CN113869679A - Hybrid thermal power station system optimal configuration method and system suitable for renewable energy sources - Google Patents

Hybrid thermal power station system optimal configuration method and system suitable for renewable energy sources Download PDF

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CN113869679A
CN113869679A CN202111085727.0A CN202111085727A CN113869679A CN 113869679 A CN113869679 A CN 113869679A CN 202111085727 A CN202111085727 A CN 202111085727A CN 113869679 A CN113869679 A CN 113869679A
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陈霞
雷轩昂
林钰钧
杨丘帆
周建宇
文劲宇
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Huazhong University of Science and Technology
State Grid Hubei Electric Power Co Ltd
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State Grid Hubei Electric Power Co Ltd
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Abstract

The invention discloses a hybrid thermal power station system optimal configuration method and system suitable for renewable energy sources, and belongs to the field of electricity. The method comprises the following steps: the method comprises the following steps of taking the designed power capacity of electric energy storage, the designed energy capacity of energy, the designed heat storage power capacity, the designed energy capacity of energy, the operation and maintenance energy capacity of electric energy storage, the total capacity of electric heating equipment, the operation and maintenance energy capacity of heat storage, the total capacity of heat exchange equipment, the air abandoning amount, the electric abandoning load amount, the real-time power of electric load and the real-time power of heat load as decision variables, constructing a target function and constraint conditions containing the decision variables, wherein the target function is the comprehensive cost of operation and construction in a scheduling period, and the constraint conditions comprise: the method comprises the following steps of (1) energy flow relation constraint of a hybrid thermal power station system, equipment configuration constraint and operation constraint; and solving the decision variables by the objective function in a minimized way to obtain the optimal equipment configuration capacity and the optimal operation strategy of each component in each time period. Under the premise of meeting the load demand and matching the fluctuation characteristics of the load demand, the economic benefit is maximized, and the utilization rate of renewable energy resources is improved.

Description

Hybrid thermal power station system optimal configuration method and system suitable for renewable energy sources
Technical Field
The invention belongs to the field of electrical engineering, and particularly relates to a hybrid thermal power station system optimal configuration method and system suitable for renewable energy sources.
Background
The energy industry in China has the problems of excessive traditional productivity, prominent phenomena of wind and light abandonment, insufficient system harmony, low overall efficiency and the like. According to the study of scholars, the electricity and heat loads are obviously different in the peak period and the valley period, particularly in the heating season, the uninterrupted heat supply is needed for residents, businesses and living places, some public places and the like, the output mode of the thermoelectric fixed ratio of the existing thermal power plant is superposed by the fluctuation, intermittence and the like of the output of renewable energy sources, and the phenomena of wind abandon, light abandon and the like are frequently caused. Based on the above, research on an integration and coordination control method of a system containing high-proportion renewable energy sources needs to be carried out, the consumption capability and the system energy efficiency of the renewable energy sources are improved, and an energy system mainly containing the renewable energy sources is constructed. The electric energy and the heat energy have good natural complementary characteristics, the electric energy is easy to transmit but difficult to store, and the heat energy transmission pipeline is complex to construct but excellent in energy storage characteristics. The two energies are combined for application and complementary optimization, and considerable benefits are expected to be brought to the whole system. And the energy storage is added into the system, so that the storage and regulation capacity of the system can be effectively improved, the output characteristics of renewable energy sources such as wind and light are met, and the multi-load requirement is met.
The correct optimization scheme can enable the equipment configuration, the operation scheduling, the economic cost and other aspects of the multi-energy system to be more reasonable, efficient and practical, so that a set of comprehensive optimization scheme needs to be designed for the hybrid thermal power station system. The related optimal distribution is mainly performed in a large area and a large power grid at present, the optimal distribution is usually performed by a traditional thermal power generating unit, a cogeneration unit, an electric energy storage unit and the like, a hybrid thermal-power station-level optimal distribution method is less considered, electric energy in a hybrid thermal power station is generated by renewable energy sources, and the research on an optimal distribution strategy is also challenged.
The main idea of the optimization control strategy for improving the wind power consumption of the thermoelectric hybrid energy storage system is to establish a mathematical model of a hybrid system of a heat storage type electric boiler and a battery energy storage matched with consumption of the abandoned wind, construct various constraints of wind power output, the heat storage type electric boiler and the battery energy storage, and design an operation gear optimization selection strategy of the electric boiler by taking the maximum consumption of the abandoned wind of the whole system and the optimal system economy as objective functions. However, the method is mainly oriented to large-area thermoelectric systems, the capacity configuration problem of a hot spot combined system (a thermal power station) is not considered, the considered constraint conditions are simple, and the system energy flow relation constraint and each equipment capacity configuration constraint are not considered. The method does not consider other heat distribution network equipment models except for the electric heating device, neglects the influence of the operation of heat distribution side equipment on the system, and does not consider the influence of heat sale income on the economy.
Disclosure of Invention
Aiming at the defects and improvement requirements of the prior art, the invention provides a hybrid thermal power station system optimal configuration method and a hybrid thermal power station system optimal configuration system suitable for renewable energy sources, and aims to integrally schedule thermoelectric resources in a station, seek a hybrid thermal-power station thermal and electric output optimal distribution method which can maximize benefit and adapt to thermal-electric load fluctuation, and solve the problem of thermoelectric hybrid comprehensive optimization facing a thermoelectric combined application scene and considering multi-device and multifunctional coordination interaction.
In order to achieve the above object, according to a first aspect of the present invention, there is provided a method for optimally configuring a hybrid thermal power station system suitable for renewable energy sources, where the hybrid thermal power station system of renewable energy sources includes a wind turbine generator, an electrical energy storage device, an electrical heating device, a heat storage device, and a heat exchange device, and the system generates electrical energy by using the wind turbine generator, internally distributes the electrical energy to the electrical energy storage device and the electrical heating device, and provides electrical energy to electrical users; the electric heating equipment converts the absorbed electric energy into heat energy to be distributed to the heat storage equipment, the heat storage equipment conveys the stored heat energy to the heat exchange equipment, and the heat exchange equipment provides heat energy for a heat user externally, and the method comprises the following steps:
designing power capacity P with electric energy storagegbrDesigned energy capacity of electric energy storagegbrDesigned power capacity P of heat storagehsrDesigned energy capacity of heat storage EhsrEnergy capacity E of electric energy storage operation and maintenancegbmTotal capacity E of electric heating equipmentEHHeat storage operation and maintenance energy capacity EhsmTotal capacity E of heat exchangerHEXAir volume of waste EWDcutElectric charge discarding capacity EPLcutHeat-removing load EHLcutElectrical load real time power PPLAnd heat load real time power PHLConstructing an objective function and a constraint condition containing the decision variables for decision variables, wherein the objective function is the comprehensive cost of operation and construction in a scheduling period of the hybrid thermal power station system, and the constraint condition comprises the following steps: the method comprises the following steps of (1) energy flow relation constraint of a hybrid thermal power station system, equipment configuration constraint and operation constraint;
based on the objective function and all constraint conditions, the objective function minimizes and solves the decision variables to obtain the optimal equipment configuration capacity EgbrAnd EhsrThe obtained air abandon quantity E of each scheduling time intervalWDcutElectric charge discarding capacity EPLcutHeat-removing load EHLcutElectrical load real time power PPLAnd heat load real time power PHLAnd the optimal operation strategy is used as each component of the hybrid thermal-power station system.
Preferably, the objective function is as follows:
Figure BDA0003265633290000031
wherein, FHPSThe overall cost is represented as a function of,
Figure BDA0003265633290000032
when represents tThe penalty cost of the section wind abandonment is saved,
Figure BDA0003265633290000033
represents the power load shedding penalty cost for the period t,
Figure BDA0003265633290000034
represents the penalty cost of cutting off the thermal load in the period t,
Figure BDA0003265633290000035
representing the power and heat sales income during the period t, t representing the period of the current scheduling cycle, n representing the total period of the scheduling cycle, CEScRepresenting construction costs of the energy storage device, CESmRepresenting the operating and maintenance costs of the energy storage device, CEHcmRepresenting the construction and operation and maintenance costs of the electric heating apparatus, CHEXcmAnd the construction, operation and maintenance costs of the heat exchange equipment are shown.
Has the advantages that: the invention provides a comprehensive economic evaluation method considering the construction cost, the operation and maintenance cost, the opportunity cost and the operation income of the hybrid thermal power station.
Preferably, the first and second electrodes are formed of a metal,
Figure BDA0003265633290000036
wherein the content of the first and second substances,
Figure BDA0003265633290000037
a wind curtailment penalty coefficient is represented,
Figure BDA0003265633290000038
a penalty factor for electricity abandonment load is represented,
Figure BDA0003265633290000041
a penalty factor for abandoning the thermal load is represented,
Figure BDA0003265633290000042
represents the price of electricity sold, delta t represents the length of the dispatch list,
Figure BDA0003265633290000043
indicating the heat sales price and the superscript t indicating the period of the current scheduling cycle.
Preferably, the first and second electrodes are formed of a metal,
Figure BDA0003265633290000044
wherein f isdayDaily chemical coefficient representing cost of energy storage device, cgbpRepresenting the cost of the electrical energy storage per unit power, cgbeRepresenting the cost of electrical energy storage per unit capacity, chspRepresenting the cost of heat storage per unit power, chseRepresents the cost of heat storage per unit volume,
Figure BDA0003265633290000045
represents the operation and maintenance cost of the electric energy storage per unit capacity,
Figure BDA0003265633290000046
the heat storage operation and maintenance cost of unit capacity is shown, r is the construction investment reduction rate of the electric heating equipment, k is the service life of the electric heating equipment, rhoEElectric heating equipment cost per unit capacity is represented, delta represents operation and maintenance investment proportionality coefficient, rhoHEXAnd the comprehensive cost coefficient of the construction, operation and maintenance of the heat exchange equipment is represented.
Has the advantages that: the invention comprehensively considers the investment cost and the operation cost of the thermal power station, and amortizes the investment cost of each device in the station to every day, so that the economic evaluation of the investment, construction and operation of the thermal power station is more reasonable.
Preferably, the hybrid thermal power plant system energy flow relationship constraints include:
and (3) representing the constraint of the power supply and demand balance in the t period:
Figure BDA0003265633290000047
wherein, PWDRepresenting wind real-time power, PhscRepresenting electric heating real-time power, PgbRepresenting the real-time comprehensive discharge power of the electric energy storage;
wind power real-time power constraint, electric load real-time power constraint, heat energy supply and demand balance constraint and heat load real-time power constraint in a time period t are respectively expressed as follows:
Figure BDA0003265633290000051
wherein, PWDdRepresenting real-time full power of wind power, DWDcRepresenting the real-time wind abandon ratio, PPLdRepresenting the real-time full power of the electrical load, DPLcRepresenting the real-time load proportion, PhsdIndicating real-time output thermal power, P, of a hybrid thermo-electric plantHLdRepresenting the thermal load real time full power, DHLcRepresenting the real-time heat load shedding ratio.
Has the advantages that: the invention fully exerts the synergistic effect of electricity and heat, improves the wind power consumption capacity through the electric energy storage in the electric power system and the electric heating device in the thermal power system, improves the utilization rate of renewable energy sources, and can exert the flexibility of the electric power system and the thermal power system through the optimal scheduling strategy of energy storage and heat storage obtained by the thermal power station system.
Preferably, the device configuration constraints include:
the thermal storage device power capacity configuration constraints and energy capacity configuration constraints are as follows:
Figure BDA0003265633290000052
wherein, PhsrmaxRepresenting the maximum power capacity, T, of the heat storage devicehsdRepresents the maximum heat release time of the heat storage device;
the electrical energy storage device configuration constraints are as follows:
Figure BDA0003265633290000053
0≤numgb≤Numgb
Pgbr＝numgb·pgb
0≤Egbr≤Tgbd·Pgbr
wherein, numgbIndicates the number of battery cells, NumgbDenotes the maximum number of batteries that can be charged, K ═ log2Numgb]Represents NumgbThe conversion represents the maximum binary digit number after the binary system, k represents the k-th digit in the binary system, hgb,kA k-th digit value, p, representing a binary representation of the number of battery cellsgbIndicating the power capacity, T, of a cellgbdRepresents the maximum discharge time of the electric storage device;
the configuration constraints of the electric heating equipment and the heat exchange equipment are as follows:
Figure BDA0003265633290000061
Figure BDA0003265633290000062
wherein, PhscRepresenting electric heating real-time power, PhsdRepresenting the real-time output thermal power of the hybrid thermo-electric plant, and n representing the total period of the scheduling cycle.
Preferably, each plant operating constraint comprises:
the electric energy storage real-time comprehensive power constraint is as follows:
Figure BDA0003265633290000063
wherein, PgbcRepresenting the real-time charging power of the electrical energy store, PgbdRepresenting the real-time discharge power of the electric energy storage;
the energy capacity constraints of the electrical energy storage at the initial time and in the scheduling period are as follows:
0.1·Egbr≤Egbb≤0.9·Egbr
Figure BDA0003265633290000064
wherein E isgbbIndicating the energy capacity at the initial moment of electrical energy storage, EgbcRepresenting the real-time energy capacity of the electrical energy storage in the scheduling period;
the electric energy storage real-time discharge power and the real-time charging power are respectively constrained as follows:
Figure BDA0003265633290000065
Figure BDA0003265633290000066
wherein, betadIndicating the discharge state, the discharge value is 1, the others are 0, betacRepresents the charging state, the charging value is 1, and the others are 0;
the electrical energy storage real-time energy state constraints are as follows:
Figure BDA0003265633290000067
wherein eta isgbcIndicates the charging efficiency, ηgbdIndicating the discharge efficiency;
when t is equal to n, the heat storage real-time energy state supplement constraint is as follows:
Figure BDA0003265633290000071
preferably, each plant operating constraint comprises:
the heat storage real-time comprehensive power constraint is as follows:
Figure BDA0003265633290000072
wherein, PhsRepresenting the real-time integrated charging power, P, of the heat storage devicehscRepresenting heat-stored real-time charging power, PhsdRepresenting the heat storage real-time discharge power;
the energy capacity constraints of the heat storage at the initial moment and in the scheduling period are respectively as follows:
0.1·Ehsr≤Ehsb≤0.9·Ehsr
Figure BDA0003265633290000073
wherein E ishsbIndicating the energy capacity at the initial moment of heat storage, EhscRepresenting the real-time energy capacity of the heat storage in the scheduling period;
the heat storage real-time charging and discharging power is restricted as follows:
Figure BDA0003265633290000074
Figure BDA0003265633290000075
the heat storage real-time energy state constraints are as follows:
Figure BDA0003265633290000076
wherein eta ishscRepresenting heat storage efficiency, ηhsdIndicating the efficiency of heat release;
when t is equal to n, the heat storage real-time energy state supplement constraint is as follows:
Figure BDA0003265633290000077
the constraints on wind curtailment and tangential load are as follows:
Figure BDA0003265633290000078
Figure BDA0003265633290000079
Figure BDA0003265633290000081
wherein epsilonWDcRepresenting the maximum allowable wind curtailment ratio, εPLcRepresenting the maximum allowable power-cut load proportion, epsilonHLcRepresents the maximum allowable heat cut load ratio;
the air volume abandonment constraint in the period t is as follows:
Figure BDA0003265633290000082
the cut-off load amount in the t period is constrained as follows:
Figure BDA0003265633290000083
the amount of the shear heat load during the period t is constrained as follows:
Figure BDA0003265633290000084
the energy capacity constraint of the electric energy storage operation and maintenance in the period t is as follows:
Figure BDA0003265633290000085
the energy capacity of the heat storage operation and maintenance in the period t is constrained as follows:
Figure BDA0003265633290000086
wherein, PWDdRepresenting real-time full power of wind power, DWDcRepresenting the real-time wind abandon ratio, PPLdRepresenting the real-time full power of the electrical load, DPLcRepresenting the real-time load proportion, PhsdIndicating real-time output thermal power, P, of a hybrid thermo-electric plantHLdRepresenting the thermal load real time full power, DHLcRepresenting the real-time heat-cut load ratio, PgbcRepresenting the real-time charging power of the electrical energy store, PgbdIndicating electric energy storageDischarge power.
Has the advantages that: the invention fully considers the influence of the operation characteristics of each device in the hot spot station and simultaneously considers the cooperative operation of the combined heat and power system, the optimized scheduling scheme can simultaneously exert the flexibility of the power system and the heat supply system, the cooperative action of the multi-energy network is realized, and the economy, the safety and the stability of the operation of the heat and power station are ensured.
To achieve the above object, according to a second aspect of the present invention, there is provided a hybrid thermal power plant system optimal configuration system suitable for renewable energy, the system comprising: a computer-readable storage medium and a processor;
the computer-readable storage medium is used for storing executable instructions;
the processor is configured to read executable instructions stored in the computer-readable storage medium, and execute the hybrid thermal power station system optimal configuration method applicable to renewable energy sources according to the first aspect.
Generally, by the above technical solution conceived by the present invention, the following beneficial effects can be obtained:
the invention fully considers the requirements of the hybrid thermal power station on two aspects of operation and planning, obtains the optimal equipment configuration capacity by minimizing daily comprehensive cost from the thermal power station level, and provides a decision scheme for investment construction and operation of the thermal power station. The system operator can make a decision according to the solving result of the invention, and further can quickly and accurately carry out real-time optimized scheduling on the thermal power station system, thereby improving the economical efficiency of the operation of the thermal power station system. The method can theoretically obtain the optimal solution of the problem, and can be used for directly solving the problem by utilizing commercial software very conveniently.
Drawings
FIG. 1 is a block diagram of a hybrid thermoelectric power station based on renewable energy provided by the present invention;
FIG. 2 is a typical solar power, heat load, and wind power curve provided by the present invention;
FIG. 3(a) shows the load shedding situation of the power load under strategy 1 provided by the present invention;
FIG. 3(b) shows the load shedding situation of the power load under strategy 2 provided by the present invention;
FIG. 4 shows the thermal load shedding situation under strategy 1 and strategy 2 provided by the present invention;
FIG. 5 shows wind power output conditions under strategy 1 and strategy 2 provided by the present invention;
fig. 6(a) shows a wind power output situation under the strategy 3 provided by the present invention;
FIG. 6(b) is a power load scenario under strategy 3 provided by the present invention;
fig. 6(c) shows the thermal load situation under strategy 3 provided by the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. In addition, the technical features involved in the embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
The basic concept of the invention is as follows: and (3) considering comprehensive optimization targets including investment construction, operation cost and the like, constructing an operation constraint model, a standby model and the like of the system under different space-time scales by utilizing the overall operation characteristics of the thermal power station and the dynamic performance of various devices in the station, and analyzing the effectiveness of the method for improving economic operation and improving the utilization rate of renewable energy. The method specifically comprises the following steps:
step one, constructing a comprehensive optimization target including investment construction, operation cost and the like.
Daily composite cost F of thermal-power station systemHPSThe method comprises the following aspects: construction cost C of energy storage (including electric energy storage and heat storage) equipmentEScAnd the operation and maintenance cost C of the energy storage equipmentESmConstruction and operation and maintenance cost C of electric heating equipmentEHcmConstruction and operation and maintenance cost C of heat exchange equipmentHEXcmWind abandon penalty cost CWDcutPenalty cost for cutting off power load CPLcutPenalty cost for cutting thermal load CHLcutSelling electricityAnd sales heat revenue IPH. The first item, the second item, the third item and the fourth item are mainly related to the manufacturing cost and the long-term operation condition, the fifth item, the sixth item, the seventh item and the eighth item are mainly related to the dispatching operation condition, and the overall aim is to reduce the comprehensive cost of the system. From this, the formula is derived as follows:
Figure BDA0003265633290000101
in the formula, t represents the period of the current scheduling cycle, and n is the total period of the scheduling cycle.
CEscThe expression of (a) is as follows:
CESc＝Cgbc+Chsc
＝fday·[(cgbp·Pgbr+cgbe·Egbr)+(chsp·Phsr+chse·Ehsr)]
in the formula (f)dayDaily chemical coefficient of cost of energy storage device, cgbpCost of electrical energy storage per unit power, PgbrDesigning the power capacity for electrical energy storage, cgbeCost of electrical energy storage per unit capacity, EgbrDesigning the energy capacity for electrical energy storage, chspCost of heat storage per unit power, PhsrDesign of power capacity for heat storage, chseCost of heat storage per unit volume, EhsrEnergy capacity is designed for heat storage. f. ofdayThe calculation formula is as follows:
Figure BDA0003265633290000102
in the formula, i is the construction investment discount rate of the energy storage equipment, and sy is the service life of the energy storage equipment.
CESmThe expression of (a) is as follows:
Figure BDA0003265633290000103
in the formula, cgbmCost of operation and maintenance of the electrical energy storage per unit capacity, EgbmStoring operation and maintenance energy capacity for electricity, chsmCost of heat storage, operation and maintenance per unit volume, EhsmThe capacity is the energy capacity of heat storage operation and maintenance. EgbmAnd EhsmThe specific calculation method of (2) is mentioned in the constraint section.
CEHcmThe expression of (a) is as follows:
Figure BDA0003265633290000111
in the formula, r is the construction investment reduction rate of the electric heating equipment, n is the service life of the electric heating equipment, rhoECost of electric heating equipment per unit capacity, EEHDelta is the operation and maintenance investment proportionality coefficient for the total capacity of the electric heating equipment.
CHEXcmThe expression of (a) is as follows:
CHEXcm＝ρHEXEHEX
in the formula, ρHEXComprehensive cost coefficient for construction and operation and maintenance of heat exchange equipment, EHEXIs the total capacity of the heat exchange equipment.
t period CWDcutThe expression of (a) is as follows:
Figure BDA0003265633290000112
in the formula, kWDcutPenalty factor for wind abandonment, EWDcutTo reject the air volume, the calculation method is mentioned in the constraint part.
t period CPLcutThe expression of (a) is as follows:
Figure BDA0003265633290000113
in the formula, kPLcutPenalty factor for electricity-abandoning load, EPLcutTo discard the electric load amount, the calculation method is mentioned in the constraint part.
time period tCHLcutThe expression of (a) is as follows:
Figure BDA0003265633290000114
in the formula, kHLcutFor abandoning the penalty factor for heat load, EHLcutTo reject the thermal load, the calculation method is mentioned in the constraint part.
IPHThe expression of (a) is as follows:
Figure BDA0003265633290000121
in the formula, cgbsFor selling electricity, PPLFor the real-time power of the electrical load, Δ t is the scheduling single-segment duration, chssFor sale of heat, PHLReal-time power for thermal load.
And step two, constructing a hybrid thermal power station system energy flow relation containing the decision variables by utilizing the overall operating characteristics of the thermal power station and considering energy supply balance constraints.
the electric energy supply and demand balance constraint in the t period is as follows:
Figure BDA0003265633290000122
in the formula, PWDFor wind power real-time power, PhscFor electric heating of real-time power, PgbFor storing energy in electric energy, integrating discharge power P in real timePLReal-time power for the electrical load.
the wind power real-time power constraint, the electric load real-time power constraint, the heat energy supply and demand balance constraint and the heat load real-time power constraint in the time period t are respectively as follows:
Figure BDA0003265633290000123
wherein, PWDdFor wind power real-time full power, DWDcFor the real-time air abandonment ratio, PPLdFor electrical loads, real-time full power, DPLcThe proportion of the real-time power-off load is.
Figure BDA0003265633290000124
Wherein, PhsdReal-time output of thermal power, P, for hybrid thermo-electric stationsHLFor thermal load real-time power, PHLdFor thermal loading, real-time full power, DHLcThe heat load proportion is cut off in real time.
And step three, modeling each device by utilizing the operating characteristics and the dynamic performance of various devices in the station, and constructing configuration constraints and operating constraints of each device containing the decision variables.
(1) Configuration constraints of components
The thermal storage device power capacity configuration constraints and energy capacity configuration constraints are as follows:
Figure BDA0003265633290000125
in the formula, PhsrDesigning the power capacity, P, for the heat storage devicehsrmaxFor maximum power capacity of the heat storage unit, EhsrDesigning the power capacity, T, for the heat storage devicehsdThe maximum heat release time of the heat storage device.
The electrical energy storage device configuration constraints are as follows:
Figure BDA0003265633290000131
0≤numgb≤Numgb
Pgbr＝numgb·pgb
0≤Egbr≤Tgbd·Pgbr
in the formula, numgbIs the number of battery cells, NumgbFor the maximum number of batteries that can be charged, K ═ log2Nymgb]Is NumgbAfter being converted into binary systemMaximum number of binary digits, k being the kth bit in binary, hgb,kThe k-th digit, P, expressed as a cell number binarygbrDesigning the power capacity, p, for the storage meansgbIs the power capacity of one cell, EgbrDesigning the energy capacity, T, for the electricity storage devicegbdThe maximum discharge time of the electric storage device.
The configuration constraints of the wind power heating device and the heat exchange device are as follows:
Figure BDA0003265633290000132
Figure BDA0003265633290000133
(2) operational constraints of the components
The electric energy storage real-time comprehensive power constraint is as follows:
Figure BDA0003265633290000134
in the formula, PgbcReal-time charging power for electrical energy storage, PgbdReal-time discharge power for electrical energy storage.
The energy capacity constraints of the electrical energy storage at the initial time and in the scheduling period are as follows:
0.1·Egbr≤Egbb≤0.9·Egbr
Figure BDA0003265633290000135
in the formula, EgbbFor storing the energy capacity of the electricity at the initial moment, EgbcEnergy capacity for storing electricity in real time during a dispatch period.
The electric energy storage real-time discharge power and the real-time charging power are respectively constrained as follows:
Figure BDA0003265633290000141
Figure BDA0003265633290000142
in the formula, betadIn the discharge state, the discharge time is 1, and the others are 0, betacIn the charged state, the charge value is 1, and the others are 0.
The electrical energy storage real-time energy state constraints are as follows:
Figure BDA0003265633290000143
in the formula etagbcFor charging efficiency, ηgbdThe discharge efficiency is obtained. The purpose of this constraint is to ensure that the energy state of the electrical energy store returns to the initial state after a scheduling period.
Analogy to electrical energy storage, the constraints of thermal storage can be listed as follows:
the heat storage real-time comprehensive power constraint is as follows:
Figure BDA0003265633290000144
in the formula, PhscReal-time charging power for heat storage, PhsdFor storing heat and discharging power in real time.
The energy capacity constraints of the heat storage at the initial moment and in the scheduling period are respectively as follows:
0.1·Ehsr≤Ehsb≤0.9·Ehsr
Figure BDA0003265633290000145
in the formula, EhsbEnergy capacity at the initial moment of heat storage, EhscThe real-time energy capacity in the scheduling period is stored for heat.
The heat storage real-time storage and heat release power is constrained as follows:
Figure BDA0003265633290000146
Figure BDA0003265633290000147
the heat storage real-time energy state constraints are as follows:
Figure BDA0003265633290000151
in the formula etahscFor heat storage efficiency, ηhsdThe exothermic efficiency is shown. The purpose of this restriction is to ensure that the stored energy state returns to the initial state after a scheduling period.
Constraints on wind curtailment and tangential load:
Figure BDA0003265633290000152
Figure BDA0003265633290000153
Figure BDA0003265633290000154
in the formula, epsilonWDcTo the maximum allowable air-abandon proportion, epsilonPLcTo the maximum allowable proportion of the load cut-off, ∈HLcThe maximum allowable heat load cut ratio.
And (5) limiting the air volume abandon in the period t:
Figure BDA0003265633290000155
and (3) limiting the cut-off load amount in the t period:
Figure BDA0003265633290000156
and (3) cutting heat load quantity constraint in a period t:
Figure BDA0003265633290000157
and (3) restraining the energy capacity of the electric energy storage operation and maintenance in the t period:
Figure BDA0003265633290000158
and (3) energy capacity constraint of heat storage operation and maintenance in a period t:
Figure BDA0003265633290000159
and fourthly, based on the objective function and the constraint conditions, carrying out linearization processing on part of constraints and solving the part of constraints to obtain an optimized operation scheme of each component of the thermal power station.
In the above formula, a nonlinear term obtained by multiplying two variables of the designed power capacity of the electric energy storage and the charge-discharge state appears, and the linearization needs to be carried out by adopting a proper method. Large M-method linearization is considered here. Before linearization, in order to avoid the situation that the charging state and the discharging state of the electrical energy storage conflict with each other, several charging and discharging state constraints need to be supplemented as follows:
Figure BDA0003265633290000161
Figure BDA0003265633290000162
(0-1 integer variable)
Figure BDA0003265633290000163
(0-1 integer variable)
Next, the nonlinear conversion is performed, and according to the above contents, the number of battery cells for electrical energy storage can be represented by binary, and the formula can be rewritten as follows:
Figure BDA0003265633290000164
Figure BDA0003265633290000165
in the formula, deltagbd,kFor a constructed 0-1 variable, it can represent 2 in the electrical energy storagek-1The discharge state of each battery cell is 1, and is not discharged, and 0.
Under the idea of large M method linearization, the above formula is continuously reconstructed, and the nonlinear term can be represented by constraint through the following formula:
Figure BDA0003265633290000166
Figure BDA0003265633290000167
in the formula, M is a penalty coefficient and is used for constructing a constraint space.
The linear transformation of the electric energy storage discharge power constraint is carried out, and the same method can be used for transforming the electric energy storage discharge power constraint, and the constraint is as follows:
Figure BDA0003265633290000168
Figure BDA0003265633290000169
examples
As shown in fig. 1, the hybrid thermal power station system is composed of a renewable energy source and its interface device, an electrical energy storage device, an in-station thermal cycle system (including an electric boiler, a working medium storage tank, a heat exchange device, a working medium pump, and other components), and a thermal-power station system output interface to a power grid and a thermal power grid. And constructing the energy flow relation of the hybrid thermal power station system by utilizing the overall operating characteristics of the thermal power station.
For electrical energy storage, it is assumed that some of the operating characteristics of each of its cells remain the same. The parameters for organizing each type of module or device are shown in the following table.
TABLE 1 hybrid thermo-station section parameters
Figure BDA0003265633290000171
The power load, the thermal load and the wind power output of the hybrid thermal power station system always change from time to time and segment within one day, and the output curve of a typical day is selected and shown as figure 2.
According to the characteristics of a thermal power station system, three optimization strategies are provided:
strategy 1 is to consider the participation of electrical energy storage and thermal energy storage in system regulation; strategy 2 is to adopt a single electric energy storage support system to operate; and strategy 3 adjusts penalty coefficient and constraint requirements on the basis of strategy 1, and provides a strategy for protecting important loads and improving wind power utilization.
Comparing strategy 1 and strategy 2, fig. 3(a) shows the load shedding situation of the power load in strategy 1 compared with the original power load, and fig. 3(b) shows the load shedding situation of the power load in strategy 2. Comparing the two strategies, it can be seen that a certain load shedding phenomenon occurs at the peak of the electrical load, and the load shedding amount is similar in the two conditions (the load shedding amount is slightly smaller under the strategy 1). Figure 4 shows the thermal load shedding under different strategies. It can be seen that under strategy 1 no thermal load is shed, whereas under strategy 2 the thermal load is shed more. Under the condition of not configuring heat energy storage, the heat load requirement is difficult to meet to a great extent. FIG. 5 shows wind power curves under two strategies, wherein the wind power curves are obtained by adopting the two strategies, and the wind power abandoning phenomenon with small amplitude is generated in both strategies, and the wind power abandoning quantity is close to that of the wind power abandoning strategy.
According to the solution result of the invention, the total daily cost of the hybrid thermal power station under the strategy 1 is 491857 yuan, and the daily energy selling income is 441687 yuan, while the total daily cost of the hybrid thermal power station under the strategy 2 is as high as 663471 yuan, and the daily energy selling income is 4368680 yuan which is lower than that of the strategy 1. Among the costs of the two strategies, the electricity storage construction occupies the largest part, and compared with the optimization result, the energy capacity of the electricity storage configuration in the strategy 1 is about 140MWh, the heat storage is 233MWh, the energy capacity of the electricity storage configuration in the strategy 1 is about 370MWh, but the heat storage construction cost is far lower than the electricity storage, and the economy of the strategy 1 is ensured. The two strategies are close to the four costs of electricity energy storage construction and operation and maintenance, wind abandonment punishment, heat energy storage construction and operation and maintenance and power cut load. Strategy 1 has no action of cutting off the heat load, so the penalty cost is zero and is lower than that of strategy 2. Strategy 1 only ES operation and maintenance one item is slightly higher than strategy 2, because strategy 1 is additionally provided with a heat storage device, and extra operation and maintenance cost is needed. The energy storage construction cost of the strategy 1 is 15 percent less than that of the strategy 2, and the rest three items are higher than that of the strategy 2.
6(a), 6(b) and 6(c) can see that under the strategy 3, the electric load and the heat load are not cut off at all, and the important load is effectively protected; although wind abandoning occurs, the proportion of the wind abandoning is extremely low, and the waste of wind energy is also reduced. According to the solution result of the invention, the total daily chemical cost of the hybrid thermal power station under the strategy 3 is 607851 yuan, which is greatly increased compared with the strategy 1. The main rising cost is generated in the two parts of electric energy storage construction and wind abandon punishment cost, and the rest costs are equal to or even lower than the strategy 1. And because the penalty coefficient of the strategy 3 for wind abandon is more than five times of that of the strategy 1, although the penalty cost of the strategy 3 for wind abandon is higher than that of the strategy 1, the wind abandon amount of the strategy 3 is greatly reduced than that of the strategy 1. From the above analysis, it can be seen that the present invention can adapt to various optimization strategies, and the effectiveness of the proposed comprehensive optimization method is verified, wherein the thermal power plant system under the operation of the strategy 1 has corresponding advantages in the aspects of comprehensive cost, load loss reduction, energy storage effective utilization, and the like, and the strategy 3 effectively reduces load shedding and wind energy waste.
The invention maximizes the economic benefit on the premise of meeting the load demand and matching the fluctuation characteristics of the load demand, optimizes the equipment capacity and the optimal operation strategy as much as possible, improves the utilization rate of renewable energy sources, realizes the optimal distribution of heat and electricity adapting to the fluctuation of the heat-electricity load, and better balances the demands of operation and planning.
It will be understood by those skilled in the art that the foregoing is only a preferred embodiment of the present invention, and is not intended to limit the invention, and that any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the present invention.

Claims (9)

1. The optimal configuration method of the hybrid thermal power station system suitable for renewable energy is characterized in that the hybrid thermal power station system of the renewable energy comprises a wind turbine generator, electric energy storage equipment, electric heating equipment, heat storage equipment and heat exchange equipment, wherein the system generates electric energy by using the wind turbine generator, internally distributes the electric energy to the electric energy storage equipment and the electric heating equipment, and provides electric energy for electric users; the electric heating equipment converts the absorbed electric energy into heat energy to be distributed to the heat storage equipment, the heat storage equipment conveys the stored heat energy to the heat exchange equipment, and the heat exchange equipment provides heat energy for a heat user externally, and the method comprises the following steps:
designing power capacity P with electric energy storagegbrDesigned energy capacity of electric energy storagegbrDesigned power capacity P of heat storagehsrDesigned energy capacity of heat storage EhsrEnergy capacity E of electric energy storage operation and maintenancegbmTotal capacity E of electric heating equipmentEHHeat storage operation and maintenance energy capacity EhsmTotal capacity E of heat exchangerHEXAir volume of waste EWDcutElectric charge discarding capacity EPLcutHeat-removing load EHLcutElectrical load real time power PPLAnd heat load real time power PHLConstructing an objective function and a constraint condition containing the decision variables for decision variables, wherein the objective function is the comprehensive cost of operation and construction in a scheduling period of the hybrid thermal power station system, and the constraint condition comprises the following steps: mixingThe method comprises the following steps of (1) energy flow relation constraint of a thermal power station system, configuration constraint and operation constraint of each device;
based on the objective function and all constraint conditions, the objective function minimizes and solves the decision variables to obtain the optimal equipment configuration capacity EgbrAnd EhsrThe obtained air abandon quantity E of each scheduling time intervalWDcutElectric charge discarding capacity EPLcutHeat-removing load EHLcutElectrical load real time power PPLAnd heat load real time power PHLAnd the optimal operation strategy is used as each component of the hybrid thermal-power station system.
2. The method of claim 1, wherein the objective function is as follows:
Figure FDA0003265633280000011
wherein, FHPSThe overall cost is represented as a function of,
Figure FDA0003265633280000012
represents the penalty cost of wind curtailment in the period t,
Figure FDA0003265633280000013
represents the power load shedding penalty cost for the period t,
Figure FDA0003265633280000014
represents the penalty cost of cutting off the thermal load in the period t,
Figure FDA0003265633280000015
representing the power and heat sales income during the period t, t representing the period of the current scheduling cycle, n representing the total period of the scheduling cycle, CEScRepresenting construction costs of the energy storage device, CESmRepresenting the operating and maintenance costs of the energy storage device, CEHcmRepresenting the construction and operation and maintenance costs of the electric heating apparatus, CHEXcmAnd the construction, operation and maintenance costs of the heat exchange equipment are shown.
3. The method of claim 2,
Figure FDA0003265633280000021
wherein the content of the first and second substances,
Figure FDA0003265633280000022
a wind curtailment penalty coefficient is represented,
Figure FDA0003265633280000023
a penalty factor for electricity abandonment load is represented,
Figure FDA0003265633280000024
a penalty factor for abandoning the thermal load is represented,
Figure FDA0003265633280000025
represents the price of electricity sold, delta t represents the length of the dispatch list,
Figure FDA0003265633280000026
indicating the heat sales price and the superscript t indicating the period of the current scheduling cycle.
4. The method of claim 2,
Figure FDA0003265633280000027
wherein f isdayDaily chemical coefficient representing cost of energy storage device, cgbpRepresenting the cost of the electrical energy storage per unit power, cgbeRepresenting the cost of electrical energy storage per unit capacity, chsRepresenting the cost of heat storage per unit power, chseRepresents the cost of heat storage per unit volume,
Figure FDA0003265633280000028
represents the operation and maintenance cost of the electric energy storage per unit capacity,
Figure FDA0003265633280000029
the heat storage operation and maintenance cost of unit capacity is shown, r is the construction investment reduction rate of the electric heating equipment, k is the service life of the electric heating equipment, rhoEElectric heating equipment cost per unit capacity is represented, delta represents operation and maintenance investment proportionality coefficient, rhoHEXAnd the comprehensive cost coefficient of the construction, operation and maintenance of the heat exchange equipment is represented.
5. The method of claim 1, wherein the hybrid thermal power plant system energy flow relationship constraints comprise:
and (3) representing the constraint of the power supply and demand balance in the t period:
Figure FDA0003265633280000031
wherein, PWDRepresenting wind real-time power, PhscRepresenting electric heating real-time power, PgbRepresenting the real-time comprehensive discharge power of the electric energy storage;
wind power real-time power constraint, electric load real-time power constraint, heat energy supply and demand balance constraint and heat load real-time power constraint in a time period t are respectively expressed as follows:
Figure FDA0003265633280000032
wherein, PWDdRepresenting real-time full power of wind power, DWDcRepresenting the real-time wind abandon ratio, PPLdRepresenting the real-time full power of the electrical load, DPLcRepresenting the real-time load proportion, PhsdIndicating real-time output thermal power, P, of a hybrid thermo-electric plantHLdRepresenting the thermal load real time full power, DHLcRepresenting the real-time heat load shedding ratio.
6. The method of claim 1, wherein each device configuration constraint comprises:
the thermal storage device power capacity configuration constraints and energy capacity configuration constraints are as follows:
Figure FDA0003265633280000033
wherein, PhsrmaxRepresenting the maximum power capacity, T, of the heat storage devicehsdRepresents the maximum heat release time of the heat storage device;
the electrical energy storage device configuration constraints are as follows:
Figure FDA0003265633280000034
0≤numgb≤Numgb
Pgbr＝numgb·pgb
0≤Egbr≤Tgbd·Pgbr
wherein, numgbIndicates the number of battery cells, NumgbDenotes the maximum number of batteries that can be charged, K ═ log2Numgb]Represents NumgbThe conversion represents the maximum binary digit number after the binary system, k represents the k-th digit in the binary system, hgb，kA k-th digit value, p, representing a binary representation of the number of battery cellsgbIndicating the power capacity, T, of a cellgbdRepresents the maximum discharge time of the electric storage device;
the configuration constraints of the electric heating equipment and the heat exchange equipment are as follows:
Figure FDA0003265633280000041
Figure FDA0003265633280000042
wherein, PhscRepresenting electric heating real-time power, PhsdRepresenting the real-time output thermal power of the hybrid thermo-electric plant, and n representing the total period of the scheduling cycle.
7. The method of claim 6, wherein each device operational constraint comprises:
the electric energy storage real-time comprehensive power constraint is as follows:
Figure FDA0003265633280000043
wherein, PgbcRepresenting the real-time charging power of the electrical energy store, PgbdRepresenting the real-time discharge power of the electric energy storage;
the energy capacity constraints of the electrical energy storage at the initial time and in the scheduling period are as follows:
0.1·Egbr≤Egbb≤0.9·Egbr
Figure FDA0003265633280000046
wherein E isgbbIndicating the energy capacity at the initial moment of electrical energy storage, EgbcRepresenting the real-time energy capacity of the electrical energy storage in the scheduling period;
the electric energy storage real-time discharge power and the real-time charging power are respectively constrained as follows:
Figure FDA0003265633280000044
Figure FDA0003265633280000045
wherein, betadIndicating the discharge state, the discharge value is 1, the others are 0, betacIndicating the state of charge, chargingThe electric time value is 1, and the others are 0;
the electrical energy storage real-time energy state constraints are as follows:
Figure FDA0003265633280000051
wherein eta isgbcIndicates the charging efficiency, ηgbdIndicating the discharge efficiency;
when t is equal to n, the heat storage real-time energy state supplement constraint is as follows:
Figure FDA0003265633280000052
8. the method of claim 1, wherein each device operational constraint comprises:
the heat storage real-time comprehensive power constraint is as follows:
Figure FDA0003265633280000053
wherein, PhsRepresenting the real-time integrated charging power, P, of the heat storage devicehscRepresenting heat-stored real-time charging power, PhsdRepresenting the heat storage real-time discharge power;
the energy capacity constraints of the heat storage at the initial moment and in the scheduling period are respectively as follows:
0.1·Ehsr≤Ehsb≤0.9·Ehsr
Figure FDA0003265633280000054
wherein E ishsbIndicating the energy capacity at the initial moment of heat storage, EhscRepresenting the real-time energy capacity of the heat storage in the scheduling period;
the heat storage real-time charging and discharging power is restricted as follows:
Figure FDA0003265633280000055
Figure FDA0003265633280000056
the heat storage real-time energy state constraints are as follows:
Figure FDA0003265633280000057
wherein eta ishscRepresenting heat storage efficiency, ηhsdIndicating the efficiency of heat release;
when t is equal to n, the heat storage real-time energy state supplement constraint is as follows:
Figure FDA0003265633280000058
the constraints on wind curtailment and tangential load are as follows:
Figure FDA0003265633280000061
Figure FDA0003265633280000062
Figure FDA0003265633280000063
wherein epsilonWDcRepresenting the maximum allowable wind curtailment ratio, εPLcRepresenting the maximum allowable power-cut load proportion, epsilonHLcRepresents the maximum allowable heat cut load ratio;
wind abandon at t periodThe quantity constraints are as follows:
Figure FDA0003265633280000064
the cut-off load amount in the t period is constrained as follows:
Figure FDA0003265633280000065
the amount of the shear heat load during the period t is constrained as follows:
Figure FDA0003265633280000066
the energy capacity constraint of the electric energy storage operation and maintenance in the period t is as follows:
Figure FDA0003265633280000067
the energy capacity of the heat storage operation and maintenance in the period t is constrained as follows:
Figure FDA0003265633280000068
wherein, PWDdRepresenting real-time full power of wind power, DWDcRepresenting the real-time wind abandon ratio, PPLdRepresenting the real-time full power of the electrical load, DPLcRepresenting the real-time load proportion, PhsdIndicating real-time output thermal power, P, of a hybrid thermo-electric plantHLdRepresenting the thermal load real time full power, DHLcRepresenting the real-time heat-cut load ratio, PgbcRepresenting the real-time charging power of the electrical energy store, PgbdRepresenting the electrical energy storage real-time discharge power.
9. A hybrid thermal power plant system optimal configuration system for renewable energy, the system comprising: a computer-readable storage medium and a processor;
the computer-readable storage medium is used for storing executable instructions;
the processor is used for reading executable instructions stored in the computer readable storage medium and executing the hybrid thermal power station system optimal configuration method suitable for renewable energy sources of any one of claims 1 to 8.
CN202111085727.0A 2021-09-16 2021-09-16 Hybrid thermal power station system optimal configuration method and system suitable for renewable energy sources Pending CN113869679A (en)

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Publication number Priority date Publication date Assignee Title
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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116306050A (en) * 2023-05-23 2023-06-23 广东电网有限责任公司阳江供电局 Energy storage configuration determining method and device and electronic equipment
CN116306050B (en) * 2023-05-23 2023-08-18 广东电网有限责任公司阳江供电局 Energy storage configuration determining method and device and electronic equipment

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