CN116093949A - Demand response optimization method considering two-stage P2G hybrid energy storage and carbon potential control - Google Patents

Demand response optimization method considering two-stage P2G hybrid energy storage and carbon potential control Download PDF

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CN116093949A
CN116093949A CN202310204428.7A CN202310204428A CN116093949A CN 116093949 A CN116093949 A CN 116093949A CN 202310204428 A CN202310204428 A CN 202310204428A CN 116093949 A CN116093949 A CN 116093949A
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energy storage
load
carbon
cost
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杨丽君
王颖
李盼
姜亚宁
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Yanshan University
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Abstract

The invention discloses a demand response optimization method considering two-stage P2G hybrid energy storage and carbon potential control, and belongs to the technical field of power system operation analysis. According to the invention, P2G is refined into a 'two-stage and four-module' operation process of replacing the traditional P2G by using EL, MR, FC and GT combined equipment through the time-space difference characteristic of a source side aiming at the new energy output, and an upper layer two-stage P2G mixed energy storage energy flow model is established with the aim of lowest operation cost, so that the multiparty benefit of the operation process is researched; the electricity price and the carbon price are used as signals to guide the demand response, and the aim is to minimize the purchase energy cost and the carbon emission cost, so that the use of high-carbon emission intensity energy sources is further reduced; and solving the upper and lower layer models by adopting a Gurobi solver, and outputting the running result of the optimized scheduling of the system. The invention directly and efficiently utilizes new energy in the comprehensive energy system, and has good economic benefit and environmental benefit.

Description

Demand response optimization method considering two-stage P2G hybrid energy storage and carbon potential control
Technical Field
The invention relates to the technical field of operation analysis of power systems, in particular to a demand response optimization method for controlling energy storage and carbon potential by taking two-stage P2G hybrid energy into consideration.
Background
To address the challenges of global climate change, the world is actively propelling energy systems to be low-carbonized and clean. With the increasing proportion of renewable energy sources connected into a power system, the phenomenon of mismatching between wind-solar output and load demand space-time is increasingly serious, the problems of low new energy consumption rate, poor energy supply economy and the like are brought, and energy waste and economic loss are caused. Therefore, how to efficiently utilize the accessed high-proportion new energy while considering economic and environmental benefits is a research aspect that is urgent to pay attention.
In terms of improving the utilization rate of new energy, most of researches at present respectively provide solutions for the two aspects of a source side and a load side. On the source side, the key is the coordination with the energy storage system, so that the energy storage system has the advantages of large capacity, long storage time, low investment cost and small carbon emission, and the traditional and single energy storage is difficult to simultaneously meet the requirements; on the load side, the initiative and the enthusiasm of the user participation are exerted, and the original flexibility of the system is activated and released by considering a demand response mechanism. Therefore, the influence of both sides of the source load on the energy demand matching process needs to be considered while the economic benefit and the environmental benefit are considered, and the comprehensive energy system collaborative optimization scheduling of the two-stage P2G hybrid energy storage and the carbon potential control demand response is considered, so that the comprehensive energy system collaborative optimization scheduling can provide more economic and environmental protection values.
At present, in order to solve the problem of unmatched space-time of new energy output and user electrical load demands, optimal configuration is calculated on projects in two aspects of source load, mostly, a small program is independently designed, and a systematic full-adaptability optimization algorithm considering two-stage P2G hybrid energy storage and carbon potential control demand response is lacked.
Disclosure of Invention
The invention aims to solve the technical problems by providing a method for optimizing response of a two-stage P2G hybrid energy storage and carbon potential control requirement so as to solve the problem of mismatching of wind light output and electric load requirement in a comprehensive energy system under the drive of a carbon target, and constructing a double-layer collaborative optimization scheduling model of the comprehensive energy system by considering energy flow economic operation and carbon discharge target integration from the viewpoint of collaborative optimization in multiple aspects of source-storage-load, and improving the problem of mismatching of supply and requirement by optimizing output and electric load curves of various devices, and simultaneously improving the economy and low carbon property of the system.
In order to solve the technical problems, the invention adopts the following technical scheme: the two-stage P2G hybrid energy storage and carbon potential control demand response optimization method is considered, and the method comprises the following steps:
step one: determining the system composition of the comprehensive energy system;
step two: refining the P2G into a two-stage operation process of replacing the traditional P2G by specific combined equipment;
step three: establishing an upper P2G hybrid energy storage related equipment mathematical model;
step four: determining the overall objective function and constraint condition of each unit and equipment participating in scheduling to obtain an upper scheduling model;
step five: establishing a lower carbon potential control demand response mechanism model;
step six: determining an overall objective function and constraint conditions under the participation of demand response in scheduling to obtain a lower scheduling model;
step seven: establishing a double-layer collaborative optimization day-ahead scheduling model of the comprehensive energy system;
step eight: solving the double-layer model established in the step seven by adopting a Gurobi solver;
step nine: and outputting an optimized operation result of the dual-layer collaborative optimization day-ahead scheduling model of the comprehensive energy system.
The technical scheme of the invention is further improved as follows: the first step is that the comprehensive energy system comprises a conventional unit, a wind turbine unit WT, photovoltaic equipment PV, an electrolytic tank EL, a fuel cell FC, a methanation device MR, a gas turbine GT and a battery energy storage device Bat.
The technical scheme of the invention is further improved as follows: the second step is to refine the P2G into a two-stage operation process of replacing the traditional P2G by EL, MR, FC and GT combined equipment, wherein the two-stage operation process is divided into electric hydrogen conversion and electric natural gas conversion, and an efficient and energy-type two-stage electric-electric energy flow ring is formed by the two-stage operation process and the fuel cell and the micro gas engine respectively.
The technical scheme of the invention is further improved as follows: in the fourth step, the objective function is determined as follows:
the upper model aims at the minimum cost of the hybrid energy storage economic operation, namely:
Figure BDA0004110279340000021
the method comprises the steps of gas purchase cost, electricity purchase cost to a power grid, energy storage charge and discharge cost, wind abandon and light abandon punishment cost, gas selling income and electricity selling income to a user side, wherein specific objective functions are as follows:
1) Cost of purchasing gas
Figure BDA0004110279340000031
Wherein: a is the purchase price of natural gas, P GT (t) is the active output of the gas turbine at the moment t GT,e The power generation efficiency coefficient of the gas turbine is;
2) Cost of purchasing electricity to power grid
C G ＝λ t P G (t)
Wherein: lambda (lambda) t The time-sharing electricity price and P of the main network at the time t are G (t) purchasing active power and electricity purchasing cost from a main network by a system at the moment t;
3) Energy storage charge and discharge costs
Figure BDA0004110279340000032
Wherein: r is R X 、P X (t) respectively representing the charge and discharge coefficients of the devices and the active force at the time t;
4) Wind and light discarding punishment cost
C AB ＝ω AB P AB (t)
Wherein: omega AB Punishment cost, P for wind and light abandoning unit AB (t) is total power of abandoned wind and abandoned light, C AB Punishment cost for wind and light discarding;
5) Benefit of gas selling
Figure BDA0004110279340000033
Wherein: b is CH 4 The price to be sold is set to be,
Figure BDA0004110279340000034
for injecting the natural gas network CH at time t 4 Active power value, & gt>
Figure BDA0004110279340000035
For injection into natural gas networks 4 The income brought by the method;
6) Selling electricity revenue to a user
S e ＝λ e P e (t)
Wherein: lambda (lambda) e Time-of-use electricity price representing electricity selling to user side, P e (t) electric power sold per unit time, S e And the selling income of the user is obtained.
The technical scheme of the invention is further improved as follows: the constraint condition is determined in the fourth step as follows:
determining operating requirements
(1) Electric power balance constraint
Figure BDA0004110279340000041
Wherein: p (P) WT (t) is the output of the wind turbine generator at the time t, P PV (t) is the output of the photovoltaic equipment at the moment t, P GT (t) is the output of the gas turbine at the time t,
Figure BDA0004110279340000042
for discharging power of the battery energy storage device at t time, P fc (t) is the output of the fuel cell at t, P G (t) is the active power purchased by the system to the main network at the moment t, P load (t) is the electric load value of the power grid at the moment t,
Figure BDA0004110279340000043
for charging power of the battery energy storage device at t time, P el (t) is the power consumption of the electrolyzer at the moment t,/for the electrolyzer>
Figure BDA0004110279340000044
Injecting a natural gas network CH at time t 4 Active power of (2);
(2) Gas turbine constraints:
P GT,min ≤P GT (t)≤P GT,max
Figure BDA0004110279340000045
wherein: p (P) GT,max 、P GT,min The upper and lower limits of the output of the gas turbine are respectively;
Figure BDA0004110279340000046
the upper limit and the lower limit of the climbing speed of the gas turbine are respectively;
(3) Interaction power constraint with power grid
P G,min ≤P G (t)≤P G,max
Wherein: p (P) G,max 、P G,min The upper and lower limits of the interaction power of the system and the power grid are respectively set;
(4) Energy storage device restraint
1) Battery energy storage device operation constraints
Figure BDA0004110279340000047
Figure BDA0004110279340000048
Wherein:
Figure BDA0004110279340000051
respectively the minimum and maximum capacities of the electric energy storage device;
2) Hydrogen energy storage system operating constraints
P fc,min ≤P fc (t)≤P fc,max
P el,min ≤P el (t)≤P el,max
Q hs,min ≤Q hs (t)≤Q hs,max
P op (t)＝a fc P fc (t)+b el P el (t)
a fc +b el ≤1
Wherein: p (P) fc,max 、P fc,min The upper and lower limits of the output of the fuel cell are respectively; p (P) el,max 、P el,min The upper and lower limits of the output force of the electrolytic tank are respectively set; q (Q) hs,max 、Q hs,min The upper limit and the lower limit of the capacity of the hydrogen storage tank are respectively; po (Po) p (t) is the operating power of the hydrogen energy system; a, a fc 、b el The working zone bit of the fuel cell and the electrolytic tank is respectively 1 when working and 0 when not working;
the technical scheme of the invention is further improved as follows: in the sixth step, the objective function is determined specifically as follows:
determining an objective function
The lower model aims at minimizing the sum of the electricity purchasing cost and the carbon emission cost of the user, namely:
Figure BDA0004110279340000052
wherein: c (C) e Indicating the cost of electricity purchased by the user,
Figure BDA0004110279340000053
representing carbon emission costs;
1) Cost of electricity purchase for users
C e ＝λ e P e (t)
Wherein: lambda (lambda) e Indicating the electricity price of electricity purchasing time, P e (t) purchasing electric power per unit time;
2) Carbon emission cost
Figure BDA0004110279340000061
Wherein:
Figure BDA0004110279340000062
for the carbon trade cost of the load at time t lambda c Is the basic price of carbon transaction, and the unit is Yuan/tco 2 D is the interval length of carbon trade, and sigma is the price increase coefficient of ladder carbon trade.
The technical scheme of the invention is further improved as follows: in the sixth step, the constraint condition is determined specifically as follows:
determining operating requirements
(1) Transferable load constraints
1) Transfer power range:
y trans,t P trans,max ≤P trans,t ≤y trans,t P trans,min
wherein: p (P) trans,t For t period of time, the electric load can be transferred to participate in the regulated load power, P trans,max Representing maximum power involved in transfer regulation, P trans,min Representing the minimum power involved in the regulation of the transfer.
2) Total transfer power:
for the electricity transferable load in the dispatching period T, the total power of the load before and after the adjustment is not changed, namely
Figure BDA0004110279340000063
Wherein: p (P) trans0,t Indicating that the electrically transferable load is involved in the pre-regulation load power during period t.
3) Minimum transfer time:
Figure BDA0004110279340000064
wherein: t (T) trans,min Representing the minimum transition time that the transferable electrical load is required to meet in connection with the transfer regulation.
(2) Carbon trade constraints
Figure BDA0004110279340000071
(3) Demand response constraints
Figure BDA0004110279340000072
Figure BDA0004110279340000073
Wherein: e, e v E is the average carbon emission intensity of all power generation equipment G,i N is the number of power generation devices for the carbon emission intensity of the power generation devices. By this constraint the dependence of the load on the high carbon intensity energy source can be limited.
The technical scheme of the invention is further improved as follows: the model solving step eight specifically comprises the following steps:
(1) Inputting basic data such as equipment parameters, wind, light, load predicted values and the like;
(2) Solving an upper hybrid energy storage energy flow economic operation model, outputting energy storage charging and discharging power, generating equipment output power and carbon emission intensity, and calculating load carbon potential and carbon emission;
(3) According to the difference of load on power generation side electric energy consumption at different moments, a reasonable carbon responsibility range of the load side is formulated, step carbon price is calculated, and the carbon emission cost of the load side is solved;
(4) Solving a lower carbon potential control demand response scheduling model, and optimizing and adjusting the load;
(5) Substituting the adjusted load data into an upper layer model, re-solving the upper layer scheduling model, repeatedly iterating the upper layer scheduling model and the lower layer scheduling model, and finally outputting the optimal operation scheme of the system.
The technical scheme of the invention is further improved as follows: the output result in the step nine is specifically: the method comprises the steps of outputting, discarding wind, discarding light quantity, purchasing power, methane injection power, and responding to the electric load condition before and after the demand of each device such as EL, MR, FC, GT, bat.
By adopting the technical scheme, the invention has the following technical progress: the P2G is refined into the selected specific combined equipment to replace the traditional two-stage operation process of the P2G, so that the advantage of high energy efficiency of hydrogen can be fully exerted; the carbon potential of the load is controlled by guiding the demand response by taking electricity price and carbon price as signals, and the carbon emission of the system is reduced by utilizing the load side regulating capability; by establishing a double-layer collaborative optimization scheduling model of the comprehensive energy system, the economical efficiency and low carbon property of the system can be effectively improved, and the method has important investment value.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art;
FIG. 1 is a two-stage P2G hybrid stored energy flow diagram;
FIG. 2 is a framework diagram of a dual-layer collaborative optimization day-ahead dispatch model of the integrated energy system;
FIG. 3 is a two-layer model solving flow diagram;
Detailed Description
The invention is further illustrated by the following examples:
the method for optimizing the demand response of the two-stage P2G hybrid energy storage and carbon potential control is considered, and comprises the following specific steps: step one: determining the system composition of the comprehensive energy system;
step two: refining the P2G into a two-stage operation process of replacing the traditional P2G by specific combined equipment;
step three: establishing an upper P2G hybrid energy storage related equipment mathematical model;
step four: determining the overall objective function and constraint condition of each unit and equipment participating in scheduling to obtain an upper scheduling model;
step five: establishing a lower carbon potential control demand response mechanism model;
step six: determining an overall objective function and constraint conditions under the participation of demand response in scheduling to obtain a lower scheduling model;
step seven: establishing a double-layer collaborative optimization day-ahead scheduling model of the comprehensive energy system;
step eight: solving the double-layer model established in the step seven by adopting a Gurobi solver;
step nine: and outputting an optimized operation result of the dual-layer collaborative optimization day-ahead scheduling model of the comprehensive energy system.
Example 1
Step one: determining the system composition of the comprehensive energy system:
the comprehensive energy system comprises a conventional unit, a wind turbine unit WT, photovoltaic equipment PV, an electrolytic tank EL, a fuel cell FC, a methanation device MR, a gas turbine GT and a battery energy storage device Bat;
step two: refinement of P2G into a two-phase run process with EL, MR, FC and GT combined devices replacing the traditional P2G:
aiming at the time-space difference characteristic of new energy output, P2G is refined into a two-stage operation process of replacing the traditional P2G by EL, MR, FC and GT combined equipment, and the two-stage operation process is specifically divided into electric hydrogen conversion and electric natural gas conversion, and an efficient and energy-type two-stage electric-gas-electric energy flow ring is formed by the P2G combined equipment, the fuel cell and the micro gas engine respectively. The basic idea of two-stage P2G hybrid energy storage combined operation is shown in fig. 1.
The first stage: when the wind-solar energy resources of the system are fewer, the energy flows only in the first stage, the first stage comprises an electric hydrogen production link and a fuel cell power generation link, the electric hydrogen conversion process only generates hydrogen and oxygen, no pollution gas is generated, and the process is simple. When the surplus electric quantity exists, the storage battery with quick response capability is charged; electrolytic hydrogen production to convert electrical energy into H 2 Compressed to a hydrogen storage tank for long-time storage and can be supplied to a fuel cell; when the system power is deficient, the storage battery discharges and the fuel cell is started to generate electricity, H 2 Is converted into electric energy to supply electric load. Thus, the electricity-to-hydrogen and the hydrogen fuel cell power generation form an electricity-gas-electricity energy flow ring with high-efficiency electricity-gas bidirectional coupling in the first stage; energy loss caused in the methane synthesis process can be avoided in the first stage, and the economic utilization of energy can be realized.
And a second stage: when the wind-solar energy resource of the system is greatly excessive, energy overflows from the energy ring in the first stage, enters the second stage, and further utilizes H through electricity to natural gas 2 Capturing CO in the atmosphere and gas turbine 2 Synthesizing methane, and supplying the methane to a gas turbine or injecting the methane into a natural gas pipeline for large-capacity storage; when the system power is deficient, the gas turbine is started to generate electricity, chemical energy in the natural gas is converted into electric energy, and the electric load is supplied. The electricity-to-natural gas and the gas turbine generate electricity to form the energy type electricity-gas-electricity energy flow ring with two-way coupling of electricity-gas in the second stage. Although the influence of energy loss caused in the methanation process exists, the waste of new energy can be avoided, and the energy efficiency level is improved; carbon dioxide generated in the gas turbine and the atmosphere can be captured, and a large amount of CO is avoided 2 The gas is discharged into the air to pollute the environment, so that the dynamic utilization and the non-forced discharge of carbon can be realized, the carbon discharge is further reduced, and the high-efficiency and environment-friendly utilization of energy sources can be realized.
The power grid is coupled with the natural gas network through the P2G conversion unit, so that the time, space and energy form conversion of renewable energy source utilization is realized, and meanwhile, the interaction between the power grid and the gas network can be relieved. When the residual wind power of the system is less, the energy flows through the first-stage high-efficiency energy flow ring to realize high-efficiency energy utilization; when the residual wind power of the system is more, the energy flow passes through the energy flow ring of the second stage to realize energy source scale utilization.
Step three: establishing an upper P2G hybrid energy storage related equipment mathematical model;
step four: determining the overall objective function and constraint condition of each unit and equipment participating in scheduling to obtain an upper scheduling model;
the determining objective function is specifically:
the upper model aims at the minimum cost of the hybrid energy storage economic operation, namely:
Figure BDA0004110279340000101
the method comprises the steps of gas purchase cost, electricity purchase cost to a power grid, energy storage charge and discharge cost, wind abandon and light abandon punishment cost, gas selling income and electricity selling income to a user side, wherein specific objective functions are as follows:
1) Cost of purchasing gas
Figure BDA0004110279340000102
Wherein: a is the purchase price of natural gas, P GT (t) is the active output of the gas turbine at the moment t GT,e The power generation efficiency coefficient of the gas turbine is;
2) Cost of purchasing electricity to power grid
C G ＝λ t P G (t)
Wherein: lambda (lambda) t The time-sharing electricity price and P of the main network at the time t are G (t) purchasing active power and electricity purchasing cost from a main network by a system at the moment t;
3) Energy storage charge and discharge costs
Figure BDA0004110279340000103
Wherein: r is R X 、P X (t) respectively representing the charge and discharge coefficients of the devices and the active force at the time t;
4) Wind and light discarding punishment cost
C AB ＝ω AB P AB (t)
Wherein: omega AB Punishment cost, P for wind and light abandoning unit AB (t) is total power of abandoned wind and abandoned light, C AB Punishment cost for wind and light discarding;
5) Benefit of gas selling
Figure BDA0004110279340000111
Wherein: b is CH 4 The price to be sold is set to be,
Figure BDA0004110279340000112
for injecting the natural gas network CH at time t 4 Active power value, & gt>
Figure BDA0004110279340000113
For injection into natural gas networks 4 The income brought by the method;
6) Selling electricity revenue to a user
S e ＝λ e P e (t)
Wherein: lambda (lambda) e Time-of-use electricity price representing electricity selling to user side, P e (t) electric power sold per unit time, S e And the selling income of the user is obtained.
The constraint condition is determined specifically as follows:
determining operating requirements
(1) Electric power balance constraint
Figure BDA0004110279340000114
Wherein: p (P) WT (t) is windOutput of motor unit at t moment, P PV (t) is the output of the photovoltaic equipment at the moment t, P GT (t) is the output of the gas turbine at the time t,
Figure BDA0004110279340000115
for discharging power of the battery energy storage device at t time, P fc (t) is the output of the fuel cell at t, P G (t) is the active power purchased by the system to the main network at the moment t, P load (t) is the electric load value of the power grid at the moment t,
Figure BDA0004110279340000116
for charging power of the battery energy storage device at t time, P el (t) is the power consumption of the electrolyzer at the moment t,/for the electrolyzer>
Figure BDA0004110279340000117
Injecting a natural gas network CH at time t 4 Active power of (2);
(2) Gas turbine constraints:
P GT,min ≤P GT (t)≤P GT,max
Figure BDA0004110279340000121
wherein: p (P) GT,max 、P GT,min The upper and lower limits of the output of the gas turbine are respectively;
Figure BDA0004110279340000122
the upper limit and the lower limit of the climbing speed of the gas turbine are respectively;
(3) Interaction power constraint with power grid
P G,min ≤P G (t)≤P G,max
Wherein: p (P) G,max 、P G,min The upper and lower limits of the interaction power of the system and the power grid are respectively set;
(4) Energy storage device restraint
1) Battery energy storage device operation constraints
Figure BDA0004110279340000123
Figure BDA0004110279340000124
Wherein:
Figure BDA0004110279340000125
respectively the minimum and maximum capacities of the electric energy storage device;
2) Hydrogen energy storage system operating constraints
P fc,min ≤P fc (t)≤P fc,max
P el,min ≤P el (t)≤P el,max
Q hs,min ≤Q hs (t)≤Q hs,max
P op (t)＝a fc P fc (t)+b el P el (t)
a fc +b el ≤1
Wherein: p (P) fc,max 、P fc,min The upper and lower limits of the output of the fuel cell are respectively; p (P) el,max 、P el,min The upper and lower limits of the output force of the electrolytic tank are respectively set; q (Q) hs,max 、Q hs,min The upper limit and the lower limit of the capacity of the hydrogen storage tank are respectively; po (Po) p (t) is the operating power of the hydrogen energy system; a, a fc 、b el The working zone bit of the fuel cell and the electrolytic tank is respectively 1 when working and 0 when not working;
step five: establishing a lower carbon potential control demand response mechanism model;
step six: determining an overall objective function and constraint conditions under the condition that demand response participates in scheduling, and obtaining a lower-layer scheduling model, wherein the determination objective function is specifically as follows:
determining an objective function
The lower model aims at minimizing the sum of the electricity purchasing cost and the carbon emission cost of the user, namely:
Figure BDA0004110279340000131
wherein: c (C) e Indicating the cost of electricity purchased by the user,
Figure BDA0004110279340000132
representing carbon emission costs;
1) Cost of electricity purchase for users
C e ＝λ e P e (t)
Wherein: lambda (lambda) e Indicating the electricity price of electricity purchasing time, P e (t) purchasing electric power per unit time;
2) Carbon emission cost
Figure BDA0004110279340000133
Wherein:
Figure BDA0004110279340000134
for the carbon trade cost of the load at time t lambda c Is the basic price of carbon transaction, and the unit is Yuan/tco 2 D is the interval length of carbon trade, and sigma is the price increase coefficient of ladder carbon trade.
The constraint condition is determined specifically as follows:
determining operating requirements
(1) Transferable load constraints
1) Transfer power range:
y trans,t P trans,max ≤P trans,t ≤y trans,t P trans,min
wherein: p (P) trans,t For t period of time, the electric load can be transferred to participate in the regulated load power, P trans,max Representing maximum power involved in transfer regulation, P trans,min Representing the minimum power involved in the regulation of the transfer.
2) Total transfer power:
for the electricity transferable load in the dispatching period T, the total power of the load before and after the adjustment is not changed, namely
Figure BDA0004110279340000141
Wherein: p (P) trans0,t Indicating that the electrically transferable load is involved in the pre-regulation load power during period t.
3) Minimum transfer time:
Figure BDA0004110279340000142
wherein: t (T) trans,min Representing the minimum transition time that the transferable electrical load is required to meet in connection with the transfer regulation.
(2) Carbon trade constraints
Figure BDA0004110279340000143
(3) Demand response constraints
Figure BDA0004110279340000144
Figure BDA0004110279340000145
Wherein: e, e v E is the average carbon emission intensity of all power generation equipment G,i N is the number of power generation devices for the carbon emission intensity of the power generation devices. By this constraint the dependence of the load on the high carbon intensity energy source can be limited.
Step seven: according to the upper layer scheduling model and the lower layer scheduling model, a double-layer collaborative optimization day-ahead scheduling model of the comprehensive energy system is established through a Yalmip modeling language: a two-layer model frame diagram is shown in fig. 2.
Step eight: solving the double-layer model established in the step seven by adopting a Gurobi solver:
the model solving flowchart is shown in fig. 3.
(1) Inputting basic data such as equipment parameters, wind, light, load predicted values and the like;
(2) Solving an upper hybrid energy storage energy flow economic operation model, outputting energy storage charging and discharging power, generating equipment output power and carbon emission intensity, and calculating load carbon potential and carbon emission;
(3) According to the difference of load on power generation side electric energy consumption at different moments, a reasonable carbon responsibility range of the load side is formulated, step carbon price is calculated, and the carbon emission cost of the load side is solved;
(4) Solving a lower carbon potential control demand response scheduling model, and optimizing and adjusting the load;
(5) Substituting the adjusted load data into an upper layer model, re-solving the upper layer scheduling model, repeatedly iterating the upper layer scheduling model and the lower layer scheduling model, and finally outputting the optimal operation scheme of the system.
Step nine: and outputting an optimized operation result of a double-layer collaborative optimization day-ahead dispatching model of the comprehensive energy system, wherein the optimized operation result comprises output force, waste wind and waste light quantity, purchase power, methane injection power and electric load conditions before and after demand response of each equipment such as EL, MR, FC, GT, bat.
According to the invention, by establishing the double-layer collaborative optimization scheduling model of the comprehensive energy system, the economy and low carbon of the system can be effectively improved, and the method has important investment value.
The above examples are only illustrative of the preferred embodiments of the present invention and are not intended to limit the scope of the present invention, and various modifications and improvements made by those skilled in the art to the technical solution of the present invention should fall within the scope of protection defined by the claims of the present invention without departing from the spirit of the design of the present invention.

Claims (9)

1. The method for optimizing the demand response of the two-stage P2G hybrid energy storage and carbon potential control is considered, and is characterized by comprising the following steps:
step one: determining the system composition of the comprehensive energy system;
step two: refining the P2G into a two-stage operation process of replacing the traditional P2G by specific combined equipment;
step three: establishing an upper P2G hybrid energy storage related equipment mathematical model;
step four: determining the overall objective function and constraint condition of each unit and equipment participating in scheduling to obtain an upper scheduling model;
step five: establishing a lower carbon potential control demand response mechanism model;
step six: determining an overall objective function and constraint conditions under the participation of demand response in scheduling to obtain a lower scheduling model;
step seven: establishing a double-layer collaborative optimization day-ahead scheduling model of the comprehensive energy system;
step eight: solving the double-layer model established in the step seven by adopting a Gurobi solver;
step nine: and outputting an optimized operation result of the dual-layer collaborative optimization day-ahead scheduling model of the comprehensive energy system.
2. The method for optimizing the demand response of considering two-stage P2G hybrid energy storage and carbon potential control according to claim 1, wherein: the first step is that the comprehensive energy system comprises a conventional unit, a wind turbine unit WT, photovoltaic equipment PV, an electrolytic tank EL, a fuel cell FC, a methanation device MR, a gas turbine GT and a battery energy storage device Bat.
3. The method for optimizing the demand response of considering two-stage P2G hybrid energy storage and carbon potential control according to claim 1, wherein: the second step is to refine the P2G into a two-stage operation process of replacing the traditional P2G by EL, MR, FC and GT combined equipment, wherein the two-stage operation process is divided into electric hydrogen conversion and electric natural gas conversion, and an efficient and energy-type two-stage electric-electric energy flow ring is formed by the two-stage operation process and the fuel cell and the micro gas engine respectively.
4. The method for optimizing the demand response of considering two-stage P2G hybrid energy storage and carbon potential control according to claim 1, wherein: in the fourth step, the objective function is determined as follows:
the upper model aims at the minimum cost of the hybrid energy storage economic operation, namely:
Figure FDA0004110279330000011
the method comprises the steps of gas purchase cost, electricity purchase cost to a power grid, energy storage charge and discharge cost, wind abandon and light abandon punishment cost, gas selling income and electricity selling income to a user side, wherein specific objective functions are as follows:
1) Cost of purchasing gas
Figure FDA0004110279330000021
Wherein: a is the purchase price of natural gas, P GT (t) is the active output of the gas turbine at the moment t GT,e The power generation efficiency coefficient of the gas turbine is;
2) Cost of purchasing electricity to power grid
C G ＝λ t P G (t)
Wherein: lambda (lambda) t The time-sharing electricity price and P of the main network at the time t are G (t) purchasing active power and electricity purchasing cost from a main network by a system at the moment t;
3) Energy storage charge and discharge costs
Figure FDA0004110279330000022
Wherein: r is R X 、P X (t) respectively representing the charge and discharge coefficients of the devices and the active force at the time t;
4) Wind and light discarding punishment cost
C AB ＝ω AB P AB (t)
Wherein: omega AB Punishment cost, P for wind and light abandoning unit AB (t) is total power of abandoned wind and abandoned light, C AB Punishment cost for wind and light discarding;
5) Benefit of gas selling
Figure FDA0004110279330000023
Wherein: b is CH 4 The price to be sold is set to be,
Figure FDA0004110279330000024
for injecting the natural gas network CH at time t 4 Active power value, & gt>
Figure FDA0004110279330000025
For injection into natural gas networks 4 The income brought by the method;
6) Selling electricity revenue to a user
S e ＝λ e P e (t)
Wherein: lambda (lambda) e Time-of-use electricity price representing electricity selling to user side, P e (t) electric power sold per unit time, S e And the selling income of the user is obtained.
5. The method for optimizing the demand response of considering two-stage P2G hybrid energy storage and carbon potential control according to claim 1, wherein: the constraint condition is determined in the fourth step as follows:
determining operating requirements
(1) Electric power balance constraint
Figure FDA0004110279330000031
Wherein: p (P) WT (t) is the output of the wind turbine generator at the time t, P PV (t) is the output of the photovoltaic equipment at the moment t, P GT (t) is the output of the gas turbine at the time t,
Figure FDA0004110279330000032
for discharging power of the battery energy storage device at t time, P fc (t) is the output of the fuel cell at t, P G (t) is the active power purchased by the system to the main network at the moment t, P load (t) grid electronegativity at t momentThe value of the load is set to be equal to the value of the load,
Figure FDA0004110279330000033
for charging power of the battery energy storage device at t time, P el (t) is the power consumption of the electrolyzer at the moment t,/for the electrolyzer>
Figure FDA0004110279330000034
Injecting a natural gas network CH at time t 4 Active power of (2);
(2) Gas turbine constraints:
P GT,min ≤P GT (t)≤P GT,max
Figure FDA0004110279330000035
wherein: p (P) GT,max 、P GT,min The upper and lower limits of the output of the gas turbine are respectively;
Figure FDA0004110279330000036
the upper limit and the lower limit of the climbing speed of the gas turbine are respectively;
(3) Interaction power constraint with power grid
P G,min ≤P G (t)≤P G,max
Wherein: p (P) G,max 、P G,min The upper and lower limits of the interaction power of the system and the power grid are respectively set;
(4) Energy storage device restraint
1) Battery energy storage device operation constraints
Figure FDA0004110279330000037
Figure FDA0004110279330000041
Wherein:
Figure FDA0004110279330000042
respectively the minimum and maximum capacities of the electric energy storage device; />
2) Hydrogen energy storage system operating constraints
P fc,min ≤P fc (t)≤P fc,max
P el,min ≤P el (t)≤P el,max
Q hs,min ≤Q hs (t)≤Q hs,max
P op (t)＝a fc P fc (t)+b el P el (t)
a fc +b el ≤1
Wherein: p (P) fc,max 、P fc,min The upper and lower limits of the output of the fuel cell are respectively; p (P) el,max 、P el,min The upper and lower limits of the output force of the electrolytic tank are respectively set; q (Q) hs,max 、Q hs,min The upper limit and the lower limit of the capacity of the hydrogen storage tank are respectively; p (P) op (t) is the operating power of the hydrogen energy system; a, a fc 、b el The operation zone bit of the fuel cell and the electrolytic tank is respectively 1 when in operation and 0 when not in operation.
6. The method for optimizing the demand response of considering two-stage P2G hybrid energy storage and carbon potential control according to claim 1, wherein: in the sixth step, the objective function is determined specifically as follows:
determining an objective function
The lower model aims at minimizing the sum of the electricity purchasing cost and the carbon emission cost of the user, namely:
Figure FDA0004110279330000043
wherein: c (C) e Indicating the cost of electricity purchased by the user,
Figure FDA0004110279330000044
representing carbon emission costs;
1) Cost of electricity purchase for users
C e ＝λ e P e (t)
Wherein: lambda (lambda) e Indicating the electricity price of electricity purchasing time, P e (t) purchasing electric power per unit time;
2) Carbon emission cost
Figure FDA0004110279330000051
Wherein:
Figure FDA0004110279330000052
for the carbon trade cost of the load at time t lambda c Is the basic price of carbon transaction, and the unit is Yuan/tco 2 D is the interval length of carbon trade, and sigma is the price increase coefficient of ladder carbon trade.
7. The method for optimizing the demand response of considering two-stage P2G hybrid energy storage and carbon potential control according to claim 1, wherein: in the sixth step, the constraint condition is determined specifically as follows:
determining operating requirements
(1) Transferable load constraints
1) Transfer power range:
y trans,t P trans,max ≤P trans,t ≤y trans,t P trans,min
wherein: p (P) trans,t For t period of time, the electric load can be transferred to participate in the regulated load power, P trans,max Representing maximum power involved in transfer regulation, P trans,min Representing the minimum power involved in the regulation of the transfer.
2) Total transfer power:
for the electricity transferable load in the dispatching period T, the total power of the load before and after the adjustment is not changed, namely
Figure FDA0004110279330000053
Wherein: p (P) trans0,t Indicating that the electrically transferable load is involved in the pre-regulation load power during period t.
3) Minimum transfer time:
Figure FDA0004110279330000054
wherein: t (T) trans,min Representing the minimum transition time that the transferable electrical load is required to meet in connection with the transfer regulation.
(2) Carbon trade constraints
Figure FDA0004110279330000061
(3) Demand response constraints
Figure FDA0004110279330000062
Figure FDA0004110279330000063
Wherein: e, e v E is the average carbon emission intensity of all power generation equipment G,i N is the number of power generation devices for the carbon emission intensity of the power generation devices. By this constraint the dependence of the load on the high carbon intensity energy source can be limited.
8. The method for optimizing the demand response of considering two-stage P2G hybrid energy storage and carbon potential control according to claim 1, wherein: the model solving step eight specifically comprises the following steps:
(1) Inputting basic data such as equipment parameters, wind, light, load predicted values and the like;
(2) Solving an upper hybrid energy storage energy flow economic operation model, outputting energy storage charging and discharging power, generating equipment output power and carbon emission intensity, and calculating load carbon potential and carbon emission;
(3) According to the difference of load on power generation side electric energy consumption at different moments, a reasonable carbon responsibility range of the load side is formulated, step carbon price is calculated, and the carbon emission cost of the load side is solved;
(4) Solving a lower carbon potential control demand response scheduling model, and optimizing and adjusting the load;
(5) Substituting the adjusted load data into an upper layer model, re-solving the upper layer scheduling model, repeatedly iterating the upper layer scheduling model and the lower layer scheduling model, and finally outputting the optimal operation scheme of the system.
9. The method for optimizing the demand response of considering two-stage P2G hybrid energy storage and carbon potential control according to claim 1, wherein: the output result in the step nine is specifically: the method comprises the steps of outputting, discarding wind, discarding light quantity, purchasing power, methane injection power, and responding to the electric load condition before and after the demand of each device such as EL, MR, FC, GT, bat.
CN202310204428.7A 2023-03-06 2023-03-06 Demand response optimization method considering two-stage P2G hybrid energy storage and carbon potential control Pending CN116093949A (en)

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116896096A (en) * 2023-06-07 2023-10-17 国网湖北省电力有限公司经济技术研究院 Low-carbon optimal operation method and system for power distribution network containing energy storage equipment
CN117458485A (en) * 2023-12-22 2024-01-26 国网湖北省电力有限公司经济技术研究院 Method and system for realizing operation optimization scheduling of power system based on carbon reduction potential

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116896096A (en) * 2023-06-07 2023-10-17 国网湖北省电力有限公司经济技术研究院 Low-carbon optimal operation method and system for power distribution network containing energy storage equipment
CN116896096B (en) * 2023-06-07 2024-03-22 国网湖北省电力有限公司经济技术研究院 Low-carbon optimal operation method and system for power distribution network containing energy storage equipment
CN117458485A (en) * 2023-12-22 2024-01-26 国网湖北省电力有限公司经济技术研究院 Method and system for realizing operation optimization scheduling of power system based on carbon reduction potential
CN117458485B (en) * 2023-12-22 2024-02-27 国网湖北省电力有限公司经济技术研究院 Method and system for realizing operation optimization scheduling of power system based on carbon reduction potential

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