CN111327053A - Multi-source microgrid capacity optimal configuration method suitable for polar climate - Google Patents
Multi-source microgrid capacity optimal configuration method suitable for polar climate Download PDFInfo
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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
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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
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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
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
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- H—ELECTRICITY
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- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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- H—ELECTRICITY
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- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
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Abstract
The invention relates to a multi-source microgrid capacity optimal configuration method suitable for polar climate, which has the advantages that under the conditions of extreme daytime and extreme nighttime, multi-target optimal operation of a microgrid is realized according to reasonable configuration of the capacity of a wind, light, diesel and hydrogen storage microgrid. Under the extreme daytime and the extreme night, the wind-solar power generation output is different, the wind power, the photovoltaic, the diesel engine, the hydrogen fuel cell, the hydrogen storage tank, the lithium battery and the load operation characteristics under the extreme daytime and the extreme night are respectively analyzed, modeling is respectively carried out on the analysis results, and the capacity of each unit is determined. In the process of optimal configuration, the constraint conditions of an optimal configuration objective function and energy storage charge-discharge state of charge, unit output, power balance and the like are established by considering the minimum configuration cost, the minimum carbon emission and the stable energy storage, wherein the carbon emission is obtained by diesel engine emission, and the stable energy storage is realized by the minimum hydrogen consumption. The multi-objective optimization configuration model of the wind-solar-diesel-hydrogen storage multi-source micro-grid is solved, and the optimal configuration of the capacity of the wind-solar-diesel-hydrogen storage is realized.
Description
Technical Field
The invention relates to the technical field of energy optimal configuration of a new energy microgrid, in particular to a multi-source microgrid capacity optimal configuration method suitable for polar climate.
Background
With the increasing global power demand and the increasing environmental problems, it is crucial to construct a micro-grid system containing various micro-sources, and in order to fully utilize renewable energy, an energy storage system is added to the micro-grid, and a hydrogen fuel cell system is added to better consume the surplus electric energy, taking the uncertainty into consideration.
The reasonable operation of the micro-grid requires optimal management of the configuration of the source storage load so as to achieve optimal operation. And due to the special climate environment of the south pole, research needs to be respectively carried out according to different running characteristics of wind-solar complementary output under the extreme daytime and the extreme night, and a reasonable source storage and charge optimization configuration strategy is established for the micro-grid in combination with the load requirements, so that the purposes of minimizing configuration cost, minimizing carbon emission and maintaining energy storage balance are achieved.
Disclosure of Invention
The invention aims to provide a multisource microgrid capacity optimal configuration method suitable for polar climate for better tracking the output configuration problem of wind, light, diesel and hydrogen storage under the special condition of south Pole, and improve the safety, reliability and economy of microgrid operation.
The purpose of the invention can be realized by the following technical scheme:
a multi-source microgrid capacity optimal configuration method suitable for polar region climate comprises the following steps:
step1: inputting parameter data of each unit of the wind, light, diesel and hydrogen storage unit and polar load data;
step2: establishing a mathematical model of each unit of the multi-source micro-grid wind-solar-diesel-hydrogen storage under the extreme daytime and night conditions to determine the unit capacity;
and step3: establishing a multi-source microgrid capacity optimization configuration model based on the goals of minimum configuration cost, minimum carbon emission and energy storage balance maintenance;
and 4, step4: and solving the model by improving a particle swarm optimization algorithm and obtaining the optimal capacity configuration.
Further, the mathematical models of the wind, light, diesel and hydrogen storage units of the multi-source microgrid in the step2 comprise a wind power generation mathematical model, a photovoltaic power generation mathematical model under the non-polar night condition, a lithium battery charging and discharging mathematical model, a diesel engine, an electrolytic cell, a hydrogen storage tank and a fuel cell model, wherein the mathematical description formula of the lithium battery charging and discharging mathematical model comprises:
Ubat＝Ut-RI
in the non-midnight situation:
in the case of the extreme night:
in the formula of UbatFor the operating voltage of the lithium battery, UtIs the open-circuit voltage of lithium battery, R is the internal resistance, I is the charge-discharge current, RchgAnd RdisRespectively is a charge-discharge resistance, SocIs the state of charge value, S, of the lithium batteryoc0At the last moment, η is the charge-discharge efficiency, PbatIs unit lithium battery power, YbatH represents an hour for the lithium battery capacity.
Further, due to the polar maximum wind speed: 43.6 m/s, minimum wind speed: 0.6 m/s, high wind days: 188 days (more than 8 grades) and adopts a wind power generation system with low temperature resistance and strong wind speed, and the mathematical description formula of the wind power generation mathematical model is as follows:
in the formula, PrRated power, V (t) is the wind speed at time t, ViFor cutting into wind speed, VoTo cut off the wind speed, PwIs the fan power;
due to the lowest temperature: -46 ℃, maximum temperature: the solar cell adopts a photovoltaic power generation mathematical model of a photovoltaic power generation system suitable for polar environment under the condition of non-polar night, which adopts a high-efficiency double-sided self-snow melting heterojunction crystalline Silicon (SHJ) solar cell with anti-ultraviolet radiation performance suitable for strong radiation and ultralow temperature, and has the climatic characteristics of 9.6 ℃, about 220 days in sunny days and no illumination in 5-7 months, and the mathematical description formula is as follows:
in the formula, PpvIs the photovoltaic power generation power under the condition of non-polar night, LrefThe standard photovoltaic radiation intensity, delta T is the time variation of photovoltaic power generation, delta L is the photovoltaic radiation intensity variation, ImAnd UmThe current and the voltage of photovoltaic power generation under the non-polar night condition are respectively, and L is the photovoltaic radiation intensity.
Furthermore, the mathematical description formula of the diesel engine model is as follows:
FDSG＝nPDSG
where the fuel consumption of the diesel engine is related to the output power, FDSGFor the fuel consumption of a single diesel engine, n is a conversion systemNumber, PDSGIs unit diesel engine output power;
the mathematical description formula of the electrolytic cell model comprises:
in the non-midnight case, the correspondence is:
in the very night case, the correspondence is:
in the formula, YeAs cell capacity, PeIs the unit cell power, YDSGIs the diesel engine capacity, YwFor the generating capacity of the fan, YpvFor photovoltaic power generation capacity, PLoadIs the load power.
Further, the mathematical description formula of the hydrogen storage tank model comprises:
in the non-midnight case, the correspondence is:
in the very night case, the correspondence is:
in the formula, PNeIs the rated power of the electrolytic cell, CrUnit hydrogen tank power, α compression ratio of the hydrogen tank, QhIs the unit of hydrogen energy in the hydrogen storage tank, YhT is the operating time, for the capacity of the hydrogen storage tankEvery 1 hour, a year for 8760 hours in total, i.e. t is 1,2 … 8760;
the mathematical description formula of the fuel cell model comprises:
in the non-midnight case, the correspondence is:
in the very night case, the correspondence is:
in the formula, PfcIs the unit fuel cell power, YfcIs the total capacity of the fuel cell.
Further, the multi-source microgrid capacity optimization configuration model in the step3 comprises a configuration cost model, a carbon emission cost model and an equivalent hydrogen consumption model for maintaining energy storage balance, wherein the configuration cost model has a description formula as follows:
A(Xi)＝CDG+COR+CR+CT
CR＝d*(Cbat RYbat+Cfc RYfc)
CT＝nUTPDSG
in the formula, A (X)i) Representing a configuration cost model function, CDGFor each unit investment cost, CORFor the operation and maintenance costs of each unit, CRFor unit replacement cost, CTAs a cost of fuel, NDGAs unit type, YiFor each unit capacity, CiInvestment cost for the ith unit, Ci ORThe operation and maintenance cost of the ith unit, M the system life, d the number of replacement times, Cbat RAnd Cfc RReplacement costs, U, for lithium and fuel cells, respectivelyTIs the unit price of diesel oil.
Further, the carbon emission cost model is described by the formula:
C＝aQDSG 2+bQDSG+c
QDSG＝λFDSG
wherein C is carbon emission cost, QDSGThe carbon emission of the diesel engine is represented by a, b, and c, which are pollution treatment cost coefficients, and λ is a conversion coefficient.
Further, the equivalent hydrogen consumption model is described by the formula:
H＝Hfc+kHbat
k＝1-2μ[S-0.5(Sh+Sl)]/(Sh-Sl)
Hfc＝aPfc+b
minH＝min(Hfc+kHbat)＝min[a(Pref-Pbat)+kHbat]
in the formula, H is the total hydrogen consumption in the system, HfcFor fuel cell hydrogen consumption, HbatMu is weight, S is lithium battery state of charge value, S is lithium battery hydrogen consumptionhAnd SlRespectively the upper limit and the lower limit of the state of charge value, P, of the lithium batteryfcPower of fuel cell ηch_avgAnd ηdis_avgAverage charge-discharge efficiency, H, of lithium batteries, respectivelyfc_avgAnd Pfc_avgIs the average hydrogen consumption and average power of the fuel cell, k is a natural number, PrefThe total power output by the hybrid energy storage system is the difference between the load demand power and the wind, light and diesel generated power.
Further, the matching constraint conditions of the multi-source microgrid capacity optimization configuration model in the step3 include a power balance constraint condition, an energy storage charging constraint condition, a unit output constraint condition and a unit climbing constraint condition, wherein:
the power balance constraints include:
in extreme daylight conditions: pLoad＝Ppv+Pw+PDSG+Pbat+Ph-Pfc-Pe；
In the case of the extreme night: pLoad＝Pw+PDSG+Pbat+Ph-Pe-Pfc；
The energy storage charge constraint condition is described by the following formula:
Si,l≤Si≤Si,h
in the formula, SiIs the i-th lithium battery state of charge value, Si,hAnd Si,lRespectively an upper limit value and a lower limit value of the state of charge value of the ith lithium battery.
Further, the unit output constraint condition is described by the following formula:
Pk,min≤Pk,t≤Pk,max；
the unit climbing constraint condition is described by the following formula:
|Pk,t-Pk,t-1|≤λkΔt
in the formula, Pk,tIs the output of the k units at time t, Pk,minAnd Pk,maxRespectively, the allowable lower upper limit, P, of the output of the k unitsk,t-1Is the output of the unit at the time of t-1, lambdakAnd the conversion coefficient of the output climbing of the k unit is shown, and the delta t is the change time of the output climbing of the k unit.
Compared with the prior art, the invention has the following advantages:
(1) in the invention, on the aspect of solving the problem of the model, the particle swarm optimization algorithm has the characteristics of premature convergence and local optimization for the solution of the optimized configuration model, so that the genetic crossover is added into the particle swarm optimization algorithm for improvement.
(2) For special climatic characteristics of polar regions, wind power generation can output under the condition of extreme days, and wind power complementation only has the output of wind power generation under the condition of extreme nights, which is equivalent to the capacity optimization configuration problem of wind-diesel-hydrogen storage, so that the capacity optimization configuration strategy of the multi-source microgrid under the conditions of extreme days and extreme nights is respectively considered. And considering the comprehensive cost of investment cost, operation and maintenance cost, operation cost and replacement cost of each unit for the minimum configuration cost. For maintaining energy storage balance, an equivalent hydrogen consumption minimum strategy is adopted for realization, and is an instant optimization strategy which aims at minimizing system hydrogen energy consumption in a unit operation period. And to achieve the minimum carbon emissions, an objective function is established that minimizes the cost of carbon emissions. In the method for solving the model, a particle swarm optimization algorithm improved by genetic crossover is selected to solve the capacity optimization configuration model of the multi-source microgrid, and a reasonable capacity optimization configuration strategy is determined.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, shall fall within the scope of protection of the present invention.
Examples
As shown in fig. 1, a flowchart of the method of the present invention is shown, and the steps of the method of the present invention specifically include: firstly, modeling is carried out on wind-solar complementary power generation, a diesel generator, a hydrogen fuel cell, an electrolytic cell, a hydrogen storage tank and a lithium battery according to a wind-solar-diesel-hydrogen storage micro-grid, an optimal configuration model based on minimum configuration cost, minimum carbon emission and energy storage balance maintenance is established on the micro-grid, and then the optimal configuration model is solved through improving a particle swarm optimization algorithm, so that optimal configuration of wind-solar-diesel-hydrogen storage is realized.
1. A mathematical model of the wind-light diesel-hydrogen storage micro-grid is established, polar special climate environments are analyzed, the difference of wind-light complementary power generation under the conditions of extreme days and extreme nights is considered, and wind-light complementary output is calculated through the mathematical model.
Wind power generation mathematical model:
in the formula, PrRated power, V (t) is the wind speed at time t, ViFor cutting into wind speed, VoTo cut off the wind speed, PwIs the fan power;
photovoltaic power generation mathematical model under non-polar night condition:
in the formula, PpvIs the photovoltaic power generation power under the condition of non-polar night, LrefThe standard photovoltaic radiation intensity, delta T is the time variation of photovoltaic power generation, delta L is the photovoltaic radiation intensity variation, ImAnd UmThe current and the voltage of photovoltaic power generation under the non-polar night condition are respectively, and L is the photovoltaic radiation intensity.
The oil consumption of the diesel engine is related to the output power, and the oil consumption formula of a single diesel engine is as follows:
FDSG＝nPDSG
where the fuel consumption of the diesel engine is related to the output power, FDSGFor the fuel consumption of a single diesel engine, n is the conversion coefficient, PDSGIs unit diesel engine output power;
energy storage charge-discharge mathematical model:
Ubat＝Ut-RI
in the non-midnight situation:
in the case of the extreme night:
in the formula of UbatFor the operating voltage of the lithium battery, UtIs the open-circuit voltage of lithium battery, R is the internal resistance, I is the charge-discharge current, RchgAnd RdisRespectively is a charge-discharge resistance, SocIs the state of charge value, S, of the lithium batteryoc0At the last moment, η is the charge-discharge efficiency, PbatIs unit lithium battery power, YbatH represents the capacity of the lithium battery;
an electrolytic cell model, the mathematical description formula of which comprises:
in the non-midnight case, the correspondence is:
in the very night case, the correspondence is:
in the formula, YeAs cell capacity, PeIs the unit cell power, YDSGIs the diesel engine capacity, YwIs windCapacity of motor generation, YpvFor photovoltaic power generation capacity, PLoadIs the load power.
The hydrogen storage tank model comprises the following mathematical description formulas:
in the non-midnight case, the correspondence is:
in the very night case, the correspondence is:
in the formula, PNeIs the rated power of the electrolytic cell, CrUnit hydrogen tank power, α compression ratio of the hydrogen tank, QhIs the unit of hydrogen energy in the hydrogen storage tank, YhFor the capacity of the hydrogen storage tank, t is the operation time, the interval is 1 hour, and the total time is 8760 hours in one year, namely t is 1,2 … 8760;
a fuel cell model whose mathematical description formula includes:
in the non-midnight case, the correspondence is:
in the very night case, the correspondence is:
in the formula, PfcIs the unit fuel cell power, YfcIs the total capacity of the fuel cell.
2. Constructing multi-source micro-grid capacity optimization configuration model
2.1 establishing an objective function
The method comprises the steps of establishing a target function based on minimum configuration cost, minimum carbon emission and energy storage balance maintenance, constructing a multi-source microgrid capacity optimization configuration model by considering constraint conditions such as power balance and charge state, considering comprehensive cost of investment cost, operation and maintenance cost, operation cost and replacement cost, considering the emission of a diesel generator only by carbon emission, and realizing energy storage balance maintenance through an equivalent hydrogen consumption method.
The configuration cost is considered to configure the capacities of a photovoltaic system, a fan, a diesel engine, a hydrogen storage tank, a fuel cell and a lithium battery, and a configuration cost model is established:
A(Xi)＝CDG+COR+CR+CT
CR＝d*(Cbat RYbat+Cfc RYfc)
CT＝nUTPDSG
in the formula, A (X)i) Representing a configuration cost model function, CDGFor each unit investment cost, CORFor the operation and maintenance costs of each unit, CRFor unit replacement cost, CTAs a cost of fuel, NDGAs unit type, YiFor each unit capacity, CiInvestment cost for the ith unit, Ci ORThe operation and maintenance cost of the ith unit, M the system life, d the number of replacement times, Cbat RAnd Cfc RReplacement costs, U, for lithium and fuel cells, respectivelyTIs the unit price of diesel oil.
The carbon emission is mainly output through a diesel generator, the new wind, light and hydrogen energy does not generate carbon emission, the new wind, light and hydrogen energy belongs to clean energy, and a carbon emission cost model is established:
C＝aQDSG 2+bQDSG+c
QDSG＝λFDSG
wherein C is carbon emission cost, QDSGThe carbon emission of the diesel engine is represented by a, b, and c, which are pollution treatment cost coefficients, and λ is a conversion coefficient.
The method adopts an equivalent hydrogen consumption minimum strategy to achieve the aim of maintaining energy storage balance, and the strategy measures the electric energy consumed by the lithium battery as hydrogen energy, so that the relation between the optimal output power of the lithium battery and the Soc of the lithium battery is solved by taking the minimum hydrogen energy use of the whole system as the aim, and the instantaneous optimization strategy aims at minimizing the hydrogen energy consumption of the system in a unit control period. The equivalent hydrogen consumption model comprises the fuel cell hydrogen consumption which is approximately a linear relation of output power and a minimum expression of equivalent hydrogen consumption of the system:
H＝Hfc+kHbat
k＝1-2μ[S-0.5(Sh+Sl)]/(Sh-Sl)
Hfc＝aPfc+b
min H＝min(Hfc+kHbat)＝min[a(Pref-Pbat)+kHbat]
in the formula, H is the total hydrogen consumption in the system, HfcFor fuel cell hydrogen consumption, HbatMu is weight, S is lithium battery state of charge value, S is lithium battery hydrogen consumptionhAnd SlRespectively the upper limit and the lower limit of the state of charge value, P, of the lithium batteryfcPower of fuel cell ηch_avgAnd ηdis_avgAverage charge-discharge efficiency, H, of lithium batteries, respectivelyfc_avgAnd Pfc_avgIs the average hydrogen consumption and average power of the fuel cell, k is a natural number, PrefThe total power output by the hybrid energy storage system is the difference between the load demand power and the wind, light and diesel generated power.
Constructing an objective function: min Z ═ f (H (x)i),C(xi))。
2.2, taking constraints into account
The constraint conditions comprise a power balance constraint condition, an energy storage charge constraint condition, a unit output constraint condition and a unit climbing constraint condition, wherein:
power balance constraints comprising:
in extreme daylight conditions: pLoad＝Ppv+Pw+PDSG+Pbat+Ph-Pfc-Pe；
In the case of the extreme night: pLoad＝Pw+PDSG+Pbat+Ph-Pe-Pfc；
The energy storage charge constraint condition is described by the following formula:
Si,l≤Si≤Si,h
in the formula, SiIs the i-th lithium battery state of charge value, Si,hAnd Si,lRespectively an upper limit value and a lower limit value of the state of charge value of the ith lithium battery.
The unit output constraint condition is described by the following formula:
Pk,min≤Pk,t≤Pk,max；
the unit climbing constraint condition has the following description formula:
|Pk,t-Pk,t-1|≤λkΔt
in the formula, Pk,tIs the output of the k units at time t, Pk,minAnd Pk,maxRespectively, the allowable lower upper limit, P, of the output of the k unitsk,t-1Is the output of the unit at the time of t-1, lambdakAnd the conversion coefficient of the output climbing of the k unit is shown, and the delta t is the change time of the output climbing of the k unit.
3. Improved particle swarm optimization algorithm solving model
Step1, initializing the initial speed V and the initial position X of the particles;
step2, calculating a fitness objective function value Z of each particle;
step3, searching an individual optimal value and a global optimal value of the microgrid particles;
step4, comparing the fitness value of each particle with the fitness function value of the global optimal position of the particle, wherein if the current fitness objective function value of the particle is better, the position of the particle is the current global optimal position;
step5, selecting particles with specified number, and randomly hybridizing every two particles according to the hybridization probability;
step6, updating the speed and position of the child;
Vi(t+1)＝ωVi(t)+c1r1(Pi(t)-Xi(t))+c2r2(Pg(t)-Xi(t))
wherein P isi(t)，Pg(t) are individual values and global values, respectively.
And Step6, if the condition is met, outputting the capacity optimization configuration of the multi-source microgrid.
The multi-source microgrid capacity optimization configuration model is solved by improving the particle swarm optimization algorithm, and the genetic algorithm and the particle swarm optimization algorithm are combined to improve the particle swarm, so that the solution is faster and more accurate.
While the invention has been described with reference to specific embodiments, the invention is not limited thereto, and various equivalent modifications and substitutions can be easily made by those skilled in the art within the technical scope of the invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A multi-source microgrid capacity optimal configuration method suitable for polar region climate is characterized by comprising the following steps:
step1: inputting parameter data of each unit of the wind, light, diesel and hydrogen storage unit and polar load data;
step2: establishing a mathematical model of each unit of the multi-source micro-grid wind-solar-diesel-hydrogen storage under the extreme daytime and night conditions to determine the unit capacity;
and step3: establishing a multi-source microgrid capacity optimization configuration model based on the goals of minimum configuration cost, minimum carbon emission and energy storage balance maintenance;
and 4, step4: and solving the model by improving a particle swarm optimization algorithm and obtaining the optimal capacity configuration.
2. The method according to claim 1, wherein the mathematical models of the wind, light and diesel energy storage units of the multi-source micro-grid in the step2 comprise a wind power generation mathematical model, a photovoltaic power generation mathematical model in the non-polar night condition, a lithium battery charging and discharging mathematical model, a diesel engine model, an electrolytic cell model, a hydrogen storage tank model and a fuel cell model, wherein the mathematical description formula of the lithium battery charging and discharging mathematical model comprises:
Ubat＝Ut-RI
in the non-midnight situation:
in the case of the extreme night:
in the formula of UbatFor the operating voltage of the lithium battery, UtIs the open-circuit voltage of lithium battery, R is the internal resistance, I is the charge-discharge current, RchgAnd RdisRespectively is a charge-discharge resistance, SocIs the state of charge value, S, of the lithium batteryoc0At the last moment, η is charge and dischargeEfficiency, PbatIs unit lithium battery power, YbatH represents an hour for the lithium battery capacity.
3. The method according to claim 2, wherein the mathematical description formula of the wind power generation mathematical model is as follows:
in the formula, PrRated power, V (t) is the wind speed at time t, ViFor cutting into wind speed, VoTo cut off the wind speed, PwIs the fan power;
the mathematical description formula of the photovoltaic power generation mathematical model under the non-polar night condition is as follows:
in the formula, PpvIs the photovoltaic power generation power under the condition of non-polar night, LrefThe standard photovoltaic radiation intensity, delta T is the time variation of photovoltaic power generation, delta L is the photovoltaic radiation intensity variation, ImAnd UmThe current and the voltage of photovoltaic power generation under the non-polar night condition are respectively, and L is the photovoltaic radiation intensity.
4. The method according to claim 2, wherein the diesel engine model has a mathematical description formula as follows:
FDSG＝nPDSG
where the fuel consumption of the diesel engine is related to the output power, FDSGFor the fuel consumption of a single diesel engine, n is the conversion coefficient, PDSGIs unit diesel engine output power;
the mathematical description formula of the electrolytic cell model comprises:
in the non-midnight case, the correspondence is:
in the very night case, the correspondence is:
in the formula, YeAs cell capacity, PeIs the unit cell power, YDSGIs the diesel engine capacity, YwFor the generating capacity of the fan, YpvFor photovoltaic power generation capacity, PLoadIs the load power.
5. The method according to claim 2, wherein the mathematical description formula of the hydrogen storage tank model comprises:
in the non-midnight case, the correspondence is:
in the very night case, the correspondence is:
in the formula, PNeIs the rated power of the electrolytic cell, CrUnit hydrogen tank power, α compression ratio of the hydrogen tank, QhIs the unit of hydrogen energy in the hydrogen storage tank, YhFor the capacity of the hydrogen storage tank, t is the operation time, the interval is 1 hour, and the total time is 8760 hours in one year, namely t is 1,2 … 8760;
the mathematical description formula of the fuel cell model comprises:
in the non-midnight case, the correspondence is:
in the very night case, the correspondence is:
in the formula, PfcIs the unit fuel cell power, YfcIs the total capacity of the fuel cell.
6. The method for optimizing and configuring the capacity of the multi-source microgrid suitable for the polar region climate in claim 1 is characterized in that the multi-source microgrid capacity optimizing and configuring model in the step3 comprises a configuration cost model, a carbon emission cost model and an equivalent hydrogen consumption model for maintaining energy storage balance, wherein the configuration cost model describes the formula as follows:
A(Xi)＝CDG+COR+CR+CT
CR＝d*(Cbat RYbat+Cfc RYfc)
CT＝nUTPDSG
in the formula, A (X)i) Representing a configuration cost model function, CDGFor each unit investment cost, CORFor the operation and maintenance costs of each unit, CRFor unit replacement cost, CTAs a cost of fuel, NDGAs unit type, YiFor each unit capacity, CiInvestment cost for the ith unit, Ci ORThe operation and maintenance cost of the ith unit, M the system life, d the number of replacement times, Cbat RAnd Cfc RReplacement costs, U, for lithium and fuel cells, respectivelyTIs the unit price of diesel oil.
7. The method for optimizing and configuring the capacity of the multi-source microgrid suitable for the polar climate of claim 6 is characterized in that the carbon emission cost model is described by the formula:
C＝aQDSG 2+bQDSG+c
QDSG＝λFDSG
wherein C is carbon emission cost, QDSGThe carbon emission of the diesel engine is represented by a, b, and c, which are pollution treatment cost coefficients, and λ is a conversion coefficient.
8. The method according to claim 6, wherein the equivalent hydrogen consumption model is described by the formula:
H＝Hfc+kHbat
k＝1-2μ[S-0.5(Sh+Sl)]/(Sh-Sl)
Hfc＝aPfc+b
minH＝min(Hfc+kHbat)＝min[a(Pref-Pbat)+kHbat]
in the formula, H is the total hydrogen consumption in the system, HfcFor fuel cell hydrogen consumption, HbatMu is weight, S is lithium battery state of charge value, S is lithium battery hydrogen consumptionhAnd SlRespectively the upper limit and the lower limit of the state of charge value, P, of the lithium batteryfcPower of fuel cell ηch_avgAnd ηdis_avgAverage charge-discharge efficiency, H, of lithium batteries, respectivelyfc_avgAnd Pfc_avgIs the average hydrogen consumption and average power of the fuel cell, k is a natural number, PrefThe total power output by the hybrid energy storage system is the difference between the load demand power and the wind, light and diesel generated power.
9. The method for optimizing and configuring the capacity of the multi-source microgrid suitable for the polar region climate of claim 1, wherein the matching constraint conditions of the multi-source microgrid capacity optimizing and configuring model in the step3 comprise a power balance constraint condition, an energy storage charge constraint condition, a unit output constraint condition and a unit climbing constraint condition, wherein:
the power balance constraints include:
in extreme daylight conditions: pLoad＝Ppv+Pw+PDSG+Pbat+Ph-Pfc-Pe；
In the case of the extreme night: pLoad＝Pw+PDSG+Pbat+Ph-Pe-Pfc；
The energy storage charge constraint condition is described by the following formula:
Si,l≤Si≤Si,h
in the formula, SiIs the i-th lithium battery state of charge value, Si,hAnd Si,lRespectively an upper limit value and a lower limit value of the state of charge value of the ith lithium battery.
10. The method according to claim 9, wherein the unit output constraint is described by the following formula:
Pk,min≤Pk,t≤Pk,max；
the unit climbing constraint condition is described by the following formula:
|Pk,t-Pk,t-1|≤λkΔt
in the formula, Pk,tIs the output of the k units at time t, Pk,minAnd Pk,maxRespectively, the allowable lower upper limit, P, of the output of the k unitsk,t-1Is the output of the unit at the time of t-1, lambdakAnd the conversion coefficient of the output climbing of the k unit is shown, and the delta t is the change time of the output climbing of the k unit.
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