CN113852110A - Photovoltaic energy storage capacity planning method based on refrigeration system - Google Patents

Photovoltaic energy storage capacity planning method based on refrigeration system Download PDF

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CN113852110A
CN113852110A CN202111114563.XA CN202111114563A CN113852110A CN 113852110 A CN113852110 A CN 113852110A CN 202111114563 A CN202111114563 A CN 202111114563A CN 113852110 A CN113852110 A CN 113852110A
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energy storage
equipment
power
refrigeration
storage equipment
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刘皓明
杨志豪
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Nanjing Mingjing Power Technology Co ltd
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Nanjing Mingjing Power Technology Co ltd
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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
    • 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
    • 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]
    • 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
    • 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 discloses a photovoltaic energy storage capacity planning method based on a refrigerating system, which comprises the following steps: step 1, building a mathematical model of related equipment based on photovoltaic cooperative electricity storage equipment, photovoltaic cooperative cold storage equipment or photovoltaic hybrid energy storage equipment configured by a refrigeration house; step 2, establishing a multi-target function based on the optimal economy and the optimal environmental protection, and establishing a mixed integer linear multi-target programming model under the condition of parallel contract constraints; step 3, performing normalization processing on the multi-target function based on constraint conditions, and then performing linear weighting; and 4, determining a weight coefficient by using a fitness deviation sorting method and configuring the capacity. The planning method establishes a mixed integer multi-objective planning model taking the optimal economy and the optimal carbon dioxide emission equivalent as objective functions, and performs capacity optimization configuration after processing the multi-objective functions, so that the total cost of the system can be reduced, the carbon dioxide emission equivalent is reduced, and the consumption of renewable energy is improved.

Description

Photovoltaic energy storage capacity planning method based on refrigeration system
Technical Field
The invention belongs to the technical field of comprehensive utilization of energy, and particularly relates to a photovoltaic energy storage capacity planning method based on a refrigerating system.
Background
The equipment in the refrigeration industry of China mainly comprises a household refrigerator, an air conditioner, a cold storage, a refrigerator car, an ice maker and the like, the power consumption of the refrigeration equipment is more than 15% of the total power consumption of the national society, and meanwhile, the gas emission generated by the use of a refrigerant causes serious environmental problems. The refrigeration house is used as refrigeration storage equipment, convenience is brought to continuous development, but the problems that the equipment is old and aged, energy-saving measures are not provided and the like exist in part, so that the refrigeration house becomes a large energy consumption household in the refrigeration field.
Disclosure of Invention
The invention aims to provide a photovoltaic energy storage capacity planning method based on a refrigeration system, and provides three configuration methods of photovoltaic cooperative electricity storage equipment, photovoltaic cooperative cold storage equipment and photovoltaic hybrid energy storage equipment aiming at the problems of energy conservation and emission reduction of a refrigeration house refrigeration system by the multi-target planning method.
The invention aims to solve the problems by the following technical scheme:
a photovoltaic energy storage capacity planning method based on a refrigerating system is characterized by comprising the following steps: the planning method comprises the following steps:
step 1, building a mathematical model of related equipment based on photovoltaic cooperative electricity storage equipment, photovoltaic cooperative cold storage equipment or photovoltaic hybrid energy storage equipment configured by a refrigeration house;
step 2, establishing a multi-target function based on the optimal economy and the optimal environmental protection, and establishing a mixed integer linear multi-target programming model under the condition of parallel contract constraints;
step 3, performing normalization processing on the multi-target function based on constraint conditions, and then performing linear weighting;
and 4, determining a weight coefficient by using a fitness deviation sorting method and configuring the capacity.
The device in the step 1 comprises a solar photovoltaic generator set based on a power grid, refrigeration equipment and energy storage equipment, wherein the energy storage equipment is electricity storage equipment and/or cold storage equipment.
The mathematical model of the solar photovoltaic generator set comprises the output power P of the solar photovoltaic generator setPVWorking temperature T of solar photovoltaic generator setPV：
PPV＝APVGSηPV[1+αPV(TPV-TSTC)] (1)
TPV＝Tout+Gs(TPV,NOTC-20) (2)
In the formulae (1) and (2), APVIs the photovoltaic array installation area of the solar photovoltaic generator set, m2；ηPVConverting the photovoltaic power generation efficiency; gSIs the intensity of solar illumination, kW/m2；αPVPower temperature coefficient,%/deg.C; t isPVThe working temperature of the solar photovoltaic generator set or the surface temperature of the photovoltaic cell panel is in the range of DEG C; t isSTCTaking the temperature as the standard test condition temperature, and taking the temperature at 25 ℃; t isoutAmbient temperature, deg.C; t isPV,NOTCThe rated working temperature is the rated working temperature of the solar photovoltaic generator set;
the mathematical model of the refrigeration equipment is the output cold power of the refrigeration equipment:
Pt EC，cool＝COPECPt EC，elec (3)
in the formula (3), Pt EC，coolThe output cold power of the refrigeration equipment at the moment t is kW; COPECThe refrigeration coefficient of the refrigeration equipment; pt EC，elecThe input electric power of the refrigeration equipment at the moment t is kW;
the energy storage equipment comprises a mathematical model of the electricity storage equipment and/or the cold storage equipment, and is represented by using the charging and discharging energy and charging and discharging energy efficiency:
Figure BDA0003274821490000024
in the formula (4), θ ∈ { EES, CES }, that is, the EES of the electric storage device or the CES of the cold storage device is represented;
Figure BDA0003274821490000025
energy stored by the energy storage device at time t, kWh;
Figure BDA00032748214900000210
for storing energyThe prepared self-loss coefficient; etaθcThe energy storage efficiency of the energy storage device; pt θcThe energy storage power of the energy storage equipment at the moment t is kW; etaθdThe discharging efficiency of the energy storage equipment is obtained; pt θdThe energy discharge power of the energy storage equipment at the moment t is kW; Δ t is a unit time step.
The target function constructed in the step 2 with the optimal economy is composed of the initial investment cost C of equipmentinAnd the equipment operation and maintenance cost ComAnd the electricity purchasing cost CgridAnd photovoltaic subsidy income BPVThe economic optimal objective function is as follows: minf1＝Cin+Com+Cgrid-BPV(ii) a The objective function of the environment-friendly optimal construction is equivalent emission of carbon dioxide
Figure BDA00032748214900000215
The formed carbon dioxide emission equivalent optimal target function is as follows:
Figure BDA00032748214900000216
initial investment cost C of the apparatusinComprises the following steps: cin＝CPV，in+CEC，in+Cθ，inIn the formula CPV,inThe initial investment cost of the solar photovoltaic generator set is ten thousand yuan; cEC,inTen thousand yuan for the initial investment cost of refrigeration equipment; cθ，inThe initial investment cost of the energy storage equipment is ten thousand yuan;
wherein, the initial investment cost C of the solar photovoltaic generator setPV,inComprises the following steps:
Figure BDA0003274821490000028
in the formula APVIs the photovoltaic array installation area of the solar photovoltaic generator set, m2；IPVIs the initial unit investment cost of the solar photovoltaic generator set, yuan/m2(ii) a r is the discount rate; n isPVThe service life of the solar photovoltaic generator set is year;
initial annual investment cost C of refrigeration plantsEC,inComprises the following steps:
Figure BDA0003274821490000031
in the formula QECCapacity, kW, is configured for the refrigeration equipment; i isECThe initial unit investment cost of the refrigeration equipment is yuan/kW; r is the discount rate; n isECThe service life of the refrigeration equipment is year;
initial annual investment costs of energy storage devices Cθ，inComprises the following steps:
Figure BDA0003274821490000032
in the formula QθConfiguring capacity, kW, for energy storage equipment; eθThe initial unit investment cost of the energy storage equipment is yuan/kW; r is the discount rate; n isθThe service life of the energy storage device is year.
The equipment operating maintenance cost ComComprises the following steps: com＝CPV，om+CEC，om+Cθ，omIn the formula CPV,omThe operation and maintenance cost of the solar photovoltaic generator set is ten thousand yuan; cEC,omThe operation and maintenance cost of the refrigeration equipment is ten thousand yuan; cθ，omThe operation and maintenance cost of the energy storage equipment is ten thousand yuan;
operation and maintenance cost C of solar photovoltaic generator setPV,omComprises the following steps:
Figure BDA0003274821490000033
wherein m is the number of typical days; rjSimilar days on the jth typical day;
Figure BDA0003274821490000034
actual output power, kW, of the solar photovoltaic generator set at the jth typical day and time t; lambda [ alpha ]PVThe unit operation cost of the solar photovoltaic generator set is Yuan/kWh; Δ t is a unit time step;
operating maintenance cost C of refrigeration equipmentEC,omComprises the following steps:
Figure BDA0003274821490000035
in the formula
Figure BDA0003274821490000036
Actual output power, kW, of the refrigeration equipment at the jth typical day time t; lambda [ alpha ]ECUnit operating cost for refrigeration equipment, yuan/kWh; Δ t is a unit time step;
operating maintenance cost C of energy storage equipmentθ，omComprises the following steps:
Figure BDA0003274821490000037
in the formula
Figure BDA0003274821490000038
The energy storage power of the energy storage equipment at the jth typical day t is kW;
Figure BDA0003274821490000039
the energy discharge power of the energy storage equipment at the jth typical day t is kW; lambda [ alpha ]θIs the unit operating cost of the energy storage device, yuan/kWh; Δ t is a unit time step.
The electricity purchasing cost CgridIn order to realize the purpose,
Figure BDA0003274821490000041
wherein m is the number of typical days; rjSimilar days on the jth typical day;
Figure BDA0003274821490000042
purchasing electric power, kW, for the jth typical day at the moment t;
Figure BDA0003274821490000043
purchasing electricity price at time t, yuan/kWh; Δ t is a unit time step;
photovoltaic subsidy income BPVComprises the following steps:
Figure BDA0003274821490000044
in the formula ISPVThe price is the local photovoltaic subsidy, yuan/kWh;
Figure BDA0003274821490000045
actual output power, kW, of the solar photovoltaic generator set at the jth typical day and time t; Δ t is a unit time step.
Calculating carbon dioxide emission equivalent
Figure BDA00032748214900000420
Figure BDA0003274821490000046
In the formula (16), phi is the electric energy standard coal conversion coefficient, kgce/kWh;
Figure BDA0003274821490000047
is a carbon dioxide emission factor of standard coal, kg-CO2(ii)/kgce; m is the typical number of days; rjSimilar days on the jth typical day;
Figure BDA0003274821490000048
purchasing electric power, kW, for the jth typical day at the moment t; Δ t is a unit time step.
The constraint conditions in the step 2 include a power balance equation constraint, an energy conversion device operation constraint, an energy storage device operation constraint and a temperature dynamic balance constraint, wherein the power balance equation constraint is as follows:
and (3) power balance equality constraint of the photovoltaic cooperative power storage equipment:
Figure BDA0003274821490000049
and (3) power balance equality constraint of the photovoltaic cooperative cold storage equipment:
Figure BDA00032748214900000410
the power balance equation of the photovoltaic hybrid energy storage device is constrained by:
Figure BDA00032748214900000411
formula (17), (1)8) In (1) and (19), the first step,
Figure BDA00032748214900000412
actual output power, kW, of the solar photovoltaic generator set at the jth typical day and time t;
Figure BDA00032748214900000413
purchasing electric power, kW, for the jth typical day at the moment t;
Figure BDA00032748214900000414
the input electric power, kW, of the refrigeration equipment at the jth typical day t;
Figure BDA00032748214900000415
actual output power, kW, of the refrigeration equipment at the jth typical day time t;
Figure BDA00032748214900000416
the charging power of the power storage equipment at the jth typical day t, kW;
Figure BDA00032748214900000417
the discharge power of the power storage equipment at the jth typical day t, kW;
Figure BDA00032748214900000418
the cold storage power of the cold storage equipment at the jth typical day t is kW;
Figure BDA00032748214900000419
the cooling power of the cooling storage equipment at the jth typical day t is kW;
Figure BDA0003274821490000051
electric load power at the jth typical day t, kW;
Figure BDA0003274821490000052
the cooling load power at the jth typical day t is kW;
establishing the operation constraints of the energy conversion equipment comprises the power inequality constraint and the area inequality constraint of the solar photovoltaic generator set and the operation inequality constraint of the refrigeration equipment,
the power inequality constraint and the area inequality constraint of the solar photovoltaic generator set are as follows:
Figure BDA0003274821490000053
and 0. ltoreq.APV≤AmaxIn the formula
Figure BDA0003274821490000054
The maximum output electric power of the solar photovoltaic generator set is kW;
Figure BDA0003274821490000055
actual output power, kW, of the solar photovoltaic generator set at the jth typical day and time t; a. thePVIs the photovoltaic array installation area of the solar photovoltaic generator set, m2；AmaxIs the maximum installation area of the roof, m2；
The inequality constraint of the operation of the refrigeration equipment is as follows:
Figure BDA0003274821490000056
in the formula
Figure BDA0003274821490000057
Actual output power, kW, of the refrigeration equipment at the jth typical day time t; qECCapacity, kW, is configured for the refrigeration equipment;
the energy storage equipment operation constraint is as follows:
Figure BDA0003274821490000058
in the formula (23), the compound represented by the formula,
Figure BDA0003274821490000059
the lower limit of the ratio of the stored energy to the capacity of the energy storage device;
Figure BDA00032748214900000510
for storage of energy-storage devicesAn energy to capacity ratio upper limit;
Figure BDA00032748214900000511
the energy storage state of the energy storage equipment at the jth typical day t moment;
Figure BDA00032748214900000512
and
Figure BDA00032748214900000513
the energy stored by the energy storage device is respectively the beginning time and the ending time of the jth typical day;
Figure BDA00032748214900000514
the energy stored by the energy storage device for the jth typical day at time t; qθIs as follows;
Figure BDA00032748214900000515
the energy storage power of the energy storage equipment at the jth typical day t is kW;
Figure BDA00032748214900000516
the maximum value of the energy charging power of the energy storage equipment is kW;
Figure BDA00032748214900000517
the discharging power of the energy storage equipment at the jth typical day t moment;
Figure BDA00032748214900000518
the maximum value of the energy discharge power of the energy storage equipment is kW;
temperature dynamic balance constraint is based on temperature dynamic balance equation c in refrigeration houseaVρadTin＝(Pt cool-Pt EC，cool) dt build-up, formula caIs the air specific heat capacity; v is the indoor volume of the refrigeration house; rhoaIs the air density; t isinThe indoor temperature of the refrigerator is set; pt coolIs the cooling load power at the moment t, kW; pt EC，coolThe output cold power of the refrigeration equipment at the moment t is kW;
equation c for dynamic balance of temperature in cold storageaVρadTin＝(Pt cool-Pt EC，cool) dt, a discrete form of the temperature dynamic equilibrium equation is obtained:
Figure BDA0003274821490000061
b is the heat transfer coefficient of the refrigeration house; htInstantaneous heat gain except temperature difference heat transfer in the refrigeration house at the moment t, including equipment heat dissipation, solar radiation heat gain, personnel heat dissipation and enclosure heat transfer; pt EC，coolThe output cold power of the refrigeration equipment at the moment t is kW; t ist outIs the outdoor temperature at time t; t ist inThe indoor temperature of the refrigeration house at the moment t;
Figure BDA0003274821490000065
is composed oft+ΔtThe indoor temperature of the refrigerator is maintained at all times.
After normalization processing is performed on the multi-target function based on the constraint conditions in the step 3, linear weighting is performed on the target function:
Figure BDA0003274821490000066
Figure BDA0003274821490000067
a1+a2＝1 (29)
in the formulae (27), (28), (29),
Figure BDA0003274821490000068
is a normalized target function; f. of(-)The objective function value before optimization; f. of(+)The optimized objective function value is obtained; minF is an optimal objective function after linear weighting;
Figure BDA0003274821490000069
is normalizedAn economic objective function;
Figure BDA00032748214900000610
the normalized carbon dioxide emission equivalent objective function is obtained; a is1And a2Are weight coefficients.
In the step 4, the weight coefficient a is determined by applying a fitness deviation sorting method1And a2Comprises the following steps:
step 41: if m objective functions exist in the system, the optimal solutions X corresponding to the m objective functions are respectively solvediWherein i is 1,2, …, m;
step 42: corresponding optimal solution X of other objective functionsjInto an objective function fiTo obtain the fitness value f of the objective function under the feasible solutioni(Xj) Where j ≠ 1,2, …, m, and j ≠ i;
step 43: solving an objective function fiSolution set of dispersion deltaiThe dispersion means an optimal value f corresponding to the objective functioni(Xi) Fitness value f to the objective functioni(Xj) The difference between them, expressed as: deltaij＝fi(Xj)-fi(Xi)＞0；
Step 44: for the objective function fiTaking the mean value of the dispersion, i.e. the mean dispersion uiSolving:
Figure BDA00032748214900000611
step 45: according to the mean deviation uiSolving the corresponding weight coefficient ai：
Figure BDA0003274821490000071
Compared with the prior art, the invention has the following advantages:
the planning method of the invention establishes a mixed integer multi-objective planning model taking the economical efficiency and the carbon dioxide emission equivalent as objective functions, and performs the capacity optimization configuration by performing the normalization processing and the linear weighting sum processing on the multi-objective functions and then determining the weight coefficient by using the fitness deviation sorting method, thereby reducing the total cost of the system, reducing the carbon dioxide emission equivalent and improving the consumption of renewable energy.
Drawings
FIG. 1 is a flow chart of the photovoltaic energy storage capacity configuration of the refrigeration system of the refrigeration house of the invention;
FIG. 2 is a schematic system structure diagram of the photovoltaic cooperative power storage apparatus of the present invention;
FIG. 3 is a schematic structural diagram of a system of the photovoltaic cooperative cold storage apparatus of the present invention;
FIG. 4 is a schematic diagram of a system structure of the photovoltaic hybrid power storage apparatus of the present invention;
FIG. 5 is a summer load distribution plot for an embodiment of the present invention;
FIG. 6 is an electrical power optimization balance diagram of the photovoltaic cooperative power storage apparatus of the present invention;
FIG. 7 is a cold power optimization balance diagram of the photovoltaic cooperative power storage apparatus of the present invention;
FIG. 8 is an electric power optimization balance diagram of the photovoltaic cooperative cold storage apparatus of the present invention;
FIG. 9 is a cold power optimization balance diagram of the photovoltaic cooperative cold storage apparatus of the present invention;
fig. 10 is an electric power optimization balance diagram of the photovoltaic hybrid power storage apparatus of the present invention;
fig. 11 is a cold power optimization balance diagram of the photovoltaic hybrid power storage apparatus of the present invention.
Detailed Description
The invention is further described with reference to the following figures and examples.
As shown in fig. 1: the invention provides a photovoltaic energy storage capacity planning method based on a refrigeration system, and provides three configuration methods of photovoltaic cooperative electricity storage equipment, photovoltaic cooperative cold storage equipment and photovoltaic hybrid energy storage equipment, wherein the adopted equipment comprises a solar photovoltaic generator set, refrigeration equipment, and energy storage equipment consisting of electricity storage equipment and/or cold storage equipment; secondly, establishing a multi-target function of economy and carbon dioxide emission equivalent and constraint conditions comprising power balance, photovoltaic output, the storage capacity and the charge-discharge power of the electricity storage equipment, the cold storage capacity and the charge-discharge power of the cold storage equipment by taking the initial investment cost of the equipment, the operation and maintenance cost of the equipment, the electricity purchasing cost and the photovoltaic subsidy income as economic evaluation indexes; then processing the multi-target function by utilizing a normalization method, a linear weighted sum method and a fitness deviation sorting method; and finally, comparing configuration results of the methods to determine whether the economy of the refrigeration house refrigeration system can be improved and the carbon dioxide emission equivalent of the refrigeration house refrigeration system can be reduced.
Step 1, three configuration methods of photovoltaic cooperative electricity storage equipment, photovoltaic cooperative cold storage equipment and photovoltaic hybrid energy storage equipment are provided, and the adopted equipment comprises a solar photovoltaic generator set based on a power grid, refrigeration equipment (an electric refrigerator is selected), electricity storage equipment and/or cold storage equipment.
Step 1-1, considering the influence of environmental temperature and the illumination intensity of the sun, and the output power P of a solar photovoltaic generator setPVAs shown in formula (1), the operating temperature of the solar photovoltaic generator set is determined by the outside temperature, the solar irradiance and the rated operating temperature thereof, as shown in formula (2):
PPV＝APVGSηPV[1+αPV(TPV-TSTC)] (1)
TPV＝Tout+Gs(TPV,NOTC-20) (2)
in the formulae (1) and (2), APVIs the photovoltaic array installation area of the solar photovoltaic generator set, m2；ηPVConverting the photovoltaic power generation efficiency; gSIs the intensity of solar illumination, kW/m2；αPVPower temperature coefficient,%/deg.C; t isPVThe working temperature of the solar photovoltaic generator set or the surface temperature of the photovoltaic cell panel is in the range of DEG C; t isSTCTaking the temperature as the standard test condition temperature, and taking the temperature at 25 ℃; t isoutAmbient temperature, deg.C; t isPV,NOTCThe rated working temperature is the rated working temperature of the solar photovoltaic generator set;
step 1-2, the refrigeration equipment converts electric energy into cold energy to supply cold load, and the output cold power of the refrigeration equipment is as follows (3):
Pt EC，cool＝COPECPt EC，elec (3)
in the formula (3), Pt EC，coolThe output cold power of the refrigeration equipment at the moment t is kW; COPECThe refrigeration coefficient of the refrigeration equipment; pt EC，elecThe input electric power of the refrigeration equipment at the moment t is kW;
step 1-3, the energy storage device comprises a mathematical model of the electricity storage device and/or the cold storage device, and the mathematical model is used for representing the energy charging and discharging and the energy charging and discharging efficiency by the energy charging and discharging and energy charging and discharging efficiency:
Figure BDA0003274821490000084
in the formula (4), theta belongs to { EES, CES }, namely electricity storage equipment (EES) or cold storage equipment (CES), the electricity storage equipment is a storage battery which is mature in technology, low in price and capable of storing a large amount of electric energy, and the cold storage equipment is ice storage which is not affected by a site and is suitable for regional cold supply;
Figure BDA0003274821490000085
energy stored by the energy storage device at time t, kWh;
Figure BDA00032748214900000810
the self-loss coefficient of the energy storage device; etaθcThe energy storage efficiency of the energy storage device; pt θcThe energy storage power of the energy storage equipment at the moment t is kW; etaθdThe discharging efficiency of the energy storage equipment is obtained; pt θdThe energy discharge power of the energy storage equipment at the moment t is kW; Δ t is a unit time step;
step 2, using the initial investment cost C of the equipmentinAnd the equipment operation and maintenance cost ComAnd the electricity purchasing cost CgridAnd photovoltaic subsidy income BPVEstablishing the equivalent of economical efficiency and carbon dioxide emission for economic evaluation indexes
Figure BDA0003274821490000088
The objective function of (1).
Figure BDA0003274821490000091
In the formula (5), minf1Representing an economic optimum objective function; minf2Representing an optimal objective function of carbon dioxide emission equivalent;
step 2-1, calculating initial investment cost C of equipmentin：
Cin＝CPV，in+CEC，in+Cθ，in (16)
In the formula (6), CPV,inThe initial investment cost of the solar photovoltaic generator set is ten thousand yuan; cEC,inTen thousand yuan for the initial investment cost of refrigeration equipment; cθ，inThe initial investment cost of the energy storage equipment is ten thousand yuan;
step 2-2, calculating initial annual investment cost C of the solar photovoltaic generator setPV,in：
Figure BDA0003274821490000092
In the formula (7), APVIs the photovoltaic array installation area of the solar photovoltaic generator set, m2；IPVIs the initial unit investment cost of the solar photovoltaic generator set, yuan/m2(ii) a r is the discount rate; n isPVThe service life of the solar photovoltaic generator set is year;
step 2-3, calculating initial annual investment cost C of refrigeration equipmentEC,in：
Figure BDA0003274821490000093
In the formula (8), QECCapacity, kW, is configured for the refrigeration equipment; i isECThe initial unit investment cost of the refrigeration equipment is yuan/kW; r is the discount rate; n isECThe service life of the refrigeration equipment is year;
step 2-4, calculating initial annual investment cost C of energy storage equipmentθ，in：
Figure BDA0003274821490000094
In the formula (9), QθConfiguring capacity, kW, for energy storage equipment; i isθThe initial unit investment cost of the energy storage equipment is yuan/kW; r is the discount rate; n isθThe service life of the energy storage equipment is year;
step 2-6, calculating the operation maintenance cost C of the equipmentom：
Com＝CPV，om+CEC，om+Cθ，om (10)
In the formula (10), CPV,omThe operation and maintenance cost of the solar photovoltaic generator set is ten thousand yuan; cEC,omThe operation and maintenance cost of the refrigeration equipment is ten thousand yuan; cθ，omThe operation and maintenance cost of the energy storage equipment is ten thousand yuan;
step 2-7, calculating the operation maintenance cost C of the solar photovoltaic generator setPV,om：
Figure BDA0003274821490000101
In the formula (11), m is the typical number of days; rjSimilar days on the jth typical day;
Figure BDA0003274821490000102
actual output power, kW, of the solar photovoltaic generator set at the jth typical day and time t; lambda [ alpha ]PVThe unit operation cost of the solar photovoltaic generator set is Yuan/kWh; Δ t is a unit time step;
step 2-8, calculating the operation maintenance cost C of the refrigeration equipmentEC,om：
Figure BDA0003274821490000103
In the formula (12), m is the typical number of days; rjIs the jth dictionaryDays of the same day of the model day;
Figure BDA0003274821490000104
actual output power, kW, of the refrigeration equipment at the jth typical day time t; lambda [ alpha ]ECUnit operating cost for refrigeration equipment, yuan/kWh; Δ t is a unit time step;
step 2-9, calculating the operation maintenance cost C of the energy storage equipmentθ，om：
Figure BDA0003274821490000105
In the formula (13), m is the typical number of days; rjSimilar days on the jth typical day;
Figure BDA0003274821490000106
the energy storage power of the energy storage equipment at the jth typical day t is kW;
Figure BDA0003274821490000107
the energy discharge power of the energy storage equipment at the jth typical day t is kW; lambda [ alpha ]θIs the unit operating cost of the energy storage device, yuan/kWh; Δ t is a unit time step;
step 2-11, calculating the electricity purchasing cost Cgrid：
Figure BDA0003274821490000108
In the formula (14), m is the typical number of days; rjSimilar days on the jth typical day;
Figure BDA0003274821490000109
purchasing electric power, kW, for the jth typical day at the moment t;
Figure BDA00032748214900001010
purchasing electricity price at time t, yuan/kWh; Δ t is a unit time step;
step 2-12, calculating photovoltaic subsidy receiptsYi BPV：
Figure BDA0003274821490000111
In the formula (15), ISPVThe price is the local photovoltaic subsidy, yuan/kWh; m is the typical number of days; rjSimilar days on the jth typical day;
Figure BDA0003274821490000112
actual output power, kW, of the solar photovoltaic generator set at the jth typical day and time t; Δ t is a unit time step;
step 2-13, calculating the equivalent of carbon dioxide emission
Figure BDA0003274821490000113
Figure BDA0003274821490000114
In the formula (16), phi is the electric energy standard coal conversion coefficient, kgce/kWh;
Figure BDA0003274821490000115
is a carbon dioxide emission factor of standard coal, kg-CO2(ii)/kgce; m is the typical number of days; rjSimilar days on the jth typical day;
Figure BDA0003274821490000116
purchasing electric power, kW, for the jth typical day at the moment t; Δ t is a unit time step.
Step 3, fig. 2, fig. 3 and fig. 4 are system structure diagrams of three configuration methods, and it can be seen from fig. 2 to 4 that the three systems are mainly supplied with power by a power grid and a solar photovoltaic generator set and are supplied with cold by refrigeration equipment, and the method one comprises power storage equipment, the method two comprises cold storage equipment, and the method three comprises power storage equipment and cold storage equipment, so that constraint conditions including power balance, photovoltaic output, the storage capacity of the power storage equipment and charging and discharging power thereof and/or the storage capacity of the cold storage equipment and charging and discharging power thereof are established according to the system structure diagrams.
Step 3-1, establishing power balance equality constraint
3-1-3, power balance equation constraint of the photovoltaic cooperative power storage equipment:
Figure BDA0003274821490000117
step 3-1-2, power balance equality constraint of the photovoltaic cooperative cold storage device:
Figure BDA0003274821490000118
3-1-3, power balance equation constraint of the photovoltaic hybrid energy storage equipment:
Figure BDA0003274821490000119
in the formulae (17), (18) and (19),
Figure BDA00032748214900001110
actual output power, kW, of the solar photovoltaic generator set at the jth typical day and time t;
Figure BDA0003274821490000121
purchasing electric power, kW, for the jth typical day at the moment t;
Figure BDA0003274821490000122
the input electric power, kW, of the refrigeration equipment at the jth typical day t;
Figure BDA0003274821490000123
actual output power, kW, of the refrigeration equipment at the jth typical day time t;
Figure BDA0003274821490000124
charging power of the electric storage device for jth typical day t，kW；
Figure BDA0003274821490000125
The discharge power of the power storage equipment at the jth typical day t, kW;
Figure BDA0003274821490000126
the cold storage power of the cold storage equipment at the jth typical day t is kW;
Figure BDA0003274821490000127
the cooling power of the cooling storage equipment at the jth typical day t is kW;
Figure BDA0003274821490000128
electric load power at the jth typical day t, kW;
Figure BDA0003274821490000129
the cooling load power at the jth typical day t is kW;
step 3-2, establishing operation constraint of energy conversion equipment
Step 3-2-1, power inequality constraint and area inequality constraint of the solar photovoltaic generator set:
Figure BDA00032748214900001210
0≤APV≤Amax (21)
in the formulae (20) and (21),
Figure BDA00032748214900001211
the maximum output electric power of the solar photovoltaic generator set is kW;
Figure BDA00032748214900001212
actual output power, kW, of the solar photovoltaic generator set at the jth typical day and time t; a. thePVIs the photovoltaic array installation area of the solar photovoltaic generator set, m2；AmaxIs the maximum installation area of the roof, m2；
Step 3-2-2, the operation of the refrigeration equipment is constrained by an inequality:
Figure BDA00032748214900001213
in the formula (22), the reaction mixture is,
Figure BDA00032748214900001214
actual output power, kW, of the refrigeration equipment at the jth typical day time t; qECCapacity, kW, is configured for the refrigeration equipment;
step 3-3, establishing the operation constraint of the energy storage equipment
Energy storage equipment needs satisfy the energy storage restraint simultaneously and charge and discharge energy power restraint, for guaranteeing energy storage equipment cyclic utilization, need restrict energy storage equipment initial and end time energy storage make it satisfy the energy conservation, but for energy storage equipment long-term utilization, need set up the upper and lower limit restraint of energy storage equipment energy storage and energy storage equipment charge and discharge energy power and need satisfy the upper and lower limit restraint:
Figure BDA00032748214900001215
in the formula (23), the compound represented by the formula,
Figure BDA00032748214900001216
the lower limit of the ratio of the stored energy to the capacity of the energy storage device;
Figure BDA00032748214900001217
the upper limit of the ratio of the stored energy of the energy storage equipment to the capacity is set;
Figure BDA0003274821490000131
the energy storage state of the energy storage equipment at the jth typical day t moment;
Figure BDA0003274821490000132
and
Figure BDA0003274821490000133
the energy stored by the energy storage device is respectively the beginning time and the ending time of the jth typical day;
Figure BDA0003274821490000134
the energy stored by the energy storage device for the jth typical day at time t; qθIs as follows;
Figure BDA0003274821490000135
the energy storage power of the energy storage equipment at the jth typical day t is kW;
Figure BDA0003274821490000136
the maximum value of the energy charging power of the energy storage equipment is kW;
Figure BDA0003274821490000137
the discharging power of the energy storage equipment at the jth typical day t moment;
Figure BDA0003274821490000138
the maximum value of the energy discharge power of the energy storage equipment is kW;
3-4, establishing temperature dynamic balance constraint
When the output cold quantity of the refrigeration equipment of the refrigeration house is equal to the absorbed heat quantity, the temperature of the refrigeration house can be kept unchanged; the refrigeration load in the cold storage needs to consider other instantaneous heat gains except temperature difference heat transfer, including equipment heat dissipation, solar radiation heat gain, personnel heat dissipation and enclosure heat transfer, and a dynamic equilibrium equation of the temperature in the cold storage is established according to energy conservation:
caVρadTin＝(Pt cool-Pt EC，cool)dt (24)
in the formula (24), caIs the air specific heat capacity; v is the indoor volume of the refrigeration house; rhoaIs the air density; t isinThe indoor temperature of the refrigerator is set; pt coolIs the cooling load power at the moment t, kW; pt EC，coolThe output cold power of the refrigeration equipment at the moment t is kW;
discretizing the formula (24) to obtain a discrete temperature dynamic equilibrium equation:
Figure BDA00032748214900001312
in the formula (25), B is the heat transfer coefficient of a refrigeration house; htInstantaneous heat gain except temperature difference heat transfer in the refrigeration house at the moment t, including equipment heat dissipation, solar radiation heat gain, personnel heat dissipation and enclosure heat transfer; pt EC，coolThe output cold power of the refrigeration equipment at the moment t is kW; t ist outIs the outdoor temperature at time t; t ist inThe indoor temperature of the refrigeration house at the moment t;
Figure BDA00032748214900001316
the indoor temperature of the refrigerator at the time t + delta t.
In actual operation, the temperature in the freezer room needs to be maintained within a certain range:
Figure BDA00032748214900001317
in the formula (26), the reaction mixture is,
Figure BDA00032748214900001318
Figure BDA00032748214900001319
respectively the lowest and highest indoor temperature set values in the cold storage.
Step 4, after normalization processing is carried out on each target function, linear weighting is carried out on the target functions:
Figure BDA00032748214900001320
Figure BDA00032748214900001321
a1+a2＝1 (29)
in the formulae (27), (28), (29),
Figure BDA0003274821490000141
is a normalized target function; f. of(-)The objective function value before optimization; f. of(+)The optimized objective function value is obtained; minF is an optimal objective function after linear weighting;
Figure BDA0003274821490000142
the economic objective function after normalization;
Figure BDA0003274821490000143
the normalized carbon dioxide emission equivalent objective function is obtained; a is1And a2Are weight coefficients.
Step 5, determining the weight coefficient a by using a fitness deviation sorting method1And a2And capacity configuration is carried out, and the operation steps are as follows:
step 5-1: if m objective functions exist in the system, the optimal solutions X corresponding to the m objective functions are respectively solvediWherein i is 1,2, …, m;
step 5-2: corresponding optimal solution X of other objective functionsjInto an objective function fiTo obtain the fitness value f of the objective function under the feasible solutioni(Xj) Where j ≠ 1,2, …, m, and j ≠ i;
step 5-3: solving an objective function fiSolution set of dispersion deltaiThe dispersion means an optimal value f corresponding to the objective functioni(Xi) Fitness value f to the objective functioni(Xj) The difference between them, expressed as: deltaij＝fi(Xj)-fi(Xi)＞0；
Step 5-4: for the objective function fiTaking the mean value of the dispersion, i.e. the mean dispersion uiSolving:
Figure BDA0003274821490000144
step 5-5: according to the mean deviation uiSolving the corresponding weight coefficient ai：
Figure BDA0003274821490000145
Examples
In the embodiment, summer load data of a refrigeration house (fig. 5 shows the summer load condition) is selected, the weight coefficients corresponding to the objective functions of the three configuration methods are determined by using a fitness deviation sorting method, and then multiple objectives are converted into a single objective for optimal configuration and compared with a system before configuration and a photovoltaic system without considering energy storage equipment. Tables 1 to 3 are selected basic data; the weight coefficient solving results are shown in table 4, and the arrangement results are shown in table 5. The configuration results prove that the three configuration methods are feasible when used in a refrigeration house, so that the economy can be improved, and the emission equivalent of carbon dioxide can be reduced.
TABLE 1 energy conversion device parameters
Figure BDA0003274821490000146
Figure BDA0003274821490000151
TABLE 2 energy storage device parameters
Figure BDA0003274821490000152
TABLE 3 time of use price
Figure BDA0003274821490000153
Table 4 weight coefficient selection
Figure BDA0003274821490000154
Table 5 comparison of configuration results
Figure BDA0003274821490000155
Fig. 6-11 are optimized power balance diagrams when the three configuration methods provided by the present invention are applied to a refrigeration storage, where fig. 6 is an electric power optimization result of a photovoltaic cooperative energy storage device, and fig. 10 is an electric power optimization result of a photovoltaic hybrid energy storage device; the system containing the electricity storage equipment can select to store energy for the electricity storage equipment at the time of low price of electricity or sufficient renewable energy, and release energy for the electricity storage equipment at the time of high price of electricity. Fig. 9 is a cold power optimization result of the photovoltaic cooperative cold storage device, and fig. 11 is a cold power optimization result of the photovoltaic hybrid energy storage transition season; after the cold storage equipment is configured, the system selects the time when the electricity price is low and the illumination is sufficient in the noon, namely the time when the renewable energy is rich, increases the energy power for the electric refrigerator, then converts the energy power into cold power, stores the cold power into the cold storage equipment, releases the cold power of the cold storage equipment at the time when the electricity price is high, shares the pressure of the electric refrigerator, and realizes the 'peak clipping and valley filling' of the cold energy.
The above embodiments are only for illustrating the technical idea of the present invention, and the protection scope of the present invention cannot be limited thereby, and any modification made on the basis of the technical scheme according to the technical idea proposed by the present invention falls within the protection scope of the present invention; the technology not related to the invention can be realized by the prior art.

Claims (10)

1. A photovoltaic energy storage capacity planning method based on a refrigerating system is characterized by comprising the following steps: the planning method comprises the following steps:
step 1, building a mathematical model of related equipment based on photovoltaic cooperative electricity storage equipment, photovoltaic cooperative cold storage equipment or photovoltaic hybrid energy storage equipment configured by a refrigeration house;
step 2, establishing a multi-target function based on the optimal economy and the optimal environmental protection, and establishing a mixed integer linear multi-target programming model under the condition of parallel contract constraints;
step 3, performing normalization processing on the multi-target function based on constraint conditions, and then performing linear weighting;
and 4, determining a weight coefficient by using a fitness deviation sorting method and configuring the capacity.
2. The refrigeration system based photovoltaic energy storage capacity planning method of claim 1, wherein: the device in the step 1 comprises a solar photovoltaic generator set based on a power grid, refrigeration equipment and energy storage equipment, wherein the energy storage equipment is electricity storage equipment and/or cold storage equipment.
3. The refrigeration system based photovoltaic energy storage capacity planning method according to claim 2, wherein: the mathematical model of the solar photovoltaic generator set comprises the output power P of the solar photovoltaic generator setPVWorking temperature T of solar photovoltaic generator setPV：
PPV＝APVGSηPV[1+αPV(TPV-TSTC)] (1)
TPV＝Tout+Gs(TPV,NOTC-20) (2)
In the formulae (1) and (2), APVIs the photovoltaic array installation area of the solar photovoltaic generator set, m2；ηPVConverting the photovoltaic power generation efficiency; gSIs the intensity of solar illumination, kW/m2；αPVPower temperature coefficient,%/deg.C; t isPVThe working temperature of the solar photovoltaic generator set or the surface temperature of the photovoltaic cell panel is in the range of DEG C; t isSTCTaking the temperature as the standard test condition temperature, and taking the temperature at 25 ℃; t isoutAmbient temperature, deg.C; t isPV,NOTCThe rated working temperature is the rated working temperature of the solar photovoltaic generator set;
the mathematical model of the refrigeration equipment is the output cold power of the refrigeration equipment:
Pt EC,cool＝COPECPt EC,elec (3)
in the formula (3), Pt EC,coolThe output cold power of the refrigeration equipment at the moment t is kW; COPECThe refrigeration coefficient of the refrigeration equipment; pt EC,elecThe input electric power of the refrigeration equipment at the moment t is kW;
the energy storage equipment comprises a mathematical model of the electricity storage equipment and/or the cold storage equipment, and is represented by using the charging and discharging energy and charging and discharging energy efficiency:
Figure FDA0003274821480000011
in the formula (4), θ ∈ { EES, CES }, that is, the EES of the electric storage device or the CES of the cold storage device is represented;
Figure FDA0003274821480000012
energy stored by the energy storage device at time t, kWh;
Figure FDA0003274821480000021
the self-loss coefficient of the energy storage device; etaθcThe energy storage efficiency of the energy storage device; pt θcThe energy storage power of the energy storage equipment at the moment t is kW; etaθdThe discharging efficiency of the energy storage equipment is obtained; pt θdThe energy discharge power of the energy storage equipment at the moment t is kW; Δ t is a unit time step.
4. The refrigeration system based photovoltaic energy storage capacity planning method of claim 1, wherein: the target function constructed in the step 2 with the optimal economy is composed of the initial investment cost C of equipmentinAnd the equipment operation and maintenance cost ComAnd the electricity purchasing cost CgridAnd photovoltaic subsidy income BPVThe economic optimal objective function is as follows: minf1＝Cin+Com+Cgrid-BPV(ii) a The target function of the environment-friendly optimal construction is the equivalent delta Q discharged by carbon dioxideCO2The formed carbon dioxide emission equivalent optimal target function is as follows:
Figure FDA0003274821480000025
5. the refrigeration system based photovoltaic energy storage capacity planning method according to claim 4, wherein: initial investment cost C of the apparatusinComprises the following steps: cin＝CPV,in+CEC,in+Cθ,inIn the formula CPV,inThe initial investment cost of the solar photovoltaic generator set is ten thousand yuan; cEC,inTen thousand yuan for the initial investment cost of refrigeration equipment; cθ,inThe initial investment cost of the energy storage equipment is ten thousand yuan;
wherein, the initial investment cost C of the solar photovoltaic generator setPV,inComprises the following steps:
Figure FDA0003274821480000022
in the formula APVIs the photovoltaic array installation area of the solar photovoltaic generator set, m2；IPVIs the initial unit investment cost of the solar photovoltaic generator set, yuan/m2(ii) a r is the discount rate; n isPVThe service life of the solar photovoltaic generator set is year;
initial annual investment cost C of refrigeration plantsEC,inComprises the following steps:
Figure FDA0003274821480000023
in the formula QECCapacity, kW, is configured for the refrigeration equipment; i isECThe initial unit investment cost of the refrigeration equipment is yuan/kW; r is the discount rate; n isECThe service life of the refrigeration equipment is year;
initial annual investment costs of energy storage devices Cθ,inComprises the following steps:
Figure FDA0003274821480000024
in the formula QθConfiguring capacity, kW, for energy storage equipment; i isθThe initial unit investment cost of the energy storage equipment is yuan/kW; r is the discount rate; n isθThe service life of the energy storage device is year.
6. The refrigeration system based photovoltaic energy storage capacity planning method according to claim 4, wherein: the equipment operating maintenance cost ComComprises the following steps: com＝CPV,om+CEC,om+Cθ,omIn the formula CPV,omThe operation and maintenance cost of the solar photovoltaic generator set is ten thousand yuan; cEC,omThe operation and maintenance cost of the refrigeration equipment is ten thousand yuan; cθ,omThe operation and maintenance cost of the energy storage equipment is ten thousand yuan;
operation and maintenance cost C of solar photovoltaic generator setPV,omComprises the following steps:
Figure FDA0003274821480000031
wherein m is the number of typical days; rjSimilar days on the jth typical day;
Figure FDA0003274821480000032
actual output power, kW, of the solar photovoltaic generator set at the jth typical day and time t; lambda [ alpha ]PVThe unit operation cost of the solar photovoltaic generator set is Yuan/kWh; Δ t is a unit time step;
operating maintenance cost C of refrigeration equipmentEC,omComprises the following steps:
Figure FDA0003274821480000033
in the formula
Figure FDA0003274821480000034
Actual output power, kW, of the refrigeration equipment at the jth typical day time t; lambda [ alpha ]ECUnit operating cost for refrigeration equipment, yuan/kWh; Δ t is a unit time step;
operating maintenance cost C of energy storage equipmentθ,omComprises the following steps:
Figure FDA0003274821480000035
in the formula
Figure FDA0003274821480000036
The energy storage power of the energy storage equipment at the jth typical day t is kW;
Figure FDA0003274821480000037
the energy discharge power of the energy storage equipment at the jth typical day t is kW; lambda [ alpha ]θIs the unit operating cost of the energy storage device, yuan/kWh; Δ t is a unit time step.
7. The refrigeration system based photovoltaic energy storage capacity planning method according to claim 4, wherein: the electricity purchasing cost CgridIn order to realize the purpose,
Figure FDA0003274821480000038
wherein m is the number of typical days; rjSimilar days on the jth typical day;
Figure FDA0003274821480000039
purchasing electric power, kW, for the jth typical day at the moment t;
Figure FDA00032748214800000310
purchasing electricity price at time t, yuan/kWh; Δ t is a unit time step;
photovoltaic subsidy income BPVComprises the following steps:
Figure FDA00032748214800000311
in the formula ISPVThe price is the local photovoltaic subsidy, yuan/kWh;
Figure FDA00032748214800000312
actual output power, kW, of the solar photovoltaic generator set at the jth typical day and time t; Δ t is a unit time step.
8. The refrigeration system based photovoltaic energy storage capacity planning method according to claim 4, wherein: the constraint conditions in the step 2 include a power balance equation constraint, an energy conversion device operation constraint, an energy storage device operation constraint and a temperature dynamic balance constraint, wherein the power balance equation constraint is as follows:
and (3) power balance equality constraint of the photovoltaic cooperative power storage equipment:
Figure FDA00032748214800000313
and (3) power balance equality constraint of the photovoltaic cooperative cold storage equipment:
Figure FDA0003274821480000041
the power balance equation of the photovoltaic hybrid energy storage device is constrained by:
Figure FDA0003274821480000042
in the formulae (17), (18) and (19),
Figure FDA0003274821480000043
actual output power, kW, of the solar photovoltaic generator set at the jth typical day and time t;
Figure FDA0003274821480000044
purchasing electric power, kW, for the jth typical day at the moment t;
Figure FDA0003274821480000045
the input electric power, kW, of the refrigeration equipment at the jth typical day t;
Figure FDA0003274821480000046
actual output power, kW, of the refrigeration equipment at the jth typical day time t;
Figure FDA0003274821480000047
the charging power of the power storage equipment at the jth typical day t, kW;
Figure FDA0003274821480000048
the discharge power of the power storage equipment at the jth typical day t, kW;
Figure FDA0003274821480000049
the cold storage power of the cold storage equipment at the jth typical day t is kW;
Figure FDA00032748214800000410
the cooling power of the cooling storage equipment at the jth typical day t is kW;
Figure FDA00032748214800000411
electric load power at the jth typical day t, kW;
Figure FDA00032748214800000412
the cooling load power at the jth typical day t is kW;
establishing the operation constraints of the energy conversion equipment comprises the power inequality constraint and the area inequality constraint of the solar photovoltaic generator set and the operation inequality constraint of the refrigeration equipment,
the power inequality constraint and the area inequality constraint of the solar photovoltaic generator set are as follows:
Figure FDA00032748214800000413
and 0. ltoreq.APV≤AmaxIn the formula
Figure FDA00032748214800000414
The maximum output electric power of the solar photovoltaic generator set is kW;
Figure FDA00032748214800000415
actual output power, kW, of the solar photovoltaic generator set at the jth typical day and time t; a. thePVIs the photovoltaic array installation area of the solar photovoltaic generator set, m2；AmaxIs the maximum installation area of the roof, m2；
The inequality constraint of the operation of the refrigeration equipment is as follows:
Figure FDA00032748214800000416
in the formula
Figure FDA00032748214800000417
Actual output power, kW, of the refrigeration equipment at the jth typical day time t; qECCapacity, kW, is configured for the refrigeration equipment;
the energy storage equipment operation constraint is as follows:
Figure FDA00032748214800000418
in the formula (23), the compound represented by the formula,
Figure FDA00032748214800000419
the lower limit of the ratio of the stored energy to the capacity of the energy storage device;
Figure FDA00032748214800000420
the upper limit of the ratio of the stored energy of the energy storage equipment to the capacity is set;
Figure FDA00032748214800000421
the energy storage state of the energy storage equipment at the jth typical day t moment;
Figure FDA00032748214800000422
and
Figure FDA00032748214800000423
the energy stored by the energy storage device is respectively the beginning time and the ending time of the jth typical day;
Figure FDA0003274821480000051
the energy stored by the energy storage device for the jth typical day at time t; qθIs as follows;
Figure FDA0003274821480000052
the energy storage power of the energy storage equipment at the jth typical day t is kW;
Figure FDA0003274821480000053
the maximum value of the energy charging power of the energy storage equipment is kW;
Figure FDA0003274821480000054
the discharging power of the energy storage equipment at the jth typical day t moment;
Figure FDA0003274821480000055
the maximum value of the energy discharge power of the energy storage equipment is kW;
temperature dynamic balance constraint is based on temperature dynamic balance equation c in refrigeration houseaVρadTin＝(Pt cool-Pt EC,cool) dt build-up, formula caIs the air specific heat capacity; v is the indoor volume of the refrigeration house; rhoaIs the air density; t isinThe indoor temperature of the refrigerator is set; pt coolIs the cooling load power at the moment t, kW; pt EC,coolThe output cold power of the refrigeration equipment at the moment t is kW;
equation c for dynamic balance of temperature in cold storageaVρadTin＝(Pt cool-Pt EC,cool) dt, a discrete form of the temperature dynamic equilibrium equation is obtained:
Figure FDA0003274821480000056
b is the heat transfer coefficient of the refrigeration house; htInstantaneous heat gain except temperature difference heat transfer in the refrigeration house at the moment t, including equipment heat dissipation, solar radiation heat gain, personnel heat dissipation and enclosure heat transfer; pt EC,coolThe output cold power of the refrigeration equipment at the moment t is kW; t ist outIs the outdoor temperature at time t; t ist inThe indoor temperature of the refrigeration house at the moment t;
Figure FDA0003274821480000057
the indoor temperature of the refrigerator at the time t + delta t.
9. The refrigeration system based photovoltaic energy storage capacity planning method of claim 8, wherein: after normalization processing is performed on the multi-target function based on the constraint conditions in the step 3, linear weighting is performed on the target function:
Figure FDA0003274821480000058
Figure FDA0003274821480000059
a1+a2＝1 (29)
in the formulae (27), (28), (29),
Figure FDA00032748214800000510
is a normalized target function; f. of(-)The objective function value before optimization; f. of(+)The optimized objective function value is obtained; minF is an optimal objective function after linear weighting;
Figure FDA00032748214800000511
the economic objective function after normalization;
Figure FDA00032748214800000512
the normalized carbon dioxide emission equivalent objective function is obtained; a is1And a2Are weight coefficients.
10. The refrigeration system based photovoltaic energy storage capacity planning method of claim 9, wherein: in the step 4, the weight coefficient a is determined by applying a fitness deviation sorting method1And a2Comprises the following steps:
step 41: if m objective functions exist in the system, the optimal solutions X corresponding to the m objective functions are respectively solvediWherein i is 1,2, …, m;
step 42: corresponding optimal solution X of other objective functionsjInto an objective function fiTo obtain the fitness value f of the objective function under the feasible solutioni(Xj) Where j ≠ 1,2, …, m, and j ≠ i;
step 43: solving an objective function fiSolution set of dispersion deltaiThe dispersion means an optimal value f corresponding to the objective functioni(Xi) Fitness value f to the objective functioni(Xj) The difference between them, expressed as: deltaij＝fi(Xj)-fi(Xi)＞0；
Step 44: for the objective function fiTaking the mean value of the dispersion, i.e. the mean dispersion uiSolving:
Figure FDA0003274821480000061
step 45: according to the mean deviation uiSolving the corresponding weight coefficient ai：
Figure FDA0003274821480000062
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CN113346556A (en) * 2021-06-08 2021-09-03 云南电网有限责任公司电力科学研究院 Photovoltaic energy storage capacity configuration method and system for refrigeration house

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