CN112086960A - Model prediction control-based flexible margin calculation method for electro-hydrogen coupling system - Google Patents

Model prediction control-based flexible margin calculation method for electro-hydrogen coupling system Download PDF

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CN112086960A
CN112086960A CN202010915404.9A CN202010915404A CN112086960A CN 112086960 A CN112086960 A CN 112086960A CN 202010915404 A CN202010915404 A CN 202010915404A CN 112086960 A CN112086960 A CN 112086960A
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power
hydrogen
energy
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CN112086960B (en
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孔令国
边育栋
蔡国伟
刘闯
于家敏
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Northeast Electric Power University
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Northeast Dianli University
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381—Dispersed generators
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/22—The renewable source being solar energy
    • H02J2300/24—The renewable source being solar energy of photovoltaic origin
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/28—The renewable source being wind energy
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/30—The power source being a fuel cell
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/50—Photovoltaic [PV] energy
    • Y02E10/56—Power conversion systems, e.g. maximum power point trackers
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02E60/30—Hydrogen technology
    • Y02E60/36—Hydrogen production from non-carbon containing sources, e.g. by water electrolysis
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E70/00—Other energy conversion or management systems reducing GHG emissions
    • Y02E70/30—Systems combining energy storage with energy generation of non-fossil origin

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Abstract

The invention relates to a model predictive control-based method for calculating the flexible margin of an electro-hydrogen coupling system, which is characterized by comprising the following steps: the method comprises the following steps of constructing a state space model of the hydrogen-electricity coupling system, constraint conditions required to be met by the model, power regulation and control solution of the hydrogen-electricity coupling system based on model prediction control, evaluation indexes of the hydrogen-electricity coupling system and the like. The method has stronger robustness, can carry out real-time analysis on the flexibility margin of the electro-hydrogen coupling system, and improves the flexibility of the system. The stability is good, the adaptability is strong, and the practical application value is high. The method has guiding significance for the optimized operation deep research and the commercial application of the renewable hydrogen coupled multifunctional system.

Description

Model prediction control-based flexible margin calculation method for electro-hydrogen coupling system
Technical Field
The invention discloses a model prediction control-based method for calculating the flexibility margin of an electro-hydrogen coupling system, which is applied to the research on energy management analysis, system power regulation and control and system flexibility margin calculation of the electro-hydrogen coupling system.
Background
The prior art models the electro-hydrogen coupling system, lacks multidimensional quantitative recognition on flexibility, has one-sided and single flexibility evaluation index, is difficult to reveal flexible operation boundary of the system by the flexibility evaluation, and can only be brought into decision by a heuristic method. Therefore, a heterogeneous energy homogenization model and a method suitable for coordination flexibility evaluation of high-proportion renewable energy are required to be established. The method has guiding significance for the development and utilization of wind and solar power generation hydrogen production systems.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a flexible margin calculation method of an electric-hydrogen coupling system based on model predictive control, which is scientific and reasonable, can carry out analysis in real time and has higher practical application value. The method is suitable for energy management analysis of the electro-hydrogen coupling system, system power regulation and control and system flexible margin calculation research.
The purpose of the invention is realized by the following technical scheme: a method for calculating the flexibility margin of an electric hydrogen coupling system based on model predictive control is characterized by comprising the following steps:
1) construction of state space model of electro-hydrogen coupling system
The wind power plant model is as follows:
Cwdxw＝0＝ζw-ηgen,wpgen,wΔt-ww (1)
wherein, CwRepresenting capacity of wind farm energy storage, dxwFor the state of charge change value, ζ, of a wind farmwCalculating wind energy collected by the wind farm for collection of wind speed data; etagen,wThe generating efficiency of the wind power plant; p is a radical ofgen,wThe output power of the wind power plant; w is awThe air volume is the air volume abandoned in the dispatching cycle, and delta t is the sampling time;
the photovoltaic station model is as follows:
Cpvdxpv＝0＝ζpv-ηgen,pvpgen,pvΔt-wpv (2)
wherein, CpvRepresenting capacity of wind farm energy storage, dxpvIs the change value of the state of charge, ζ, of the photovoltaic stationpvCalculating the solar energy, eta, collected by the photovoltaic station for the collection of irradiation datagen,pvFor the efficiency of the generation of the photovoltaic power plant itself, pgen,pvIs the output power of the photovoltaic station, wpvThe light abandon amount is the scheduling period, and delta t is the sampling time;
the battery energy storage model is as follows:
Cbdxb＝ηload,bpload,bΔt-ηgen,bpgen,bΔt (3)
wherein, CbRepresenting the capacity of the battery to store energy, dxbIs the state of charge change value, η, of the batteryload,bTo the charging efficiency of the battery, pgen,bCharging power of the battery, ηgen,bRepresents the discharge efficiency of the cell, pgen,bThe discharge power of the battery is shown, and delta t is sampling time;
hydrogen energy storage system model
Chdxh＝ζh+ηload,hpload,hΔt-ηgen,hpgen,hΔt (4)
Wherein, ChIndicating the capacity of the hydrogen storage tank, dxhIs the hydrogen-charged state change value, zeta, of the batteryhFor hydrogen supply and demand, etaload,hRepresents the hydrogen production efficiency of the cell, pload,hIs the power of the electrolytic cell etagen,hRepresents the power generation efficiency of the fuel cell, pgen,hIs the output power of the fuel cell, and Δ t is the sampling time;
the gas engine set model is as follows:
Cfdxf＝ζf-ηgen,fpgen,fΔt (5)
wherein, CfIndicating the capacity of the gas reservoir, dxfIs the change value of the charged state of the gas storage tank, zetafIs the input quantity of fuel gas, etagen,fFor gas power generation efficiency, pgen,fAs a gasThe output power of the unit, delta t is sampling time;
2) constraint conditions that the model needs to satisfy
Firstly, climbing constraint: unit and load ramp rate constraint
dpload,i,min≤dpload,i≤dpload,i,max (6)
Wherein dpload,i,minAnd dpload,i,maxRespectively is a lower limit value and an upper limit value of the load climbing rate;
dpgen,i,min≤dpgen,i≤dpgen,i,max (7)
wherein dpgen,i,minAnd dpgen,i,maxRespectively a lower limit value and an upper limit value of the unit climbing rate;
output power constraint: system load and output constraints
0≤kpload,i,min≤kpload,i≤kpload,i,max (8)
Wherein p isload,i,minAnd pload,i,maxRespectively representing the upper limit and the lower limit of the load demand, wherein k is binary number and represents whether the load demand exists, 1 is present, and 0 is absent;
0≤Xpgen,i,min≤Xpgen,i≤Xpgen,i,max (9)
wherein p isgen,i,minAnd pgen,i,maxThe upper limit and the lower limit of the unit output are respectively, X is a binary number and represents whether the unit output exists, 1 is present, and 0 is absent;
③ energy storage restraint: state of charge and energy storage capacity constraints
Figure BDA0002664842430000021
Wherein x isminAnd xmaxUpper and lower limits of the state of charge, CiIndicating capacity of stored energy, C when stored energy is present>0, otherwise, when C is 0, no charge state constraint exists;
fourthly, renewable energy abandon constraint: energy input/consumption and spill energy constraints
Figure BDA0002664842430000031
Therein, ζiFor the output/consumption of external energy; w is aiTo spill energy, when ζi> 0 represents external input of energy; w is aiThe condition that the air and light are abandoned is more than or equal to 0, which means that overflow energy exists; zetai< 0 indicates external consumption of energy;
the state space discrete equation of the electro-hydrogen coupling system is as follows:
Figure BDA0002664842430000032
wherein x iswIs the state of charge, ζ, of a wind farmwCalculating wind energy, eta, collected by a wind farm for collection of wind speed datagen，wFor the efficiency of the generation of the wind farm itself, pgen，wFor the output power of the wind farm, wwFor the air-abandon of the scheduling period, xpvIs the state of charge, ζ, of the photovoltaic stationpvCalculating the solar energy, eta, collected by the photovoltaic station for the collection of irradiation datagen，pvFor the efficiency of the generation of the photovoltaic power plant itself, pgen，pvIs the output power of the photovoltaic station, wpvTo schedule the amount of light lost in a cycle, CbRepresenting the capacity of the battery to store energy, xbIs the state of charge, η, of the batteryload，bTo the charging efficiency of the battery, pload，bCharging power of the battery, ηgen，bRepresents the discharge efficiency of the cell, pgen，bIs the discharge power of the battery, ChDenotes the capacity, x, of the hydrogen storage tankhZeta charged state of the batteryhFor hydrogen supply and demand, etaload，hRepresents the hydrogen production efficiency of the cell, pload，hIs the power of the electrolytic cell etagen，hRepresents the power generation efficiency of the fuel cell, pgen，hIs the output power of the fuel cell, CfIndicating the capacity of the gas storage pipe; zetafAs gas input, xfIs the charged state of the gas storage tank etagen，fFor gas power generation efficiency, pgen，fThe output power of the gas turbine is k, the current moment is k, k +1 is the next moment, delta t is sampling time, y is a controlled output variable, and C is an output matrix;
the state variables of the electro-hydrogen coupling system are as follows:
x(k)＝[xw(k)，xpv(k)，xb(k)，xh(k)，xf(k)]T (13)
wherein x iswIs the state of charge, x, of the wind farmpvIs the state of charge, x, of the photovoltaic stationbIs the state of charge, x, of the batteryhIs the hydrogen-charged state of the cell, xfThe state is the charge state of the gas storage tank, and k is the current moment;
the control variables of the electro-hydrogen coupling system are:
u(k)＝[pgen，w(k)，pgen，pv(k)，pgen，b(k)，pload，b(k)，pgen，h(k)，pload，h(k)，pgen，f(k)，ww(k)，wpv(k)]T (14)
wherein p isgen,wFor the output power of the wind farm, pgen,pvIs the output power of the photovoltaic station, pload,bCharging power for the battery, pgen,bIs the discharge power of the battery, pload,hFor cell power, pgen,hIs the output power of the fuel cell, wwFor the air-abandon of the scheduling period, wpvThe light abandon amount is the scheduling period, and k is the current time;
disturbance variables of the electro-hydrogen coupled system are:
d(k)＝[ξw(k),ξpv(k),ξh(k),ξf(k)]T (15)
therein, ζwCalculating wind energy collected by a wind farm, ζ, for collection of wind speed datapvCalculating the solar energy collected by the photovoltaic station for the collection of irradiation data, ζhZeta supply and demand for hydrogenfThe input quantity of the fuel gas is, and k is the current moment;
the output variables of the electro-hydrogen coupling system are:
y(k)＝[xb(k),xh(k)]T (16)
wherein x isbIs the state of charge, x, of the batteryhIs the hydrogen-charged state of the battery;
the system matrix, the control matrix and the output matrix in the electro-hydrogen coupling system are as follows:
Figure BDA0002664842430000041
Figure BDA0002664842430000042
Figure BDA0002664842430000043
Figure BDA0002664842430000044
wherein: a is a system matrix, B is a control matrix, CcIs an output matrix, ηgen,wFor the efficiency of the electricity generation of the wind farm itself, ηgen,pvFor the generating efficiency of the photovoltaic power plant itself, CbRepresenting capacity of battery energy storage, ηload,bIs the charging efficiency of the battery, etagen,bRepresents the discharge efficiency of the battery, ChIndicates the capacity of the hydrogen storage tank, etaload,hRepresents the hydrogen production efficiency of the electrolytic cell, etagen,hRepresents the power generation efficiency of the fuel cell, ηgen,fGenerating efficiency for gas;
3) solving power regulation and control of electro-hydrogen coupling system based on model predictive control
The power regulation and control function of the electro-hydrogen coupling system is as follows:
Figure BDA0002664842430000051
wherein: n is a radical ofpTo predict the time domain, NcFor controlling the time domain, Q, R are weight matrices, j is 1, 2, 3 … NpJ is the objective function, y is the output variable, yrefA state quantity reference track is adopted, k is the current moment, and delta u is a control increment;
4) the evaluation indexes of the electro-hydrogen coupling system are as follows:
the power of climbing slope can not meet the index:
Figure BDA0002664842430000052
Figure BDA0002664842430000053
wherein E isIRDp for the power not meeting the specification for climbinggen,i,For the power of the unit climbing dpload,i,For load climbing power, dPnet,tRepresenting the net load climbing rate at the moment t; rhosRepresenting the flexible margin insufficiency probability in the s scene; n is a radical ofTRepresenting the number of system scheduling intervals; beta is as,tRepresenting the number of times of the flexible margin insufficiency; n is a radical ofGRepresenting the number of generator sets; n is a radical ofLRepresents the number of loads;
output power not meeting the index
Figure BDA0002664842430000054
Wherein E isIOFor output power not meeting the specification, Pnet,tRepresenting the net load power, p, at time tgen,i,For the output power of the unit, pload,i,As load power, psRepresenting the flexible margin insufficiency probability in the s scene; n is a radical ofTRepresenting the number of system scheduling intervals; beta is as,tRepresenting the number of times of the flexible margin insufficiency; n is a radical ofGRepresenting the number of generator sets; n is a radical ofLRepresents the number of loads;
fourthly, the power supply energy does not meet the index:
Figure BDA0002664842430000055
wherein E isICFor providing electric energy which does not meet the specification, Pnet,tRepresenting the net load power, p, at time tgen,i,For the output power of the unit, pload,i,As load power, psRepresenting the flexible margin insufficiency probability in the s scene; n is a radical ofTRepresenting the number of system scheduling intervals; beta is as,tRepresenting the number of times of the flexible margin insufficiency; n is a radical ofGRepresenting the number of generator sets; n is a radical ofLThe number of loads is indicated.
The method for calculating the flexible margin of the electro-hydrogen coupling system based on model predictive control is provided based on the problems of instability of a wind-solar hydrogen production system and low carbon requirements of an energy supply system, hydrogen energy plays an important role in low carbon conversion of energy, and electro-hydrogen coupling is bound to become a typical energy existence scene. Aiming at the problem of the operation flexibility of a low-carbon hydrogen-electricity coupling system containing high-proportion wind power and photovoltaic, the invention develops the calculation research of the flexibility margin of the hydrogen-electricity coupling energy block based on model predictive control. And establishing a homogenization model of various heterogeneous energy sources by analyzing the power exchange characteristics of the heterogeneous energy sources. According to the analysis of the flexible margin of the power system, flexible margin evaluation indexes of three dimensions are defined from the operation dimension of the system, an electric-hydrogen coupling energy block scheduling model is established, the power balance operation of the electric-hydrogen coupling energy block is optimized on line by utilizing a model prediction control algorithm, and meanwhile, the flexible margin of the energy block is quantitatively analyzed and calculated. The method has stronger robustness, can carry out real-time analysis on the flexibility margin of the electro-hydrogen coupling system, and improves the flexibility of the system. The stability is good, the adaptability is strong, and the practical application value is high. The method has certain guiding significance for the optimized operation deep research and the commercial application of the renewable hydrogen coupled multifunctional system.
Drawings
FIG. 1 is a schematic diagram of wind, photovoltaic, electrical load and hydrogen load power settings;
FIG. 2 is a schematic diagram of the power variation of a renewable energy, hydrogen coupled system;
FIG. 3 is a schematic diagram of the power variation of the electro-hydrogen coupling system;
FIG. 4 is a schematic diagram of a renewable energy system flexible margin analysis;
FIG. 5 is a schematic diagram of a renewable energy, hydrogen coupled system flexible margin analysis;
FIG. 6 is a schematic diagram of an analysis of flexibility margins of an electro-hydrogen coupled system.
Detailed Description
The invention is further illustrated by the following figures and specific examples.
The invention discloses a model predictive control-based method for calculating the flexible margin of an electro-hydrogen coupling system, which comprises the following steps:
1) construction of state space model of electro-hydrogen coupling system
The wind power plant model is as follows:
Cwdxw＝0＝ζw-ηgen,wpgen,wΔt-ww (1)
wherein, CwRepresenting capacity of wind farm energy storage, dxwFor the state of charge change value, ζ, of a wind farmwCalculating wind energy collected by the wind farm for collection of wind speed data; etagen,wThe generating efficiency of the wind power plant; p is a radical ofgen,wThe output power of the wind power plant; w is awThe air volume is the air volume abandoned in the dispatching cycle, and delta t is the sampling time;
the photovoltaic station model is as follows:
Cpvdxpv＝0＝ζpv-ηgen,pvpgen,pvΔt-wpv (2)
wherein, CpvRepresenting capacity of wind farm energy storage, dxpvIs the change value of the state of charge, ζ, of the photovoltaic stationpvCalculating the solar energy, eta, collected by the photovoltaic station for the collection of irradiation datagen,pvFor the efficiency of the generation of the photovoltaic power plant itself, pgen,pvIs the output power of the photovoltaic station, wpvThe light abandon amount is the scheduling period, and delta t is the sampling time;
the battery energy storage model is as follows:
Cbdxb＝ηload,bpload,bΔt-ηgen,bpgen,bΔt (3)
wherein, CbRepresenting the capacity of the battery to store energy, dxbIs the state of charge change value, η, of the batteryload,bTo the charging efficiency of the battery, pgen,bCharging power of the battery, ηgen,bRepresents the discharge efficiency of the cell, pgen,bThe discharge power of the battery is shown, and delta t is sampling time;
hydrogen energy storage system model
Chdxh＝ζh+ηload,hpload,hΔt-ηgen,hpgen,hΔt (4)
Wherein, ChIndicating the capacity of the hydrogen storage tank, dxhIs the hydrogen-charged state change value, zeta, of the batteryhFor hydrogen supply and demand, etaload,hRepresents the hydrogen production efficiency of the cell, pload,hIs the power of the electrolytic cell etagen,hRepresents the power generation efficiency of the fuel cell, pgen,hIs the output power of the fuel cell, and Δ t is the sampling time;
the gas engine set model is as follows:
Cfdxf＝ζf-ηgen,fpgen,fΔt (5)
wherein, CfIndicating the capacity of the gas reservoir, dxfIs the change value of the charged state of the gas storage tank, zetafIs the input quantity of fuel gas, etagen,fFor gas power generation efficiency, pgen,fThe output power of the gas unit is shown, and delta t is sampling time;
2) constraint conditions that the model needs to satisfy
Fifth, climbing restriction: unit and load ramp rate constraint
dpload,i,min≤dpload,i≤dpload,i,max (6)
Wherein dpload,i,minAnd dpload,i,maxClimbing respectively for the loadLower and upper rate limits;
dpgen,i,min≤dpgen,i≤dpgen,i,max (7)
wherein dpgen,i,minAnd dpgen,i,maxRespectively a lower limit value and an upper limit value of the unit climbing rate;
output power constraint: system load and output constraints
0≤kpload,i,min≤kpload,i≤kpload,i,max (8)
Wherein p isload,i,minAnd pload,i,maxRespectively representing the upper limit and the lower limit of the load demand, wherein k is binary number and represents whether the load demand exists, 1 is present, and 0 is absent;
0≤Xpgen,i,min≤Xpgen,i≤Xpgen,i,max (9)
wherein p isgen,i,minAnd pgen,i,maxThe upper limit and the lower limit of the unit output are respectively, X is a binary number and represents whether the unit output exists, 1 is present, and 0 is absent;
energy storage restraint: state of charge and energy storage capacity constraints
Figure BDA0002664842430000081
Wherein x isminAnd xmaxUpper and lower limits of the state of charge, CiIndicating capacity of stored energy, C when stored energy is present>0, otherwise, when C is 0, no charge state constraint exists;
sixth, renewable energy abandonment constraint: energy input/consumption and spill energy constraints
Figure BDA0002664842430000082
Therein, ζiFor the output/consumption of external energy; w is aiTo spill energy, when ζi>0 represents an external input of energy; w is aiMore than or equal to 0 represents the existence of overflow in the process of wind and light abandonmentEnergy is output; zetai<0 represents the external consumption of energy;
the state space discrete equation of the electro-hydrogen coupling system is as follows:
Figure BDA0002664842430000083
wherein x iswIs the state of charge, ζ, of a wind farmwCalculating wind energy, eta, collected by a wind farm for collection of wind speed datagen,wFor the efficiency of the generation of the wind farm itself, pgen,wFor the output power of the wind farm, wwFor the air-abandon of the scheduling period, xpvIs the state of charge, ζ, of the photovoltaic stationpvCalculating the solar energy, eta, collected by the photovoltaic station for the collection of irradiation datagen,pvFor the efficiency of the generation of the photovoltaic power plant itself, pgen,pvIs the output power of the photovoltaic station, wpvTo schedule the amount of light lost in a cycle, CbRepresenting the capacity of the battery to store energy, xbIs the state of charge, η, of the batteryload,bTo the charging efficiency of the battery, pload,bCharging power of the battery, ηgen,bRepresents the discharge efficiency of the cell, pgen,bIs the discharge power of the battery, ChDenotes the capacity, x, of the hydrogen storage tankhZeta charged state of the batteryhFor hydrogen supply and demand, etaload,hRepresents the hydrogen production efficiency of the cell, pload,hIs the power of the electrolytic cell etagen,hRepresents the power generation efficiency of the fuel cell, pgen,hIs the output power of the fuel cell, CfIndicating the capacity of the gas storage pipe; zetafAs gas input, xfIs the charged state of the gas storage tank etagen,fFor gas power generation efficiency, pgen,fThe output power of the gas turbine is k, the current moment is k, k +1 is the next moment, delta t is sampling time, y is a controlled output variable, and C is an output matrix;
the state variables of the electro-hydrogen coupling system are as follows:
x(k)＝[xw(k),xpv(k),xb(k),xh(k),xf(k)]T (13)
wherein x iswIs the state of charge, x, of the wind farmpvIs the state of charge, x, of the photovoltaic stationbIs the state of charge, x, of the batteryhIs the hydrogen-charged state of the cell, xfThe state is the charge state of the gas storage tank, and k is the current moment;
the control variables of the electro-hydrogen coupling system are:
u(k)＝[pgen,w(k),pgen,pv(k),pgen,b(k),pload,b(k),pgen,h(k),pload,h(k),pgen,f(k),ww(k),wpv(k)]T (14)
wherein p isgen,wFor the output power of the wind farm, pgen,pvIs the output power of the photovoltaic station, pload,bCharging power for the battery, pgen,bIs the discharge power of the battery, pload,hFor cell power, pgen,hIs the output power of the fuel cell, wwFor the air-abandon of the scheduling period, wpvThe light abandon amount is the scheduling period, and k is the current time;
disturbance variables of the electro-hydrogen coupled system are:
d(k)＝[ξw(k),ξpv(k),ξh(k),ξf(k)]T (15)
therein, ζwCalculating wind energy collected by a wind farm, ζ, for collection of wind speed datapvCalculating the solar energy collected by the photovoltaic station for the collection of irradiation data, ζhZeta supply and demand for hydrogenfThe input quantity of the fuel gas is, and k is the current moment;
the output variables of the electro-hydrogen coupling system are:
y(k)＝[xb(k),xh(k)]T (16)
wherein x isbIs the state of charge, x, of the batteryhIs the hydrogen-charged state of the battery;
the system matrix, the control matrix and the output matrix in the electro-hydrogen coupling system are as follows:
Figure BDA0002664842430000091
Figure BDA0002664842430000092
Figure BDA0002664842430000101
Figure BDA0002664842430000102
wherein: a is a system matrix, B is a control matrix, CcIs an output matrix, ηgen,wFor the efficiency of the electricity generation of the wind farm itself, ηgen,pvFor the generating efficiency of the photovoltaic power plant itself, CbRepresenting capacity of battery energy storage, ηload,bIs the charging efficiency of the battery, etagen,bRepresents the discharge efficiency of the battery, ChIndicates the capacity of the hydrogen storage tank, etaload,hRepresents the hydrogen production efficiency of the electrolytic cell, etagen,hRepresents the power generation efficiency of the fuel cell, ηgen,fGenerating efficiency for gas;
3) solving power regulation and control of electro-hydrogen coupling system based on model predictive control
The power regulation and control function of the electro-hydrogen coupling system is as follows:
Figure BDA0002664842430000103
wherein: n is a radical ofpTo predict the time domain, NcFor controlling the time domain, Q, R are weight matrices, j is 1, 2, 3 … NpJ is the objective function, y is the output variable, yrefA state quantity reference track is adopted, k is the current moment, and delta u is a control increment;
4) the evaluation indexes of the electro-hydrogen coupling system are as follows:
the power of climbing slope can not meet the index:
Figure BDA0002664842430000104
Figure BDA0002664842430000105
wherein E isIRDp for the power not meeting the specification for climbinggen,i,For the power of the unit climbing dpload,i,For load climbing power, dPnet,tRepresenting the net load climbing rate at the moment t; rhosRepresenting the flexible margin insufficiency probability in the s scene; n is a radical ofTRepresenting the number of system scheduling intervals; beta is as,tRepresenting the number of times of the flexible margin insufficiency; n is a radical ofGRepresenting the number of generator sets; n is a radical ofLRepresents the number of loads;
output power not meeting the index
Figure BDA0002664842430000106
Wherein E isIOFor output power not meeting the specification, Pnet,tRepresenting the net load power, p, at time tgen,i,For the output power of the unit, pload,i,As load power, psRepresenting the flexible margin insufficiency probability in the s scene; n is a radical ofTRepresenting the number of system scheduling intervals; beta is as,tRepresenting the number of times of the flexible margin insufficiency; n is a radical ofGRepresenting the number of generator sets; n is a radical ofLRepresents the number of loads;
and the power supply energy does not meet the index:
Figure BDA0002664842430000111
wherein E isICFor providing electric energy which does not meet the specification, Pnet,tRepresenting the net load power, p, at time tgen,i,For the output power of the unit, pload,i,As load power, psRepresenting the flexible margin insufficiency probability in the s scene; n is a radical ofTRepresenting the number of system scheduling intervals; beta is as,tRepresenting the number of times of the flexible margin insufficiency; n is a radical ofGRepresenting the number of generator sets; n is a radical ofLThe number of loads is indicated.
Specific examples are as follows:
based on simulation parameters, the method for calculating the flexible margin of the hydrogen-electricity coupling system based on model predictive control is analyzed.
The installed capacity of the wind power station is 600MW, and the installed capacity of the photovoltaic power generation system is 600 MW. The capacity of the gas engine assembling machine is 100 MW. And in the aspect of a hydrogen energy system, the power generation system comprises a 300MW PEM fuel cell power generation system and a 300MW PEM electrolyzer system, and the initial state of charge value of the hydrogen energy storage is 40%. The energy storage device selects a storage battery with the total capacity of 100MWh for energy storage, the rated charge-discharge power of the storage battery is +/-100 MW, and the initial charge of the storage battery is 45%. FIG. 1 is a schematic diagram of wind, photovoltaic, electrical load and hydrogen load power settings; fig. 2 is a schematic diagram of power change of the renewable energy and hydrogen coupling system, and it can be seen from the diagram that the renewable energy and hydrogen coupling system supplements flexible margin requirements in terms of energy storage and output through a combined module of a fuel cell and an electrolysis cell, and the operation requirement of the renewable energy and hydrogen coupling system is to avoid frequent start-up and shut-down of a unit on the basis of sufficiently supplying load and absorbing renewable energy. Fig. 3 is a schematic diagram of power change of the hydrogen-electricity coupling system, and it can be known from the diagram that the ramp power required by the battery energy storage and further supplementary module of the gas turbine set in the hydrogen-electricity coupling system and the flexible margin requirement in the output of the gas turbine set further improve the flexible margin level of the system. Fig. 4 is a schematic diagram of analysis of flexible margin of a renewable energy system, and fig. 5 is a schematic diagram of analysis of flexible margin of a renewable energy and hydrogen coupling system, and it can be known from the diagrams that, in a 5-33 hour interval, night and no wind conditions occur, output of a fan and photovoltaic is insufficient, and climbing power in an energy module and output of a unit are insufficient, so that sufficient flexible resources cannot be provided. The maximum value of the climbing power shortage in the period is 36MW/min, the maximum value of the output power shortage is 185MW, and the maximum value of the supply electric energy shortage is 42.7 MWh. In the interval of 163-. The maximum value of the climbing power shortage in the period is 46.7MW/min, the maximum value of the output power shortage is 198.25MW, and the maximum value of the supply power shortage is 362.7 MWh. In the 42-46, 115, 155-165, 230, 300-310, 325-355 hour interval, the energy module may not be able to sufficiently consume the renewable energy. The electric quantity abandoned accounts for 7% of the total electric quantity generated by the renewable energy sources. The flexible margin indexes are EIR 15.14MW/min, EIO 129.71MW and EIC 167.79 MWh. FIG. 6 is a schematic diagram of an analysis of flexibility margins of an electro-hydrogen coupled system. The upward flexibility requirement of the energy module can be basically met, but the situation that the renewable energy power generation cannot be completely consumed still exists in the interval of 44, 114-. The electric quantity abandoned accounts for 1.5 percent of the total electric quantity generated by the renewable energy sources. The flexible margin indexes of the electro-hydrogen coupling system are EIR (equivalent interference rejection ratio) 0MW/min, EIO (equivalent interference rejection ratio) 9.5MW and EIC (equivalent interference rejection ratio) 34.2 MWh.
The detailed description of the present invention is not exhaustive, and it should be noted that, for those skilled in the art, various modifications and improvements can be made without departing from the principle of the present invention, and these should be construed as the protection scope of the present invention.

Claims (1)

1. A method for calculating the flexibility margin of an electric hydrogen coupling system based on model predictive control is characterized by comprising the following steps:
1) construction of state space model of electro-hydrogen coupling system
The wind power plant model is as follows:
Cwdxw＝0＝ζw-ηgen,wpgen,wΔt-ww (1)
wherein, CwRepresenting capacity of wind farm energy storage, dxwFor the state of charge change value, ζ, of a wind farmwCalculating wind energy collected by the wind farm for collection of wind speed data; etagen,wThe generating efficiency of the wind power plant; p is a radical ofgen,wThe output power of the wind power plant; w is awThe air volume is the air volume abandoned in the dispatching cycle, and delta t is the sampling time;
the photovoltaic station model is as follows:
Cpvdxpv＝0＝ζpv-ηgen,pvpgen,pvΔt-wpv (2)
wherein, CpvRepresenting capacity of wind farm energy storage, dxpvIs the change value of the state of charge, ζ, of the photovoltaic stationpvCalculating the solar energy, eta, collected by the photovoltaic station for the collection of irradiation datagen,pvFor the efficiency of the generation of the photovoltaic power plant itself, pgen,pvIs the output power of the photovoltaic station, wpvThe light abandon amount is the scheduling period, and delta t is the sampling time;
the battery energy storage model is as follows:
Cbdxb＝ηload,bpload,bΔt-ηgen,bpgen,bΔt (3)
wherein, CbRepresenting the capacity of the battery to store energy, dxbIs the state of charge change value, η, of the batteryload,bTo the charging efficiency of the battery, pgen,bCharging power of the battery, ηgen,bRepresents the discharge efficiency of the cell, pgen,bThe discharge power of the battery is shown, and delta t is sampling time;
hydrogen energy storage system model
Chdxh＝ζh+ηload,hpload,hΔt-ηgen,hpgen,hΔt (4)
Wherein, ChIndicating the capacity of the hydrogen storage tank, dxhIs the hydrogen-charged state change value, zeta, of the batteryhFor hydrogen supply and demand, etaload,hRepresents the hydrogen production efficiency of the cell, pload,hIs the power of the electrolytic cell etagen,hRepresents the power generation efficiency of the fuel cell, pgen,hIs the output power of the fuel cell, and Δ t is the sampling time;
the gas engine set model is as follows:
Cfdxf＝ζf-ηgen,fpgen,fΔt (5)
wherein, CfIndicating the capacity of the gas reservoir, dxfIs the change value of the charged state of the gas storage tank, zetafIs the input quantity of fuel gas, etagen,fFor gas power generation efficiency, pgen,fThe output power of the gas unit is shown, and delta t is sampling time;
2) constraint conditions that the model needs to satisfy
Firstly, climbing constraint: unit and load ramp rate constraint
dpload,i,min≤dpload,i≤dpload,i,max (6)
Wherein dpload,i,minAnd dpload,i,maxRespectively is a lower limit value and an upper limit value of the load climbing rate;
dpgen,i,min≤dpgen,i≤dpgen,i,max (7)
wherein dpgen,i,minAnd dpgen,i,maxRespectively a lower limit value and an upper limit value of the unit climbing rate;
output power constraint: system load and output constraints
0≤kpload,i,min≤kpload,i≤kpload,i,max (8)
Wherein p isload,i,minAnd pload,i,maxRespectively representing the upper limit and the lower limit of the load demand, wherein k is binary number and represents whether the load demand exists, 1 is present, and 0 is absent;
0≤Xpgen,i,min≤Xpgen,i≤Xpgen,i,max (9)
wherein p isgen,i,minAnd pgen,i,maxThe upper limit and the lower limit of the unit output are respectively, X is a binary number and represents whether the unit output exists, 1 is present, and 0 is absent;
③ energy storage restraint: state of charge and energy storage capacity constraints
Figure FDA0002664842420000021
Wherein x isminAnd xmaxUpper and lower limits of the state of charge, CiIndicating capacity of stored energy, C when stored energy is present>0, otherwise, when C is 0, no charge state constraint exists;
fourthly, renewable energy abandon constraint: energy input/consumption and spill energy constraints
Figure FDA0002664842420000022
Therein, ζiFor the output/consumption of external energy; w is aiTo spill energy, when ζi>0 represents an external input of energy; w is aiThe condition that the air and light are abandoned is more than or equal to 0, which means that overflow energy exists; zetai<0 represents the external consumption of energy;
the state space discrete equation of the electro-hydrogen coupling system is as follows:
Figure FDA0002664842420000023
wherein x iswIs the state of charge, ζ, of a wind farmwCalculating wind energy, eta, collected by a wind farm for collection of wind speed datagen,wFor the efficiency of the generation of the wind farm itself, pgen,wFor the output power of the wind farm, wwFor the air-abandon of the scheduling period, xpvIs the state of charge, ζ, of the photovoltaic stationpvCalculating the solar energy, eta, collected by the photovoltaic station for the collection of irradiation datagen,pvFor the efficiency of the generation of the photovoltaic power plant itself, pgen,pvIs the output power of the photovoltaic station, wpvTo schedule the amount of light lost in a cycle, CbRepresenting the capacity of the battery to store energy, xbIs the state of charge, η, of the batteryload,bTo the charging efficiency of the battery, pload,bCharging power of the battery, ηgen,bRepresents the discharge efficiency of the cell, pgen,bIs the discharge power of the battery, ChDenotes the capacity, x, of the hydrogen storage tankhZeta charged state of the batteryhFor hydrogen supply and demand, etaload,hRepresents the hydrogen production efficiency of the cell, pload,hIs the power of the electrolytic cell etagen,hRepresents the power generation efficiency of the fuel cell, pgen,hIs the output power of the fuel cell, CfIndicating the capacity of the gas storage pipe; zetafAs gas input, xfIs the charged state of the gas storage tank etagen,fFor gas power generation efficiency, pgen,fThe output power of the gas turbine is k, the current moment is k, k +1 is the next moment, delta t is sampling time, y is a controlled output variable, and C is an output matrix;
the state variables of the electro-hydrogen coupling system are as follows:
x(k)＝[xw(k),xpv(k),xb(k),xh(k),xf(k)]T (13)
wherein x iswIs the state of charge, x, of the wind farmpvIs the state of charge, x, of the photovoltaic stationbIs the state of charge, x, of the batteryhIs the hydrogen-charged state of the cell, xfThe state is the charge state of the gas storage tank, and k is the current moment;
the control variables of the electro-hydrogen coupling system are:
u(k)＝[pgen,w(k),pgen,pv(k),pgen,b(k),pload,b(k),pgen,h(k),pload,h(k),pgen,f(k),ww(k),wpv(k)]T (14)
wherein p isgen,wFor the output power of the wind farm, pgen,pvIs the output power of the photovoltaic station, pload,bCharging power for the battery, pgen,bIs the discharge power of the battery, pload,hFor cell power, pgen,hIs the output power of the fuel cell, wwFor the air-abandon of the scheduling period, wpvThe light abandon amount is the scheduling period, and k is the current time;
disturbance variables of the electro-hydrogen coupled system are:
d(k)＝[ξw(k),ξpv(k),ξh(k),ξf(k)]T (15)
therein, ζwCalculating wind energy collected by a wind farm, ζ, for collection of wind speed datapvCalculating the solar energy collected by the photovoltaic station for the collection of irradiation data, ζhZeta supply and demand for hydrogenfIs the gas input amount, and k is the current timeEngraving;
the output variables of the electro-hydrogen coupling system are:
y(k)＝[xb(k),xh(k)]T (16)
wherein x isbIs the state of charge, x, of the batteryhIs the hydrogen-charged state of the battery;
the system matrix, the control matrix and the output matrix in the electro-hydrogen coupling system are as follows:
Figure FDA0002664842420000041
Figure FDA0002664842420000042
Figure FDA0002664842420000043
Figure FDA0002664842420000044
wherein: a is a system matrix, B is a control matrix, CcIs an output matrix, ηgen,wFor the efficiency of the electricity generation of the wind farm itself, ηgen,pvFor the generating efficiency of the photovoltaic power plant itself, CbRepresenting capacity of battery energy storage, ηload,bIs the charging efficiency of the battery, etagen,bRepresents the discharge efficiency of the battery, ChIndicates the capacity of the hydrogen storage tank, etaload,hRepresents the hydrogen production efficiency of the electrolytic cell, etagen,hRepresents the power generation efficiency of the fuel cell, ηgen,fGenerating efficiency for gas;
3) solving power regulation and control of electro-hydrogen coupling system based on model predictive control
The power regulation and control function of the electro-hydrogen coupling system is as follows:
Figure FDA0002664842420000045
wherein: n is a radical ofpTo predict the time domain, NcFor controlling the time domain, Q, R are weight matrices, j is 1, 2, 3 … NpJ is the objective function, y is the output variable, yrefA state quantity reference track is adopted, k is the current moment, and delta u is a control increment;
4) the evaluation indexes of the electro-hydrogen coupling system are as follows:
the power of climbing slope can not meet the index:
Figure FDA0002664842420000051
Figure FDA0002664842420000052
wherein E isIRDp for the power not meeting the specification for climbinggen,i,For the power of the unit climbing dpload,i,For load climbing power, dPnet,tRepresenting the net load climbing rate at the moment t; rhosRepresenting the flexible margin insufficiency probability in the s scene; n is a radical ofTRepresenting the number of system scheduling intervals; beta is as,tRepresenting the number of times of the flexible margin insufficiency; n is a radical ofGRepresenting the number of generator sets; n is a radical ofLRepresents the number of loads;
output power not meeting the index
Figure FDA0002664842420000053
Wherein E isIOFor output power not meeting the specification, Pnet,tRepresenting the net load power, p, at time tgen,i,For the output power of the unit, pload,i,As load power, psRepresenting the flexible margin insufficiency probability in the s scene; n is a radical ofTRepresenting the number of system scheduling intervals; beta is as,tFlexible and rich representationThe times of insufficient degree; n is a radical ofGRepresenting the number of generator sets; n is a radical ofLRepresents the number of loads;
thirdly, the power supply energy does not meet the index:
Figure FDA0002664842420000054
wherein E isICFor providing electric energy which does not meet the specification, Pnet,tRepresenting the net load power, p, at time tgen,i,For the output power of the unit, pload,i,As load power, psRepresenting the flexible margin insufficiency probability in the s scene; n is a radical ofTRepresenting the number of system scheduling intervals; beta is as,tRepresenting the number of times of the flexible margin insufficiency; n is a radical ofGRepresenting the number of generator sets; n is a radical ofLThe number of loads is indicated.
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