c (t) and P x,d (t) charging and discharging energy of the x-th type energy storage equipment at the moment t; η (eta) x,c And eta x,d For the energy charging and discharging efficiency of the equipment, x represents the type of energy storage equipment, and concretely comprises an electricity storage equipment ES, a heat storage equipment QS, a cold storage equipment HS and a hydrogen storage equipment H 2 S。
Further, when the integrated energy system model operates, electric load, cold load, steam load, heating load, high temperature load and medium temperature load supply and demand balance, gas balance and hydrogen balance need to be ensured, as shown in formula (8):
wherein: p (P) EL,in (t)、H H,L (t)、Q st,L (t)、Q W,L (t)、Q M,L (t)、Q m,L (t) electric load, cold load, steam load, heating load, high temperature load and medium temperature load, respectively; p (P) WT (t) wind power generation; p (P) g,buy (t) and P e,buy (t) the air purchasing amount and the electricity purchasing amount in the t period; q (Q) WHB,out (t) outputting heat power for the time t of the waste heat boiler; q (Q) EHP (t) and P EHP (t) outputting thermal power and inputting electric power for the moment t of the electric heating pump; q (Q) PCA,out (t) and Q PCA,in (t) peak heater output thermal power and output thermal power, respectively; q (Q) P2G (t) is the waste heat recovery power in the t period; h AB (t) and Q AB (t) outputting cold power and consumed hot power for the absorption refrigeration unit at the moment t; q (Q) GT,st And (t) is the steam thermal power output by the gas turbine.
The plant operation constraints are shown in formula (9):
wherein: p (P) s min And P s max The upper and lower limits of the output power of the equipment s are respectively; ΔP s,down And DeltaP s,up The upper and lower limits of the climbing rate of the device s are respectively set.
The energy storage constraint is shown in formula (10):
wherein: the energy storage energy of the scheduling equipment at the initial time and the end time is the same; s is S x,min And S is x,max The upper limit and the lower limit of the energy storage state are adopted; t is a scheduling period; respectively representing upper and lower limits of charging and discharging power of the energy storage system; λ=1 represents energy storage system charging; λ=0 represents energy storage system disabling.
Further, the objective function of the hydrogen-containing comprehensive energy optimization scheduling model is as follows: considering a comprehensive energy system for carbon transaction, aiming at minimum system running cost, minimum wind abandoning penalty and maximum energy efficiency utilization, as shown in a formula (11):
wherein: f is the total objective function; C. c (C) 0 Total cost of actual and reference number of operation of the system respectively; c (C) cut 、C cut,o Punishment of the system for actual and reference number of abandoned wind respectively; η, η o Respectively determining the actual and the reference energy utilization rates of the system; lambda (lambda) 1 、λ 2 And lambda (lambda) 3 Is the weight coefficient of each target, and lambda 1 +λ 2 +λ 3 ＝1。
Further, the total cost of actual operation is represented by formula (12):
C＝C om +C buy +C car (12) Wherein: c (C) om The operation and maintenance cost of the system; c (C) buy The energy purchasing cost is the external energy purchasing cost of the system; c (C) car Is the cost of carbon trade.
The external electricity purchase is provided for the coal-fired unit, and the total carbon emission quota in the carbon transaction cost is represented by formulas (13) - (14):
E IES ＝E e,buy +E GTU (13)
wherein: e (E) IES Is the total carbon emission allowance; e (E) e,buy And E is GTU Carbon emission quota for external power grid and gas system; delta e,p And delta h,p Respectively allocating the carbon emission amount of the unit electric quantity and the unit heat; p (P) e,buy (t) is the electricity purchasing amount in the t period; p (P) GTU (t)、Q GTU (t) outputting electric energy and heat energy for the gas turbine system at the period of t respectively;is an electro-thermal conversion coefficient.
In the actual carbon emission model, the Methane Reactor (MR) can absorb a part of CO in the process of converting hydrogen into natural gas 2 Taking this into consideration, the actual carbon emission amount and the carbon emission cost are represented by formulas (15) to (19):
E IES,a ＝E e,buy,a +E GTU,a -E MR,a (15)
E IES,t ＝E IES,a -E IES (18)
C car ＝P car E IES,t (19) Wherein: e (E) IES,a 、E e,buy,a And E is GTU,a Actual carbon emission of park, external power grid and gas unit system E MR,a Carbon displacement for methane reactor absorption, a 1 、b 1 、c 1 And a 2 、b 2 、c 2 The carbon emission calculation parameters of the coal-fired unit and the gas-fired unit are respectively calculated, P (t) is the equivalent output power of the gas-fired unit in the period of t, omega is the absorption of CO in the process of converting hydrogen energy into natural gas by MR equipment 2 Parameters, E IES,t For carbon emission trade amount, P car Trade price per carbon.
The wind curtailment cost is represented by formula (20):
wherein: delta cut Punishment cost for unit abandoned wind; p (P) WT,cut (t) wind power is discarded for t period
Further, the actual energy utilization rate of the system,
the actual energy utilization rate of the system is represented by the formula (21):
wherein: lambda (lambda) e 、λ q And lambda (lambda) h The energy coefficients of electric, thermal and cold loads; lambda (lambda) E 、λ G And lambda (lambda) W The conversion coefficient of electric energy, natural gas and wind energy; l (L) Q And (t) is a thermal load.
Further, the step four of establishing a hydrogen-containing comprehensive energy optimization scheduling model considering dynamic energy efficiency, adopting yalminip to call cplex to solve the comprehensive energy optimization operation model, and under constraint conditions, using an objective function to solve an optimal solution of an optimization result at minimum, wherein the optimal scheduling scheme comprises: gas turbines, gas boilers, electric refrigerators, electrolytic tanks, hydrogen fuel cells, methane reactor output power, stored/discharged energy power schedule values of hydrogen storage devices, and wind power consumption.
Examples:
selecting a comprehensive energy system of an industrial park in an offshore area of China, wherein 24 hours a day are taken as scheduling time, and the unit scheduling time is 1 hour; FIG. 4 is a graph of the optimized results of a hydrogen-containing integrated energy system taking dynamic efficiency into account, wherein the graph comprises a gas turbine output electricity and thermal power curve, a gas boiler output thermal power curve, an electrolytic cell output electric power curve and an electric refrigerator power consumption curve, the electric refrigerator is mainly supplied by the gas turbine in the electric power supply aspect, the electric refrigerator is mainly supplied by the absorption refrigerator in the cold supply aspect, the electric refrigerator bears the required cold supply requirement in the electric valley period, the gas turbine bears the main heat supply in the heat supply aspect, the gas boiler supplies heat when the steam load requirement is high, the electrolytic cell outputs when the night wind power is high, the wind power consumption is promoted, the dynamic energy efficiency is considered, the energy efficiency is regulated by the equipment when the load rate changes, and the energy utilization rate is improved; FIG. 5 is a graph showing the relationship between hydrogen energy utilization in the optimization result of a hydrogen-containing comprehensive energy system considering dynamic efficiency, including the output relationship of an electrolytic tank, a methane reactor, a hydrogen fuel cell and hydrogen energy storage, wherein the electrolytic tank is mainly used as a hydrogen energy source and is in a working state all day, the hydrogen production power is improved when the wind power output is high at night, the wind power absorption is promoted, the hydrogen energy is flexibly converted into other types of energy situations by the hydrogen fuel cell and the methane reactor, and the system multi-energy interconversion is formed; FIG. 6 is a comparison of wind curtailment and wind power consumption of the method provided by the invention at night with high wind power generation time, wherein the wind power consumption is larger than that of a traditional scene (a hydrogen-free and dynamic efficiency-free scene), and the method has the effect of promoting wind power consumption.
Table 1 evaluation index comparison table
| Optimizing results(Unit: yuan) | Traditional scene | The invention optimizes the scene |
| Total cost of system operation | 57493.6 | 53940.5 |
| Cost of purchasing energy | 34297.5 | 30327.8 |
| Cost of operation and maintenance | 16804 | 18599 |
| Cost of carbon trade | 6392.1 | 5013.7 |
| Cost of wind disposal | 3819.4 | 2390.2 |
| Energy utilization rate | 65.9％ | 78.2％ |
Table 1 is a comparison of the optimization results of the conventional scenario (hydrogen-free, dynamic efficiency-free scenario) and the proposed optimization scenario of the present invention based on energy efficiency cascade utilization. Under the traditional optimal scheduling scene, the total running cost of the system is 57493.6 yuan, wherein the purchase energy cost is 34297.5 yuan, the operation and maintenance cost is 16804 yuan, the carbon transaction cost is 6392.1 yuan, the air abandoning cost is 3819.4 yuan, and the energy utilization efficiency is 65.9%; under the hydrogen-containing comprehensive energy optimization scheduling mode considering dynamic efficiency, the total system operation cost is 53940.5 yuan, wherein the purchase energy cost is 30327.8 yuan, the operation and maintenance cost is 18599 yuan, the carbon transaction cost is 5013.7 yuan, the air abandon cost is 2390.2 yuan, and the energy utilization efficiency is 78.2%. Compared with the traditional method, the method provided by the invention has the advantages that the carbon transaction cost is reduced by 1378.4 yuan, the wind discarding cost is reduced by 1429.2 yuan, the total operation cost of the system is reduced by 3553.1 yuan, the energy efficiency utilization rate is increased by 12.3%, the operation mode provided by the invention improves the clean energy consumption, reduces the environmental cost, promotes the wind power consumption, and improves the energy efficiency utilization rate by considering the dynamic efficiency; the hydrogen-containing comprehensive energy system considering dynamic efficiency can reduce the total operation cost of the system, reduce the punishment of waste wind and increase the energy utilization efficiency.
The invention also discloses a hydrogen-containing comprehensive energy optimization scheduling device considering dynamic energy efficiency, which comprises:
the first construction module is used for constructing a hydrogen-containing comprehensive energy system model;
the second construction module is used for constructing a hydrogen-containing comprehensive energy optimization scheduling model considering dynamic energy efficiency based on the hydrogen-containing comprehensive energy system model; the hydrogen-containing comprehensive energy optimization scheduling model comprises an objective function and constraint conditions; the objective function is optimized by minimizing the total cost of the system, minimizing the abandoned wind cost and maximizing the energy utilization rate, and the constraint conditions comprise equilibrium relation constraint, equipment operation constraint and energy storage constraint of the hydrogen-containing comprehensive energy system;
and the solving module is used for solving the hydrogen-containing comprehensive energy optimization scheduling model according to the purchased power, the wind power output power and the purchased natural gas quantity acquired at different moments in a day to obtain a hydrogen-containing comprehensive energy optimization scheduling scheme.
Compared with the prior art, the method has the beneficial effects that the method is provided for promoting wind power consumption, improving the system operation efficiency and reducing the operation cost, and the method is used for optimizing and scheduling the hydrogen-containing comprehensive energy source by considering dynamic energy efficiency. On the basis of considering the energy cascade utilization principle, a dynamic efficiency model of the equipment is constructed, a comprehensive energy system containing electricity for hydrogen production is established, clean energy consumption is promoted, the aims of minimizing the system running cost and maximizing the energy utilization rate are achieved, and the optimal dispatching of the comprehensive energy containing electricity for hydrogen production is realized.
The foregoing is merely a preferred embodiment of the present invention, and it should be noted that modifications and variations could be made by those skilled in the art without departing from the technical principles of the present invention, and such modifications and variations should also be regarded as being within the scope of the invention.
Claims (10)
1. The hydrogen-containing comprehensive energy optimization scheduling method considering dynamic energy efficiency is characterized by comprising the following steps of:
constructing a hydrogen-containing comprehensive energy system model;
based on the hydrogen-containing comprehensive energy system model, constructing a hydrogen-containing comprehensive energy optimization scheduling model considering dynamic energy efficiency; the hydrogen-containing comprehensive energy optimization scheduling model comprises an objective function and constraint conditions; the objective function is optimized by minimizing the total cost of the system, minimizing the abandoned wind cost and maximizing the energy utilization rate, and the constraint conditions comprise equilibrium relation constraint, equipment operation constraint and energy storage constraint of the hydrogen-containing comprehensive energy system;
and solving the hydrogen-containing comprehensive energy optimization scheduling model according to the purchased electric power, the wind power output power and the purchased natural gas quantity which are acquired at different moments in one day to obtain a hydrogen-containing comprehensive energy optimization scheduling scheme.
2. The method for optimizing and dispatching hydrogen-containing comprehensive energy taking dynamic energy efficiency into consideration according to claim 1, wherein the hydrogen-containing comprehensive energy system model comprises electric energy, natural gas, an energy utilization module and an energy storage module, wherein the energy utilization module comprises a gas turbine, a waste heat boiler, a gas boiler, an absorption refrigerator, a peak heater, an electric heat pump, an electric refrigerator, an electrolytic tank, a methane reactor and a hydrogen fuel cell, and the energy storage module comprises an electricity storage device, a heat storage module, a cold storage device and a hydrogen storage device, and the energy load is subdivided into an electric load, a cold load, a steam load, a heating load, a high-temperature load and a medium-temperature load according to the grade of the energy;
the gas turbine burns natural gas to generate electric energy and steam heat energy, and the waste heat boiler heats waste heat flue gas to supply medium-temperature load; the gas boiler burns natural gas to generate steam heat energy; the absorption refrigerator consumes steam to generate cold energy, and the peak heater supplies high-temperature load by using the steam; the traditional P2G equipment is divided into an electrolytic tank and a methane reactor, wherein the electrolytic tank is used for electrolyzing electric energy into hydrogen for a hydrogen storage tank, a hydrogen fuel cell and the methane reactor; and (3) recovering waste heat of the methanation reaction of the hydrogen and supplying heat for heating load by using a hydrogen fuel cell.
3. The hydrogen-containing comprehensive energy optimization scheduling method considering dynamic energy efficiency according to claim 2, wherein an electrolysis formula of the electrolysis cell for electrolyzing electric energy into hydrogen is shown as (1):
wherein:outputting hydrogen power for the t-period electrolytic cell; q (Q) P2G (t) is t period P2G waste heat recovery power; η (eta) EL (t) and eta hP2G Respectively the conversion efficiency and the waste heat recovery efficiency of the electrolytic cell; p (P) EL,in (t) is the electric power at the input side of the electrolytic cell, P ELN Rated power generation power for the gas turbine; i represents the polynomial fitting order of the efficiency function; n represents the highest degree of the efficiency function polynomial; beta EL,i Representing the ith order polynomial fit factor of the cell.
4. The method for optimizing and scheduling hydrogen-containing comprehensive energy taking dynamic energy efficiency into consideration according to claim 2, wherein the electrolytic hydrogen is used for a fuel cell and a methane reactor, and the electrolytic hydrogen is used for the methane reactor as shown in formula (2):
wherein:natural gas power generated for the methane reactor for the period t; />Consuming hydrogen power for the methane reactor t period; η (eta) MR The conversion efficiency between the hydrogen and the natural gas of the methane reactor is improved;
the electrolytic hydrogen gas is used for a hydrogen fuel cell as shown in formula (3):
wherein: p (P) HFC (t) and Q HFC (t) the output electric power and the output thermal power of the hydrogen fuel cell at the time t respectively;inputting hydrogen power for the hydrogen fuel cell at the time t; />Generating efficiency for hydrogen fuel cell, +.>Is the heat generating efficiency of the hydrogen fuel cell.
5. The hydrogen-containing comprehensive energy optimization scheduling method considering dynamic energy efficiency according to claim 2, wherein the model formula of the gas turbine is:
wherein: η (eta) GT (t) is the power generation efficiency of the gas turbine at the moment t; p (P) GT (t) outputting electric power for the moment t of the gas turbine; p (P) GTN Rated power generation power for the gas turbine; i represents the polynomial fitting order of the efficiency function; n represents the highest degree of the efficiency function polynomial; beta GT,i The ith order polynomial fitting factor of the gas turbine is represented, and h is the lower calorific value of the natural gas; g GT (t) is the air inflow of the gas turbine at the moment t;
the model formula of the system gas boiler is as follows:
wherein: η (eta) GB (t) is the heating efficiency of the gas boiler at the moment t; q (Q) GB (t) is the thermal power output by the gas turbine at the moment t, Q GBN Rated power value of the gas boiler; g GB (t) is the air inflow of the gas turbine at the moment t; beta GB,i Representing an ith order polynomial fitting factor of the gas boiler;
the model formula of the electric refrigerator is as follows:
wherein: h EC (t) outputting cold power for the electric refrigerator at the moment t; h EC (t) is the refrigerating power of the electric refrigerator at the moment t, H ECN Rated refrigeration power at t moment of the electric refrigerator; p (P) EC (t) consuming electric power for the electric refrigerator at time t; beta EC,i An ith order polynomial fitting factor for the refrigeration energy efficiency ratio function;
the energy storage module equipment model formula is:
wherein: s is S x (t) and S x (t-1) is the energy storage state of the x-th type energy storage equipment at the time of t and t-1; beta is the self-loss coefficient of the energy storage equipment; p (P) x,c (t) is the charging power of the x-th type energy storage device at the time t, P x,d (t) discharging power at t for the x-th type energy storage device; η (eta) x,c Charge efficiency eta of the equipment x,d For the energy release efficiency of the equipment, deltat is a time interval, x represents the type of energy storage module equipment, specifically an electricity storage equipment ES, a heat storage equipment QS, a cold storage equipment HS and a hydrogen storage equipment H 2 S。
6. The hydrogen-containing comprehensive energy optimization scheduling method considering dynamic energy efficiency according to claim 2, wherein the constraint conditions of the hydrogen-containing comprehensive energy optimization scheduling model considering dynamic energy efficiency include:
satisfies the supply and demand balance of electric load, cold load, steam load, heating load, high temperature load and medium temperature load, natural gas balance and hydrogen balance, as shown in formula (8):
wherein: p (P) EL,in (t) is an electrical load, H H,L (t) is a cold load, Q st,L (t) is steam load, Q W,L (t) is heating load, Q M,L (t) is a high temperature load, Q m,L (t) is a medium temperature load; p (P) WT (t) wind power generation; p (P) g,buy (t) is the natural gas purchase amount in the period t, P e,buy (t) is the electricity purchasing amount in the t period; q (Q) WHB,out (t) outputting heat power for the time t of the waste heat boiler; q (Q) EHP (t) is the output thermal power of the electrothermal pump at the moment t, P EHP (t) inputting electric power for the moment t of the electric heating pump; q (Q) PCA,out (t) peak heater output thermal power, Q PCA,in (t) is peak heater output thermal power; q (Q) P2G (t) is the waste heat recovery power in the t period; h AB (t) and Q AB (t) outputting cold power and consumed hot power for the absorption refrigeration unit at the moment t; q (Q) GT,st (t) is the steam heat power output by the gas turbine;
the energy consumption module device operation constraint is as shown in formula (9):
wherein: s represents different energy using module equipment types, P s (t) and P s (t-1) represents the power of the device s at time t and time t-1 respectively,for the upper limit of the output power of the device s, +.>A lower limit for the output power of the device s; ΔP s,up For the upper limit of the climbing rate of the equipment s, delta P s,down A lower limit on the climbing rate for the device s;
the energy storage constraint is shown in formula (10):
wherein: energy storage S at initial moment of scheduling device x (0) Energy storage S at the end time x (T) is the same, T is a scheduling period; s is S x,max Is the upper limit of the energy storage state, S x,min Is the lower limit of the energy storage state;representing an upper limit of charging power of the energy storage system; />Representing an upper limit of energy discharging power of the energy storage system; λ=1 represents energy storage system charging; λ=0 represents energy storage system disabling.
7. The hydrogen-containing comprehensive energy optimization scheduling method considering dynamic energy efficiency according to claim 1, wherein the objective function of the hydrogen-containing comprehensive energy optimization scheduling model is as shown in formula (11):
wherein: f is the total objective function; c is the total cost of actual operation of the system, C 0 Running the total cost for the reference number; c (C) cut Punishment is carried out on the system; c (C) cut,o Punishment for the parametric number of wind curtailment; η is the actual energy utilization rate of the system; η (eta) o The energy utilization rate is the reference number; lambda (lambda) 1 、λ 2 And lambda (lambda) 3 Is the weight coefficient of each target, and lambda 1 +λ 2 +λ 3 ＝1。
8. The hydrogen-containing integrated energy optimization scheduling method considering dynamic energy efficiency according to claim 7, wherein the actual total running cost is represented by formula (12):
C＝C om +C buy +C car (12)
wherein: c (C) om The operation and maintenance cost of the system; c (C) buy The energy purchasing cost is the external energy purchasing cost of the system; c (C) car Cost for carbon trade;
the external electricity purchase is provided for the coal-fired unit, and the total carbon emission quota in the carbon transaction cost is represented by formulas (13) - (14):
E IES ＝E e,buy +E GTU (13)
wherein: e (E) IES Is the total carbon emission allowance; e (E) e,buy For external grid carbon emission quota, E GTU Carbon emission quota for gas system; delta e,p Carbon emission allocation amount delta for unit electric quantity h,p Allocate carbon emissions per unit of heat; p (P) e,buy (t) is the electricity purchasing amount in the t period; p (P) GTU (t) is t periodThe gas unit system outputs electric energy, Q GTU (t) outputting heat energy for the t-period gas turbine system;is an electro-thermal conversion coefficient;
in the actual carbon emission, the actual carbon emission amount and the carbon emission cost are represented by the following formulas:
E IES,a ＝E e,buy,a +E GTU,a -E MR,a (15)
E IES,t ＝E IES,a -E IES (18)
C car ＝P car E IES,t (19)
wherein: e (E) IES,a Actual carbon emission for the park; e (E) e,buy,a The actual carbon emission is the actual carbon emission of the external power grid; e (E) GTU,a E is the actual carbon emission of the gas unit system MR,a Carbon displacement for methane reactor absorption, a 1 、b 1 、c 1 Calculating parameters for carbon emission of the coal-fired unit; a, a 2 、b 2 、c 2 For the carbon emission calculation parameters of the gas turbine unit, P (T) is the equivalent output power of the gas turbine unit in the period T, T is the scheduling period, omega is the absorption of CO in the process of converting hydrogen energy into natural gas by MR equipment 2 Parameters, E IES,t For carbon emission trade amount, P car Trade price per carbon;
the wind curtailment cost is represented by formula (20):
wherein: delta cut Punishment cost for unit abandoned wind; p (P) WT,cut And (t) the wind power is abandoned in the period t.
9. The hydrogen-containing comprehensive energy optimization scheduling method considering dynamic energy efficiency according to claim 7, wherein the actual energy utilization rate of the system is represented by formula (21):
wherein: lambda (lambda) e Is the electrical load energy coefficient; lambda (lambda) q Is the thermal load energy coefficient; lambda (lambda) h Is the cold load energy coefficient; lambda (lambda) E Is the conversion coefficient of the electric energy; lambda (lambda) G Is the conversion coefficient of natural gas; lambda (lambda) W The conversion coefficient of wind energy; l (L) Q And (t) is a thermal load.
10. A hydrogen-containing integrated energy optimization scheduling device considering dynamic energy efficiency, the device comprising:
the first construction module is used for constructing a hydrogen-containing comprehensive energy system model;
the second construction module is used for constructing a hydrogen-containing comprehensive energy optimization scheduling model considering dynamic energy efficiency based on the hydrogen-containing comprehensive energy system model; the hydrogen-containing comprehensive energy optimization scheduling model comprises an objective function and constraint conditions; the objective function is optimized by minimizing the total cost of the system, minimizing the abandoned wind cost and maximizing the energy utilization rate, and the constraint conditions comprise equilibrium relation constraint, equipment operation constraint and energy storage constraint of the hydrogen-containing comprehensive energy system;
and the solving module is used for solving the hydrogen-containing comprehensive energy optimization scheduling model according to the purchased power, the wind power output power and the purchased natural gas quantity acquired at different moments in a day to obtain a hydrogen-containing comprehensive energy optimization scheduling scheme.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311035665.1A CN117035331A (en) | 2023-08-16 | 2023-08-16 | Dynamic energy efficiency considered hydrogen-containing comprehensive energy optimization scheduling method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311035665.1A CN117035331A (en) | 2023-08-16 | 2023-08-16 | Dynamic energy efficiency considered hydrogen-containing comprehensive energy optimization scheduling method and device |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117035331A true CN117035331A (en) | 2023-11-10 |
Family
ID=88622444
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311035665.1A Pending CN117035331A (en) | 2023-08-16 | 2023-08-16 | Dynamic energy efficiency considered hydrogen-containing comprehensive energy optimization scheduling method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117035331A (en) |
-
2023
- 2023-08-16 CN CN202311035665.1A patent/CN117035331A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111738502B (en) | Multi-energy complementary system demand response operation optimization method for promoting surplus wind power consumption | |
| CN109523052B (en) | Virtual power plant optimal scheduling method considering demand response and carbon transaction | |
| CN111639824B (en) | Thermoelectric optimization scheduling method for regional comprehensive energy system with electric-to-gas conversion | |
| CN109447323B (en) | Comprehensive energy system two-stage capacity configuration method considering node heat price | |
| CN109474025B (en) | Optimized dispatching model of park level comprehensive energy system | |
| CN110163415B (en) | Multi-objective fuzzy cooperative optimization method for multi-energy flow system under variable working condition characteristic | |
| CN113850474B (en) | Thermoelectric hydrogen multi-energy flow comprehensive energy system and optimal scheduling method thereof | |
| CN111030104B (en) | Method for improving energy utilization rate of multi-energy system containing hydrogen storage device | |
| CN111324849A (en) | Electric heating combined system optimal scheduling method considering heat supply network characteristics | |
| CN110957722B (en) | Day-ahead optimal scheduling method for micro energy network with electricity-to-gas equipment | |
| CN110286590A (en) | Consider the cooling heating and power generation system Optimization Scheduling a few days ago of polymorphic type energy storage | |
| CN117350419A (en) | Park comprehensive energy system optimization operation method considering flexible load and carbon flow | |
| CN110245863B (en) | Electrical combination system based on electrical conversion energy storage and online regulation and control method | |
| CN114759599A (en) | Photo-hydrogen fuel cell cogeneration system, capacity allocation method, and medium | |
| CN114742276A (en) | Multi-objective optimization scheduling method of park comprehensive energy system with ORC (organic Rankine cycle) considering exergy efficiency | |
| CN113078684B (en) | Regional energy community planning method based on double-layer optimization | |
| CN114396822B (en) | Energy comprehensive utilization configuration and operation method | |
| CN115906456A (en) | Hydrogen-containing energy IES scheduling optimization model considering response uncertainty of demand side | |
| CN114386256A (en) | Regional electric heating system optimal scheduling method considering flexibility constraint of electric heating equipment and heat supply network characteristics | |
| CN117035331A (en) | Dynamic energy efficiency considered hydrogen-containing comprehensive energy optimization scheduling method and device | |
| CN114676897A (en) | Optimal scheduling method for comprehensive energy system of park containing CHP-P2G-hydrogen energy | |
| CN109449974A (en) | A kind of integrated energy system Optimization Scheduling based on change Mode Decomposition and sample entropy theory | |
| Hao et al. | A Multi-Objective Optimization Planning of Park-level Integrated Energy System Based on Two-tier Planning Model | |
| CN114362152B (en) | Multi-time scale scheduling method for comprehensive energy system | |
| Men et al. | Optimization strategy of ac/dc hybrid energy supply in multi energy system considering adiabatic compressed air energy storage device and comprehensive demand response |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |