CN116613741A - Comprehensive energy system optimization scheduling method considering stepped carbon transaction - Google Patents
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Abstract
The invention discloses a comprehensive energy system optimization scheduling method considering stepped carbon transaction, which comprises the following steps: step 1, constructing an electric-hydrogen-heat comprehensive energy system to meet the requirement of a multi-energy load; step 2, constructing an economic low-carbon scheduling model of the comprehensive energy system with the minimum economic cost and carbon transaction cost as targets, and solving the model by adopting a particle swarm algorithm to reduce the total cost and carbon emission of the system; step 3: and constructing LabVIEW-based monitoring software to display the optimization result. According to the invention, the links of hydrogen production by electrolysis of water, methanation of hydrogen and CO2 and conversion of hydrogen into electric heat energy by HFC are considered, the carbon emission condition of the comprehensive energy system is calculated, a stepped carbon transaction mechanism model is established, the aim of daily low-carbon economic dispatch is established by the system purchase cost and the stepped carbon transaction cost, and the influence of refined P2G on the system emission reduction is analyzed on the premise of meeting the requirement of multipotency load.
Description
Technical Field
The invention belongs to the field of operation optimization of a multi-energy complementary system, and particularly relates to a comprehensive energy system optimization scheduling method considering stepped carbon transaction.
Background
In recent years, with the increasing serious problems of energy crisis and environmental pollution, the construction of a safe, reliable, clean, economical and sustainable energy system becomes an important national strategic goal. Under the background, renewable energy sources such as photovoltaic, wind power and the like are widely applied to the energy industry so as to accelerate the rapid transformation of an energy system. The integrated energy system (Integrated Energy System, IES) integrates the steps of multi-energy production, conversion and storage, and can further improve the energy utilization rate and promote the renewable energy consumption on the premise of meeting the multi-energy load demand of the terminal. On the one hand, with the continuous development of Power to Gas (P2G) technology, the electric-Gas closed loop formed in the integrated energy system gradually enhances the electric coupling of the system, and has important significance for realizing multi-energy complementation and collaborative development. However, when modeling P2G, most of the existing studies only consider the process of converting electricity into natural gas, but two stages of actually converting P2G into natural gas by hydrogen and electricity are not modeled in detail. Therefore, the traditional P2G modeling is too simple, and the effective utilization link of hydrogen is omitted. On the other hand, carbon trade is considered as one of the effective measures for reducing carbon emissions, and the introduction of carbon trade to combine the economy and low-carbon environmental friendliness of an integrated energy system will promote the reduction of carbon emissions. Notably, conventional carbon trading mechanisms simply trade and purchase based on a fixed quota of subjects and actual carbon emissions. In contrast, the stepwise carbon trading mechanism can guide manufacturers to reasonably produce and discharge by combining self quota based on a carbon trading market, and under the condition of ensuring certain economy, the inhibition capability on the carbon discharge of the system is stronger.
From the analysis, the P2G is modeled in a refined manner at present, so that less research is performed to consider efficient utilization of hydrogen, and influence of participation of the P2G link in the carbon transaction market on the economical efficiency and the environmental benefit of the comprehensive energy system is rarely considered.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, and provides a comprehensive energy system optimization scheduling method considering stepped carbon transaction, which refines a P2G process by introducing a hydrogen fuel cell (Hydrogen Fuel Cel l, HFC), and considers links of hydrogen production by water electrolysis, methanation of hydrogen and CO2 and conversion of the hydrogen into electric heat energy by the HFC. And then calculating the carbon emission condition of each main body in the comprehensive energy system, and establishing a stepped carbon transaction mechanism model. The aim of daily low-carbon economic dispatching is established by the system energy purchasing cost and the stepwise carbon transaction cost, and the influence of refined P2G on the system emission reduction is analyzed on the premise of meeting the requirement of the multi-energy load. The running state of the system and the optimization result can be displayed through monitoring software built based on LabVIEW.
The technical scheme for achieving the purpose is as follows: an integrated energy system optimization scheduling method considering stepped carbon transaction comprises the following steps:
step 1, constructing an electric-hydrogen-heat comprehensive energy system to meet the requirement of a multi-energy load;
step 2, constructing an economic low-carbon scheduling model of the comprehensive energy system with the minimum economic cost and carbon transaction cost as targets, and solving the model by adopting a particle swarm algorithm to reduce the total cost and carbon emission of the system;
step 3: constructing LabVIEW-based monitoring software to display the optimized result
In step 1, the comprehensive energy system comprises wind turbine, photovoltaic unit, electrolyzed water, methanation, fuel cell, gas turbine, gas boiler, electric storage, heat storage and hydrogen storage device.
Still further, in step 1, a fuel cell refinement P2G process is introduced: electrolytic water is used for producing hydrogen, part of hydrogen is synthesized into natural gas through methanation, part of hydrogen is converted into electricity and heat energy, and the other part of hydrogen can be stored in a hydrogen storage device.
In step 1, the refined P2G process converts hydrogen energy directly into electricity and heat energy through the fuel cell, and compared with the process of converting the hydrogen energy into the natural gas firstly and then converting the natural gas into the electricity and heat energy through the gas turbine or the gas boiler, the energy conversion link is reduced, the energy utilization rate is improved, and the use of the hydrogen energy can not generate CO2.
Still further, in step 1, during the refined P2G process, the methanated hydrogen energy to natural gas process may absorb a portion of CO2, further reducing the carbon emissions of the system.
Further, in step 2, the system manufacturers are guided to participate in the carbon trade market by using the ladder-type carbon trade mechanism, and the traditional ladder-type carbon trade mechanism is improved based on the idea of rewarding renewable energy sources for power generation.
Still further, in step 2, the improved stepwise carbon trade mechanism can realize the dynamic change of carbon trade base price along with the renewable energy source ratio, and promote renewable energy source power generation with a certain reward coefficient.
In step 2, predicting the carbon trade base price of the carbon trade market for a period of time in the future by adopting an ARMA-BP residual error optimization combination model; considering that wind power has larger randomness and volatility, in order to lighten the pressure of a main network, IES is not considered for selling electricity to an upper power grid, and the economic cost is composed of equipment operation maintenance cost, electricity purchasing cost and gas purchasing cost.
In step 2, an integrated energy system optimization scheduling model is built, and equipment operation constraint, network constraint and power balance constraint are considered.
Further, in step 3, the monitoring software provides data acquisition, optimal scheduling and result visualization services for the comprehensive energy system.
The comprehensive energy system optimization scheduling method considering the stepped carbon transaction has the following outstanding substantive characteristics and obvious technical progress:
the comprehensive energy system constructed by the invention introduces an HFC refining traditional P2G process, and an electric-gas closed loop formed by Ele and MR can absorb part of CO2 in the system, and an electric-hydrogen double closed loop formed by Ele and HFC improves the energy utilization rate and further reduces the carbon emission of the system.
The stepped carbon transaction mechanism provided by the invention guides each producer to actively participate in the carbon transaction market, realizes the effective balance of system economy and environmental cost, and ensures the low-carbon economy of the system.
The new energy power generation rewarding mechanism considers the dynamic relation between the renewable energy power generation duty ratio and the carbon transaction basic price, realizes low-carbon emission, promotes the renewable energy power generation, and further accelerates the transformation of low-carbon cleaning of the system.
The monitoring software can collect and analyze the data of the system, optimize and schedule the data by using the energy management system, and finally visually display the optimized result.
At the same time, the invention can be extended to other more similar multi-energy complementary systems.
Drawings
FIG. 1 is a block diagram of an integrated energy system for an electro-hydrogen thermal zone of the present invention;
FIG. 2 is a schematic diagram of an integrated energy system optimization schedule in view of a stepwise carbon transaction in accordance with the present invention;
fig. 3 is a diagram of a support system architecture of the optimal scheduling method of the present invention.
Detailed Description
In order to better understand the technical solution of the present invention, the following detailed description is given by way of specific examples:
as shown in fig. 1, 2 and 3, the invention provides an integrated energy system optimization scheduling method considering stepped carbon transaction, comprising the following steps:
step 1: construction of an electric-thermal-hydrogen comprehensive energy system
In the step 1, referring to fig. 1, the system structure is divided according to the energy form, and the system structure is divided into four buses, namely an electric bus, a thermal bus, a hydrogen bus and a natural gas bus based on the concept of energy bus. The wind power and photovoltaic unit provides renewable clean energy for the system; electrolytic water is used for producing hydrogen, and electric energy is converted into hydrogen energy; part of the hydrogen energy is converted into natural gas through methanation, and the natural gas can also be directly subjected to heat and power cogeneration through a fuel cell; the natural gas is combusted by the gas turbine to meet the thermoelectric load requirement; the gas boiler burns natural gas to provide heat energy; the fuel cell is connected with the electric bus, and equipment and energy sources covered by the electric bus comprise electric storage, electrolysis of water and electric loads; the gas turbine, the gas boiler and the fuel cell are connected into a thermal bus, and equipment covered by the thermal bus has thermal storage and thermal load; the natural gas and methanation device is connected with a gas bus, and equipment covered by the gas bus comprises a gas turbine and a gas boiler; the electrolyzed water is connected into a hydrogen bus, and equipment covered by the hydrogen bus comprises a hydrogen storage device, a fuel cell, a methanation device and a hydrogen load.
Step 2: constructing an optimized scheduling model of a comprehensive energy system with minimum economic cost and carbon transaction cost as targets
In said step 2, see fig. 2, an objective function is established that aims at optimizing the overall cost of the system in terms of operational maintenance costs, net purchase/sales electricity, and natural gas and carbon transaction costs.
The objective function is expressed as:
wherein f is total daily operation cost of the micro-grid; f (f) ope Maintenance costs for a fixed daily operation of the device; f (f) buy For purchase/sale costs;representing the cost of carbon trade.
Cost of system operation and maintenance f ope Can be expressed as:
in the method, in the process of the invention,the operation maintenance cost of the unit output power of the equipment k is meta/(kW.h); p (P) k,t Is the actual output power of device k.
Net purchase/selling electricity and natural gas costs f buy Can be expressed as:
wherein beta is b (t) is the electricity purchasing price at the moment t, and the unit/(kW.h); beta s (t) is the electricity selling price at the moment t, and the price per unit/(kW.h); p (P) e (t) is the interaction power of the power grid and the energy hub, P e (t) > 0 represents electricity purchasing to the power grid, P e (t) < 0 represents selling electricity to the grid; 0/1 variable rho represents the energy hub purchase/sale state, 0 represents the purchase of electricity to the power grid, and 1 represents the sale of electricity to the power grid; beta g (t) is the price of the fuel gas unit heat value at the moment t, and the price is Yuan/(kW.h); f (F) g And (t) represents the air purchase amount at the time t.
In the step 2, an improved ladder-type carbon trading mechanism is adopted in carbon trading, namely, new energy power generation rewards are combined with predicted unit carbon emission right trading (carbon trading) collecting prices to form dynamic carbon trading prices, carbon dioxide emission is divided into a plurality of sections, and the more the carbon emission is, the higher the carbon trading prices are.
The equipment such as the gas turbine, the power grid, the gas boiler and the like adopts an industry datum line method to calculate carbon emission quota, the gas turbine with the thermoelectric ratio larger than 1 is classified into a heating industry, the gas turbine with the thermoelectric ratio smaller than 1 is classified into a power supply industry, the gas turbine with the thermoelectric ratio larger than 1 is taken as an example, and a carbon emission quota model of the system is as follows:
wherein E is IES 、E Grid,s 、E GT 、E GB The method comprises the steps of respectively supplying power to a comprehensive energy system, a power grid, a gas turbine and a carbon emission quota of a gas boiler; delta e 、δ g The carbon emission quota coefficient is the unit power consumption of the coal-fired unit and the unit natural gas consumption of the gas-fired unit respectively; alpha is a conversion coefficient for converting the generated energy into the heat supply quantity; p (P) Grid,s (t) is the power supply quantity of the power grid in the period t; t is the scheduling period.
The methanation device can absorb a part of carbon dioxide discharged by the comprehensive energy system when converting hydrogen into natural gas, and the actual carbon discharge model is as follows:
wherein E is IES,f 、E Grid,s,f The actual carbon emission for supplying power to the comprehensive energy system and the power grid; e (E) G,f The actual carbon emission is the actual carbon emission of the gas turbine and the gas boiler; e (E) MR,f The actual carbon dioxide absorption amount for the methanation equipment; q (Q) G (t) is the total equivalent heat supply of the gas turbine and the gas boiler; p (P) GT Generating electric power for the gas turbine; q (Q) GT Generating heat power for the gas turbine; q (Q) GB The heat generating power of the gas boiler; gamma is a parameter of carbon dioxide absorption when the hydrogen of the methanation equipment is converted into natural gas; f (F) MR And (t) is natural gas power output by the methanation equipment.
Cost of system carbon tradeCan be expressed as:
wherein, sigma is the predicted unit carbon trade receiving price; sigma' unit carbon trade dynamic price; p is the carbon emission interval length of the carbon trade; delta is the increase of carbon trade price; lambda is the new energy power generation rewarding coefficient.
In the step 2, an integrated energy system optimization scheduling model is established, and bus power balance constraint and equipment operation constraint are considered
The bus power balancing constraints are as follows:
the electric bus power balance equation is expressed as:
P e (t)+P WT (t)+P PV (t)+P FC (t)+P ES,dis (t)+P GT (t)＝P el (t)+P ES,c (t)+P EL (t) (8)
wherein P is e The interactive power between the public power grid and the system is obtained; p (P) WT The output power of the wind turbine generator is; p (P) PV The output power of the photovoltaic unit; p (P) FC Generating electrical power for the fuel cell; p (P) el Inputting power for the water electrolysis link; p (P) EL Is the electrical load demand; p (P) ES,c 、P ES,dis For storing and discharging power.
The thermal bus power balancing equation is expressed as:
Q GT +Q GB +Q FC (t)+P TS,dis (t)＝P TS,c (t)+P TL (t) (9)
in which Q FC Generating heat power for the fuel cell; p (P) TL Is the heat load demand; p (P) TS,c 、P TS,dis Is the heat storage and release power.
The gas bus power balance equation is expressed as:
F g (t)+F MR (t)＝F GT (t)+F GB (t) (10)
wherein F is g Is the output quantity of natural gas; f (F) GT Natural gas is consumed for the gas turbine; f (F) GB Natural gas is consumed for the gas boiler.
The hydrogen bus power balance equation is expressed as:
P eh (t)+P HS,dis (t)＝P fc (t)+P HS,c (t)+P HL (t)+P MR (t) (11)
wherein P is eh Output power for electrolyzed water; p (P) FC Inputting power to the fuel cell; p (P) HL Is hydrogen load demand; p (P) HS,c 、P HS,dis The power for storing and releasing hydrogen.
The device operation constraints are as follows:
mathematical model of electrolyzed water:
P eh (t)＝η el P el (t) (12)
wherein eta is el Is the efficiency of water electrolysis.
Mathematical model of hydrogen fuel cell:
P FC (t)＝η FC，e P fc (t) (13)
Q FC (t)＝η FC，h P fc (t) (14)
wherein eta is FC，e 、η FC，h The power generation and heat generation efficiency is achieved;
the input power of the fuel cell and the electrolyzer should satisfy the following mutually exclusive conditions:
P el (t)×P fc ′(t)＝0 (15)
mathematical model of energy storage device:
in omega j Is a collection of stored energy j; c (C) j (t) is the capacity of the energy storage j at time t; zeta type j Is the self-loss coefficient of the energy storage j; η (eta) j,c 、η j,d The energy charging and discharging efficiency of the energy storage j are respectively; p (P) j,c (t)、P j,d (t) is the charging and discharging power (kW) of the energy storage j respectively; ES, TS, HS are respectively electrical, thermal, and hydrogen storage.
Mathematical model of gas turbine:
[P GT (t)]＝η GT,e [F GT (t)] (17)
[Q GT (t)]＝σ GT [P GT (t)] (18)
wherein eta is GT,e The power generation efficiency for GT; sigma (sigma) GT Is the GT thermoelectric ratio.
Mathematical model of gas boiler:
[Q GB (t)]＝η GB,h [F GB (t)] (19)
wherein eta is GB,h Is GB heat generating efficiency.
Methanation mathematical model:
F MR (t)＝η MR P MR (t) (20)
wherein eta is MR The efficiency of converting hydrogen energy to natural gas for methanation facilities.
The device operation and energy storage charging and discharging power constraints are as follows:
an electrolytic cell:
in the method, in the process of the invention,the upper and lower limits of power are output for the water electrolysis link.
A fuel cell:
in the method, in the process of the invention,the upper limit and the lower limit of the power generation are set; />Is the upper and lower limits of heat generation power.
Electricity, heat, hydrogen storage:
wherein C is j The installation capacity of the energy storage j;respectively the maximum and minimum percentages of the capacity of the energy storage j;the upper limit and the lower limit of the energy storage j energy release power are respectively; />The upper limit and the lower limit of the charging power of the energy storage j are respectively.
The energy storage device cannot enter the charging and discharging states at the same time, so that the charging and discharging power of the energy storage j meets the mutual exclusion condition:
[P j,d (t)]×[P j,c (t)]＝0 (27)
in order to ensure that enough adjustment allowance is reserved in the next period of energy storage, the energy storage energy at the moment when the energy storage j optimization control starts and ends is equal:
[C j (1)]＝[C j (T)] (28)
wherein C is j (1)、C j And (T) respectively optimizing the capacity of the energy storage j at the starting time and the ending time of the control.
Electric network purchase/sell electricity constraints:
in the method, in the process of the invention,the upper limit and the lower limit of the interactive power of the system and the power grid are adopted.
Natural gas network constraints:
in the method, in the process of the invention,is the maximum transmission power of the natural gas pipeline.
In the step 2, in MATLAB, a particle swarm algorithm is adopted to optimize the comprehensive energy system, and the rationality and the effectiveness of the method are verified.
Step 3: constructing LabVIEW-based monitoring software to display the optimized result
In the step 3, referring to fig. 3, based on the monitoring software set up by the LabVIEW, the convenient programming language and operation control function of the LabVIEW are combined with the powerful simulation calculation function of MATLAB. The software writes the energy management optimization algorithm into MATLAB script nodes in LabVIEW, and realizes hybrid programming of MATLAB and LabVIEW through the nodes. The software has the functions of controlling equipment, updating data, detecting system state, monitoring data, inquiring historical data, switching scenes and the like. The control device and the updated data comprise an upper limit and a lower limit of the output power of the device, a trade price of unit carbon emission, an increase range of the trade price of carbon, a carbon emission quota of the gas turbine, a carbon emission quota of the gas boiler, a carbon emission quota of a power grid and the like. And the influence of carbon transaction on the environmental performance and the economical performance of the comprehensive energy system under different carbon transaction states is verified through the control of equipment and the updating of data.
It will be appreciated by persons skilled in the art that the above embodiments are provided for illustration only and not for limitation of the invention, and that variations and modifications of the above described embodiments are intended to fall within the scope of the claims of the invention as long as they fall within the true spirit of the invention.
Claims (10)
1. The comprehensive energy system optimization scheduling method considering the stepped carbon transaction is characterized by comprising the following steps of:
step 1, constructing an electric-hydrogen-heat comprehensive energy system to meet the requirement of a multi-energy load;
step 2, constructing an economic low-carbon scheduling model of the comprehensive energy system with the minimum economic cost and carbon transaction cost as targets, and solving the model by adopting a particle swarm algorithm to reduce the total cost and carbon emission of the system;
step 3: and constructing LabVIEW-based monitoring software to display the optimization result.
2. The method for optimizing and dispatching a comprehensive energy system according to claim 1, wherein in step 1, the comprehensive energy system comprises wind power generation units, photovoltaic units, electrolyzed water, methanation, fuel cells, gas turbines, gas boilers, electric storage, heat storage and hydrogen storage devices.
3. The comprehensive energy system optimization scheduling method considering the stepwise carbon trade according to claim 2, wherein in step 1, a fuel cell refinement P2G process is introduced: electrolytic water is used for producing hydrogen, part of hydrogen is synthesized into natural gas through methanation, part of hydrogen is converted into electricity and heat energy, and the other part of hydrogen can be stored in a hydrogen storage device.
4. The comprehensive energy system optimization scheduling method considering stepped carbon transaction according to claim 3, wherein in the step 1, the refined P2G process is characterized in that hydrogen energy is directly converted into electricity and heat energy through a fuel cell, compared with the process of converting the hydrogen energy into natural gas firstly and then into electricity and heat energy through a gas turbine or a gas boiler, the energy conversion link is reduced, the energy utilization rate is improved, and the use of the hydrogen energy does not generate CO2.
5. The comprehensive energy system optimization scheduling method considering stepped carbon transaction according to claim 3, wherein in the step 1, the refined P2G process and the methanation of hydrogen energy into natural gas process can absorb a part of CO2, so that the carbon emission of the system is further reduced.
6. The comprehensive energy system optimization scheduling method considering the stepped carbon trade according to claim 1, wherein in step 2, the stepped carbon trade mechanism is utilized to guide each producer of the system to participate in the carbon trade market, and the traditional stepped carbon trade mechanism is improved based on the idea of rewarding renewable energy power generation.
7. The comprehensive energy system optimization scheduling method considering the stepped carbon trade according to claim 6, wherein in the step 2, the improved stepped carbon trade mechanism can realize the dynamic change of the carbon trade base price along with the renewable energy duty ratio and promote the renewable energy to generate electricity with a certain rewarding coefficient.
8. The comprehensive energy system optimization scheduling method considering the stepwise carbon trade according to claim 6, wherein in step 2, the carbon trade base price of the carbon trade market for a period of time in the future is predicted by adopting an ARMA-BP residual error optimization combination model; considering that wind power has larger randomness and volatility, in order to lighten the pressure of a main network, IES is not considered for selling electricity to an upper power grid, and the economic cost is composed of equipment operation maintenance cost, electricity purchasing cost and gas purchasing cost.
9. The comprehensive energy system optimization scheduling method considering the stepwise carbon trade according to claim 6, wherein in step 2, a comprehensive energy system optimization scheduling model is established, and equipment operation constraint, network constraint and power balance constraint are considered.
10. The method for optimizing and scheduling a comprehensive energy system according to claim 1, wherein in step 3, the monitoring software provides data acquisition, optimizing and scheduling and result visualization services for the comprehensive energy system.
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| CN117455210A (en) * | 2023-12-26 | 2024-01-26 | 山东建筑大学 | Comprehensive energy system scheduling method, system, medium and equipment |
| CN118034066A (en) * | 2024-04-11 | 2024-05-14 | 国网江苏省电力有限公司常州供电分公司 | Coordinated operation control method, equipment and storage medium for energy system of multi-energy coupling cabin |
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| CN117455210A (en) * | 2023-12-26 | 2024-01-26 | 山东建筑大学 | Comprehensive energy system scheduling method, system, medium and equipment |
| CN117455210B (en) * | 2023-12-26 | 2024-04-05 | 山东建筑大学 | Comprehensive energy system scheduling method, system, medium and equipment |
| CN118034066A (en) * | 2024-04-11 | 2024-05-14 | 国网江苏省电力有限公司常州供电分公司 | Coordinated operation control method, equipment and storage medium for energy system of multi-energy coupling cabin |
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