CN112510682A - Fuel cell cogeneration microgrid day-ahead scheduling method based on dynamic programming - Google Patents

Fuel cell cogeneration microgrid day-ahead scheduling method based on dynamic programming Download PDF

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CN112510682A
CN112510682A CN202011244230.4A CN202011244230A CN112510682A CN 112510682 A CN112510682 A CN 112510682A CN 202011244230 A CN202011244230 A CN 202011244230A CN 112510682 A CN112510682 A CN 112510682A
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heat
storage tank
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scheduling
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孙立
金宇晖
周宇杰
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Southeast University
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06313—Resource planning in a project environment
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas or water supply
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381—Dispersed generators
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/30—The power source being a fuel cell
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/40—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation wherein a plurality of decentralised, dispersed or local energy generation technologies are operated simultaneously
    • 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
    • Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
    • Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
    • 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
    • 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
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Abstract

The invention discloses a day-ahead scheduling method of a fuel cell cogeneration microgrid based on dynamic programming, which constructs an off-grid type household cogeneration system structure taking a fuel cell as a main prime mover and comprises the fuel cell, an air source heat pump, a lithium battery, a phase change heat storage tank and other equipment. By establishing a scheduling model, a safety range and terminal constraints of main equipment of the system, the invention constructs a day-ahead scheduling optimization model taking the minimized fuel cost as a target, and designs a dynamic programming algorithm with the terminal constraints to carry out quantitative solution on the economic model. The invention can realize the heat and power decoupling of the off-grid fuel cell cogeneration system, ensure the balance of heat and power supply and demand, and improve the energy utilization efficiency of the system and the operation reliability of the lithium battery.

Description

Fuel cell cogeneration microgrid day-ahead scheduling method based on dynamic programming
Technical Field
The invention relates to the technical field of optimal scheduling of a fuel cell cogeneration microgrid, in particular to a day-ahead scheduling method of the fuel cell cogeneration microgrid based on dynamic programming.
Background
The hydrogen energy fuel cell has the characteristics of cleanness, low carbon, high efficiency and expandability, and is widely concerned by academic circles and industrial circles, but the potential of the fuel cell for household cogeneration still needs to be excavated. At present, a heating boiler is usually adopted for residential heating, and the heating mode has low efficiency and large carbon emission, and is not ideal in both economy and environmental protection. Compared with the traditional heating boiler, the hydrogen energy fuel cell has the following three advantages in the aspect of household heating: firstly, hydrogen can be prepared by catalytic reforming of natural gas, and natural gas pipelines are widely laid in thousands of households, so that additional hydrogen pipelines are not required to be laid; secondly, the waste heat temperature of the low-temperature fuel cell is matched with the temperature (55 ℃) required by household heating, so that the laying of a heating pipe network can be omitted, and the heat energy loss is reduced to the maximum extent; finally, compared with the traditional cogeneration systems such as a diesel engine, an internal combustion engine and the like, the hydrogen energy fuel cell has low operation noise and small monomer capacity, and is more suitable for household heating.
Although hydrogen fuel cells have many of the advantages described above for cogeneration in homes, the mismatch between the power supply heat to power ratio and the home energy heat to power ratio is currently a major obstacle that impedes their use. Specifically, the thermoelectric ratio of the energy supplied by the fuel cell is regular, but the thermoelectric demand of the user is irregular with time, weather conditions, personnel activities, and the like. In order to guarantee the balance of the heat and power supply and demand and the comfort of users, measures are needed to compensate the mismatch of the heat and power supply and demand ratios. In addition, currently, the optimization of the fuel cell efficiency is mainly applied to fuel cell/battery hybrid electric vehicles, and such optimization problems are usually solved by adopting an optimization control method. Although mature, these models for automobiles cannot be directly used for home energy applications of fuel cells and hybrid systems thereof, and systems and models for home application scenarios are in need of development.
Disclosure of Invention
The invention aims to provide a fuel cell cogeneration microgrid day-ahead scheduling method based on dynamic programming, which is characterized in that a scheduling model is established based on a proton exchange membrane hydrogen energy fuel cell, an air source heat pump, a lithium battery and heat storage tank equipment with a phase change energy storage function related to scheduling, the dynamic programming algorithm is utilized to solve after the description of the scheduling model is optimized, and the energy utilization efficiency is improved on the basis of meeting the heat and power supply and demand balance of an off-grid system.
The technical scheme adopted by the invention is as follows:
a day-ahead scheduling method of a fuel cell cogeneration microgrid based on dynamic programming is characterized in that a scheduling model based on equipment characteristics is established for each equipment related to scheduling in a hybrid fuel cell cogeneration system of the microgrid, the description of the scheduling model is optimized, and solution is carried out based on the scheduling model and the output constraint characteristics of each equipment;
the main equipment of the hybrid fuel cell cogeneration system comprises a proton exchange membrane hydrogen energy fuel cell, an air source heat pump, a lithium battery and a heat storage tank with a phase change energy storage function, and other auxiliary equipment comprises an energy controller, a pipeline, a valve, a circulating water pump and electrical equipment. The hot water produced by the fuel cell and the heat pump is stored in the phase-change energy-storage heat storage tank, and then the heat is taken from the heat storage tank to supply the heat for the residential house and the hot water. And a bus bar power supply structure is adopted, and power generated by the fuel cell, charge and discharge of the lithium battery, the heat pump and power consumption of a user are all connected to the bus bar.
Each of the devices related to scheduling in the hybrid fuel cell cogeneration system includes: proton exchange membrane hydrogen energy fuel cells, heat pumps, lithium batteries and phase change energy storage-based heat storage tanks; the scheduling model comprises main equipment models related to scheduling, namely a proton exchange membrane hydrogen energy fuel cell, an air source heat pump, a lithium battery and a heat storage tank model containing phase change energy storage, and specifically comprises the following steps: a heat supply model of the proton exchange membrane hydrogen energy fuel cell, an electric quantity dynamic characteristic model of the lithium battery, a heat storage degree model of the heat storage tank and a power consumption model of the heat pump;
the optimization scheduling model description comprises determination of an objective function and a constraint which aim at minimizing the operation fuel consumption, and the constraint is performed on the conditions that a lithium battery and a heat storage tank work in three states of charging, discharging or not working, and a heat pump works in two states of starting and stopping, namely 3 multiplied by 2 is 18 states of starting and stopping of equipment.
The method for establishing the scheduling model comprises the following steps:
the proton exchange membrane hydrogen energy fuel cell model is obtained by fitting empirical data, and is shown as a formula (1):
Figure BDA0002769013300000021
wherein etaEAnd ηTRespectively the electrical efficiency and the thermal efficiency of the fuel cell,
Figure BDA0002769013300000022
if the power generation amount of the fuel cell is shown, and the subscript "r" refers specifically to the rated working condition, the heat supply amount of the fuel cell at the time k can be obtained by the formula (2):
Figure BDA0002769013300000023
further, the model of the lithium battery includes its output power
Figure BDA0002769013300000024
The expression is as follows:
Figure BDA0002769013300000025
wherein n is1And n2The number of rows and columns of the battery array, respectively, the current of each battery unit can be obtained by equation (4):
Figure BDA0002769013300000026
wherein the open loop voltage VOCAnd internal resistance R of batterybCan be regarded as a fixed value, the dynamic characteristic of the lithium battery SOC can be expressed as:
Figure BDA0002769013300000027
wherein QBIs the lithium battery capacity and Δ T is the step size.
Further, the heat storage tank model containing the phase change energy storage imitates an SOC model of a lithium battery, the heat storage degree HSD of the heat storage tank is defined, and the expression is as follows:
Figure BDA0002769013300000028
wherein Hs，kIs a charging (positive) or discharging (negative) power,
Figure BDA0002769013300000029
HSD is more than or equal to 0 and less than or equal to 1, and represents the states of no heat and full heat of the heat storage tank respectively when the value is 0 or 1;
the energy efficiency ratio (COP) of the heat pump is obtained by empirical fitting, and the expression is as follows:
Figure BDA0002769013300000031
wherein Δ THPIs the temperature T of hot water produced by the heat pumpDHWAnd ambient temperature at time k
Figure BDA0002769013300000032
The heat supply amount of the heat pump at the moment k is as follows:
Figure BDA0002769013300000033
wherein
Figure BDA0002769013300000034
Is the heat pump power consumption.
The determination method of the target function and the constraint is as follows:
the aim of minimizing the operating fuel consumption is to translate into hydrogen consumption, namely:
Figure BDA0002769013300000035
wherein
Figure BDA00027690133000000313
Is the Lower Heating Value (LHV) of hydrogen;
lithium cell, heat accumulation jar can work and fill, put or the inoperative state, and the heat pump can work opens and stops two kinds of states, then equipment opens and stops the state and have 3 x 2 ═ 18 kinds of circumstances, gets objective function and restraint as follows:
Figure BDA0002769013300000036
Figure BDA0002769013300000037
Figure BDA0002769013300000038
Figure BDA0002769013300000039
Figure BDA00027690133000000310
SOC0＝SOCinit (15)
HSD0＝HSDinit (16)
SOCmin≤SOCk≤SOCmax (17)
HSDmin≤HSDk≤HSDmax (18)
Figure BDA00027690133000000311
Figure BDA00027690133000000312
SOCTL≤SOCN≤SOCTH (21)
HSDTL≤HSDN≤HSDTH (22)
wherein formula (10) is an objective function, N is an optimized time domain, i.e. a scheduling period,
Figure BDA0002769013300000041
the hydrogen consumption obtained by back calculation of the power supply power of the fuel cell at the moment k;
the expressions (11) to (12) are state variables xk＝[SOCk HSDk]The state transition equation of (1) to (8) is determined by an electric balance equation (13), a heat balance equation (14) and equipment characteristic model equations;
formulas (15) and (16) are initial states of the lithium battery and the heat storage tank respectively;
equations (19) and (20) respectively constrain the upper and lower limits of the power generation of the fuel cell and the power consumption of the heat pump, where the upper limit is the rated power of the device.
The equation (21) is terminal constraint, that is, the state constraint of the lithium battery at the end time N of the scheduling period is defined, and the upper and lower limits are states SOC of the state at the initial time0A small neighborhood of; through the constraint, the energy storage device can return to the vicinity of the initial state at the end point moment, so that the energy storage device has sufficient electric energy consumption capability at the beginning of the next scheduling period;
equation (22) is a terminal constraint, i.e. a state constraint of the thermal storage tank at the end time N of the scheduling period is defined, and the upper and lower limits are HSD states of the thermal storage tank at the initial time0A small neighborhood of;by this constraint, the energy storage device can return to near its initial state at the end point in time, thereby ensuring that sufficient thermal energy consumption is available at the beginning of the next scheduling period.
The SOC and HSD terminal constraints are actually near the initial values and are approximately equal, no terminal constraint is guaranteed, all heat storage and power storage consumption is exhausted to the low operation limit by the optimization algorithm, and the subsequent energy supply and storage potential is limited, so that the energy supply and storage potential in operation is brought into full play by the arrangement of the design formulas (21) and (22).
The invention adopts a dynamic programming method to solve the optimal scheduling problem. Specifically, the scheduling optimization model designed by the invention has the characteristics of multivariable, strong coupling and accumulation type objective functions, and is difficult to solve. Therefore, a level set dynamic programming algorithm can be adopted for solving.
The invention has the following beneficial effects:
the invention constructs an off-grid type household combined heat and power system structure which takes a fuel cell as a main prime mover and comprises the fuel cell, an air source heat pump, a lithium battery, a phase change heat storage tank and other equipment. Based on the system structure, a scheduling model, a safety range and terminal constraints of main equipment of the system are established, a day-ahead scheduling strategy which takes the minimum fuel cost as a target is adopted, the description of the scheduling model is optimized, and the economic model is quantitatively solved by adopting a dynamic programming algorithm with the terminal constraints, so that the heat and power decoupling of the off-grid fuel cell cogeneration system can be realized, and the heat and power supply and demand balance is ensured. Meanwhile, the invention also has the following advantages:
the invention optimizes the terminal constraint on the energy states of the lithium battery and the heat storage tank in the description of the scheduling model, and ensures that the equipment has enough energy charging and discharging space during operation.
The application of the phase-change energy storage heat storage tank in the system can improve the energy utilization efficiency of the system and the operation reliability of the lithium battery;
drawings
FIG. 1 is a schematic diagram of a system according to an embodiment of the present invention.
Fig. 2 is a graph of the load prediction before the house day versus the ambient temperature in accordance with an embodiment of the present invention.
Fig. 3 is a result of electrical load scheduling according to an embodiment of the present invention.
Fig. 4 shows the result of thermal load scheduling in the embodiment of the present invention.
Fig. 5 shows the scheduling result before the day when the battery and the heat storage tank are charged and discharged.
Fig. 6 is a result of the electrical load scheduling without the heat storage tank in the embodiment of the present invention.
Fig. 7 is a result of thermal load scheduling without a thermal storage tank in the embodiment of the present invention.
Detailed Description
The schematic diagram of the hybrid fuel cell cogeneration system is shown in fig. 1, and the main devices of the hybrid fuel cell cogeneration system include a proton exchange membrane hydrogen energy fuel cell, an air source heat pump, a lithium battery and a heat storage tank containing phase change energy storage, and other auxiliary devices include an energy controller, a pipeline, a valve, a circulating water pump and an electrical device which are not shown in fig. 1.
The hot water produced by the fuel cell and the heat pump of the system is stored in the phase-change energy-storage heat storage tank, and then the heat is taken from the heat storage tank to supply the heat for the residential house and the hot water. And a bus bar power supply structure is adopted, and power generated by the fuel cell, charge and discharge of the lithium battery, the heat pump and power consumption of a user are all connected to the bus bar.
Each of the devices related to scheduling in the hybrid fuel cell cogeneration system includes: proton exchange membrane hydrogen energy fuel cells, heat pumps, lithium batteries and phase change energy storage-based heat storage tanks; the scheduling model comprises main equipment models related to scheduling, namely a proton exchange membrane hydrogen energy fuel cell, an air source heat pump, a lithium battery and a heat storage tank model containing phase change energy storage, and specifically comprises the following steps: a heat supply model of the proton exchange membrane hydrogen energy fuel cell, an electric quantity dynamic characteristic model of the lithium battery, a heat storage degree model of the heat storage tank and a power consumption model of the heat pump;
the optimization scheduling model description comprises determination of an objective function and a constraint which aim at minimizing the operation fuel consumption, and the constraint is performed on the conditions that a lithium battery and a heat storage tank work in three states of charging, discharging or not working, and a heat pump works in two states of starting and stopping, namely 3 multiplied by 2 is 18 states of starting and stopping of equipment.
The method for establishing the scheduling model comprises the following steps:
the proton exchange membrane hydrogen energy fuel cell model is obtained by fitting empirical data, and is shown as a formula (1):
Figure BDA0002769013300000051
wherein etaEAnd ηTRespectively the electrical efficiency and the thermal efficiency of the fuel cell,
Figure BDA0002769013300000052
if the power generation amount of the fuel cell is shown, and the subscript "r" refers specifically to the rated working condition, the heat supply amount of the fuel cell at the time k can be obtained by the formula (2):
Figure BDA0002769013300000053
further, the model of the lithium battery includes its output power
Figure BDA0002769013300000054
The expression is as follows:
Figure BDA0002769013300000055
wherein n is1And n2The number of rows and columns of the battery array, respectively, the current of each battery unit can be obtained by equation (4):
Figure BDA0002769013300000056
wherein the open loop voltage VOCAnd internal resistance R of batterybCan be regarded as a fixed value, the dynamic characteristic of the lithium battery SOC can be expressed as:
Figure BDA0002769013300000057
wherein QBIs the lithium battery capacity and Δ T is the step size.
Further, the heat storage tank model containing the phase change energy storage imitates an SOC model of a lithium battery, the heat storage degree HSD of the heat storage tank is defined, and the expression is as follows:
Figure BDA0002769013300000061
wherein Hs，kIs a charging (positive) or discharging (negative) power,
Figure BDA0002769013300000062
HSD is more than or equal to 0 and less than or equal to 1, and represents the states of no heat and full heat of the heat storage tank respectively when the value is 0 or 1;
the energy efficiency ratio (COP) of the heat pump is obtained by empirical fitting, and the expression is as follows:
Figure BDA0002769013300000063
wherein Δ THPIs the temperature T of hot water produced by the heat pumpDHWAnd ambient temperature at time k
Figure BDA0002769013300000064
The heat supply amount of the heat pump at the moment k is as follows:
Figure BDA0002769013300000065
wherein
Figure BDA0002769013300000066
Is the heat pump power consumption.
The determination method of the target function and the constraint is as follows:
the aim of minimizing the operating fuel consumption is to translate into hydrogen consumption, namely:
Figure BDA0002769013300000067
wherein
Figure BDA00027690133000000613
Is the Lower Heating Value (LHV) of hydrogen;
lithium cell, heat accumulation jar can work and fill, put or the inoperative state, and the heat pump can work opens and stops two kinds of states, then equipment opens and stops the state and have 3 x 2 ═ 18 kinds of circumstances, gets objective function and restraint as follows:
Figure BDA0002769013300000068
Figure BDA0002769013300000069
Figure BDA00027690133000000610
Figure BDA00027690133000000611
Figure BDA00027690133000000612
SOC0＝SOCinit (15)
HSD0＝HSDinit (16)
SOCmin≤SOCk≤SOCmax (17)
HSDmin≤HSDk≤HSDmax (18)
Figure BDA0002769013300000071
Figure BDA0002769013300000072
SOCTL≤SOCN≤SOCTH (21)
HSDTL≤HSDN≤HSDTH (22)
wherein, the formula (10) is an objective function, and since the system supplies energy for off-grid, the energy is only provided by hydrogen, so that the cost caused by the consumption of the hydrogen is only considered when the economy of the system is calculated;
the meaning of the specific parameters in the formula (10) is that N is an optimized time domain, and N is 24, that is, represents 24 scheduling time instants;
Figure BDA0002769013300000073
the hydrogen consumption obtained by back calculation of the power supply power of the fuel cell at the moment k;
the expressions (11) to (12) are state variables xk＝[SOCk HSDk]The state transition equation of (1) to (8) is determined by an electric balance equation (13), a heat balance equation (14) and equipment characteristic model equations;
formulas (15) and (16) are initial states of the lithium battery and the heat storage tank respectively;
equations (19) and (20) respectively constrain the upper and lower limits of the power generation of the fuel cell and the power consumption of the heat pump, where the upper limit is the rated power of the device.
The equation (21) defines the state constraint of the lithium battery at the end time N of the scheduling period, and the upper and lower limits are states SOC of the lithium battery at the initial time0A small neighborhood of (a). Through the constraint, the energy storage device can return to the vicinity of the initial state at the end point moment, so that the energy storage device has sufficient electric energy consumption capability at the beginning of the next scheduling period;
equation (22) defines the state constraint of the thermal storage tank at the end time N of the scheduling period, and the upper and lower limits are HSD states of the thermal storage tank at the initial time0A small neighborhood of (a). Tong (Chinese character of 'tong')Beyond this constraint, the energy storage device may return to near its initial state at the end time, thereby ensuring sufficient thermal energy consumption capability at the beginning of the next scheduling period.
The SOC and HSD terminal constraints are actually near the initial values and are approximately equal, no terminal constraint is guaranteed, all heat storage and power storage consumption is exhausted to the low operation limit by the optimization algorithm, and the subsequent energy supply and storage potential is limited, so that the sufficient energy charging and discharging space is guaranteed when the equipment operates by the arrangement of the design formulas (21) and (22), and the energy supply and storage potential in operation is furthest exerted.
For a typical load prediction and ambient temperature curve of a house before winter, as shown in fig. 2, a main equipment planning configuration scheme as shown in table 1 is employed.
TABLE 1 Main plant planning and configuration scheme
Figure BDA0002769013300000074
In table 1: the capacity of the fuel cell is 2.5kW, the maximum and minimum output forces are 2.5kW and 0.3kW respectively, the low-grade heat value of hydrogen is 119.96kJ/g, the electric efficiency of the fuel cell is 37 percent, and the heat efficiency is 53 percent; the capacity of the lithium battery is 3.3Ah, the number of rows and columns of the battery array is 6, the upper limit and the lower limit of SOC are 0.9 and 0.3 respectively, the SOC range of the lithium battery at the end of each day is limited to 0.45-0.5, the initial SOC is 0.5, the internal resistance of a single battery of the lithium battery is 20m omega, and the open-circuit voltage is 3.2V; the capacity of the heat storage tank is 10kWh, the upper limit and the lower limit of HSD are respectively 0 and 1, the HSD range of the heat storage tank is limited to 0.48-0.5 at the end moment of each day, the initial value of HSD is 0.5, and the outlet water temperature of the heat storage tank is 55 ℃; the rated power of the heat pump is 500W, the maximum heating power is 500W, and the minimum heating power is 0W.
Based on table 1 and the objective function formula (10), a level set dynamic programming algorithm with terminal constraint is adopted to carry out quantitative solution, and the thermoelectric scheduling results 24 hours and day ahead of typical days in winter are shown in fig. 3 and 4.
As can be seen from fig. 4, in the first 5 hours from the start of a typical day, the electric load is low, the power generation and heat supply of the fuel cell are both at a low level, and at this time, the heat supply of the heat pump and the power consumption are relatively low, so that the heat storage tank containing the phase change energy storage is in a heat release state. Then, after the electric load is increased, the fuel cell power generation waste heat is used to heat the heat storage tank until the electric load is decreased at night. During the noon hours, the heat pump is activated despite the small heat load. This is because the heat pump has a high energy efficiency at the time of high midday ambient temperature, and can generate more heat and store it in the heat storage tank with the same power consumption, and when the ambient temperature decreases and the COP of the heat pump is low at the night, the heat storage tank can be used to supply a part of the heat, thereby reducing the load of the heat pump.
Because the electric quantity loss caused by the internal resistance of the battery is inevitable, compared with the heat storage tank heat charge and discharge quantity in the heat load scheduling result shown in fig. 4, the charge and discharge power value of the lithium battery in the electric load scheduling result shown in fig. 3 is obviously smaller, but the battery still plays an important role in ensuring the balance of the supply and demand of electric power. Taking the time period from 3:00 to 7:00 as an illustration, at 3:00 to 5:00, the fuel cell power generation is greater than the user electrical load, the battery is in a charged state, and at 5:00 to 7:00, the battery is in a discharged state again, which is caused by mismatch of supply and demand thermoelectric ratios. In the first half of the interval, the electric load is low but the heat load is high, the fuel cell generates surplus electricity for generating more waste heat, and the situation is opposite in the second half. Therefore, the battery and the heat storage tank play a role in thermoelectric decoupling.
From fig. 5, after the operation of one day is finished, the energy states of the lithium battery and the heat storage tank return to 50%, and a sufficient energy charging and discharging space is provided, so that the energy supply and demand balance and the thermoelectric decoupling requirements of the system in the next day are ensured.
To demonstrate the role of the thermal storage tank in the system constructed by the invention, the part gives the day-ahead scheduling result of the system without the thermal storage tank. When there is no heat storage tank, redundant fuel cell residual heat will be discharged directly, the state change constraint (12) should be removed, the rated power of the heat pump is raised to 1kW for satisfying the heat load peak, and the heat balance constraint equation (14) should be rewritten as follows:
Figure BDA0002769013300000081
i.e. when the fuel cell residual heat has exceeded the thermal load, the heat pump will be deactivated.
Fig. 6 and 7 show the optimized scheduling results of the system thermoelectric load without the heat storage tank, and it can be seen from the figure that the balance of supply and demand can be realized with the electric power without the heat storage tank, but at 5:00 to 20:00, part of the residual heat of the fuel cell is excessive and can only be dissipated in the environment. The maximum operation power of the heat pump is increased to 900W, and the heat pump cannot be operated under the working conditions of low ambient temperature and small COP. For the reasons, the system scheduling quantification results of the heat storage tank can be summarized in table 2, so that the increase of the heat storage tank can reduce the operation energy consumption, improve the total energy efficiency, reduce the charge and discharge actions of the battery, further reduce the operation cost and the carbon emission, and improve the economy and the environmental protection. TABLE 2 quantized index table for system scheduling with or without heat storage tank
Item Total daily hydrogen consumption (kg) Total energy efficiency (%) Charge and discharge operation of battery
With heat-accumulating tanks 3.39 87.1 3.23
Non-heat storage tank 3.52 83.6 3.99
Relative amount (%) -3.8 +4.0％ -23％

Claims (6)

1. A day-ahead scheduling method of a fuel cell cogeneration microgrid based on dynamic programming is characterized in that a scheduling model based on equipment characteristics is established for each equipment related to scheduling in a hybrid fuel cell cogeneration system, the description of the scheduling model is optimized, and the solution is carried out based on the scheduling model and the output constraint characteristics of each equipment;
each of the devices related to scheduling in the hybrid fuel cell cogeneration system includes: the system comprises a proton exchange membrane hydrogen energy fuel cell, a heat pump, a lithium battery and a heat storage tank based on phase change energy storage, wherein hot water produced by the fuel cell and the heat pump is stored in the heat storage tank, and then heat is taken from the heat storage tank to supply to heating and hot water of a residential house;
the scheduling model includes: a heat supply model of the proton exchange membrane hydrogen energy fuel cell, an electric quantity dynamic characteristic model of the lithium battery, a heat storage degree model of the heat storage tank and a power consumption model of the heat pump;
the optimized dispatching model description comprises the determination of an objective function, safety constraint and terminal constraint aiming at minimizing the operation fuel consumption, and the output constraint of the equipment is carried out aiming at the conditions that the lithium battery and the heat storage tank work in three states of charging, discharging or not working, and the heat pump works in two states of starting and stopping, and the total number of the states is 18.
2. The fuel cell cogeneration microgrid day-ahead scheduling method based on dynamic programming according to claim 1, characterized in that: the method for establishing the scheduling model comprises the following steps:
the proton exchange membrane hydrogen energy fuel cell model is obtained by fitting empirical data, and is shown as a formula (1):
Figure FDA0002769013290000011
wherein etaEAnd ηTRespectively the electrical efficiency and the thermal efficiency of the fuel cell,
Figure FDA0002769013290000012
if the power generation amount of the fuel cell is shown, and the subscript "r" refers specifically to the rated working condition, the heat supply amount of the fuel cell at the time k can be obtained by the formula (2):
Figure FDA0002769013290000013
further, the model of the lithium battery includes its output power
Figure FDA0002769013290000014
The expression is as follows:
Figure FDA0002769013290000015
wherein n is1And n2The number of rows and columns of the battery array, respectively, the current of each battery unit can be obtained by equation (4):
Figure FDA0002769013290000016
wherein the open loop voltage VOCAnd internal resistance R of batterybCan be regarded as a fixed value, the dynamic characteristic of the lithium battery SOC can be expressed as:
Figure FDA0002769013290000017
wherein QBIs the lithium battery capacity, Δ T is the step size;
the heat storage tank model containing the phase change energy storage imitates an SOC model of a lithium battery, the heat storage degree HSD of the heat storage tank is defined, and the expression is as follows:
Figure FDA0002769013290000021
wherein Hs，kIs a charging (positive) or discharging (negative) power,
Figure FDA0002769013290000022
HSD is more than or equal to 0 and less than or equal to 1, and represents the states of no heat and full heat of the heat storage tank respectively when the value is 0 or 1;
the energy efficiency ratio (COP) of the heat pump is obtained by empirical fitting, and the expression is as follows:
Figure FDA0002769013290000023
wherein Δ THPIs the temperature T of hot water produced by the heat pumpDHWAnd ambient temperature at time k
Figure FDA0002769013290000024
The heat supply amount of the heat pump at the moment k is as follows:
Figure FDA0002769013290000025
wherein
Figure FDA0002769013290000026
Is the heat pump power consumption.
3. The dynamic programming-based fuel cell cogeneration microgrid current scheduling method of claim 2, characterized in that: the determination method of the target function and the constraint is as follows:
the aim of minimizing the operating fuel consumption is to translate into hydrogen consumption, namely:
Figure FDA0002769013290000027
wherein
Figure FDA00027690132900000213
Is the Lower Heating Value (LHV) of hydrogen;
the lithium battery and the heat storage tank can work in a charging state, a discharging state or a non-working state, the heat pump can work in a starting state and a stopping state, and the starting and stopping states of the equipment have 18 conditions, so that the objective function and the constraint are as follows:
Figure FDA0002769013290000028
Figure FDA0002769013290000029
Figure FDA00027690132900000210
Figure FDA00027690132900000211
Figure FDA00027690132900000212
SOC0＝SOCinit (15)
HSD0＝HSDinit (16)
SOCmin≤SOCk≤SOCmax (17)
HSDmin≤HSDk≤HSDmax (18)
Figure FDA0002769013290000031
Figure FDA0002769013290000032
SOCTL≤SOCN≤SOCTH (21)
HSDTL≤HSDN≤HSDTH (22)
wherein formula (10) is an objective function, N is an optimized time domain, i.e. a scheduling period,
Figure FDA0002769013290000033
the hydrogen consumption obtained by back calculation of the power supply power of the fuel cell at the moment k;
the expressions (11) to (12) are state variables xk＝[SOCk HSDk]The state transition equation of (1) to (8) is determined by an electric balance equation (13), a heat balance equation (14) and equipment characteristic model equations;
formulas (15) and (16) are respectively the initial state of the lithium battery and the initial state of the heat storage tank;
equations (19) and (20) respectively constrain the upper and lower limits of the power generation power of the fuel cell and the power consumption power of the heat pump, wherein the upper limit is the rated power of the equipment;
the equation (21) is terminal constraint, that is, the state constraint of the lithium battery at the end time N of the scheduling period is defined, and the upper and lower limits are states SOC of the state at the initial time0A small neighborhood of; through the constraint, the energy storage device can return to the vicinity of the initial state at the end point moment, so that the energy storage device has sufficient electric energy consumption capability at the beginning of the next scheduling period;
equation (22) is a terminal constraint, i.e. a state constraint of the thermal storage tank at the end time N of the scheduling period is defined, and the upper and lower limits are that the state isInitial time state HSD0A small neighborhood of; by this constraint, the energy storage device can return to near its initial state at the end point in time, thereby ensuring that sufficient thermal energy consumption is available at the beginning of the next scheduling period.
4. The fuel cell cogeneration microgrid day-ahead scheduling method based on dynamic programming according to claim 3, characterized in that: and (4) carrying out quantitative solving by adopting a level set dynamic programming algorithm with terminal constraint.
5. The fuel cell cogeneration microgrid day-ahead scheduling method based on dynamic programming according to claim 1, characterized in that: the hybrid fuel cell cogeneration system adopts a bus system power supply structure, and the fuel cell power generation, the charge and discharge of the lithium battery, the heat pump and the power consumption of a user are all connected to the bus.
6. The fuel cell cogeneration microgrid day-ahead scheduling method based on dynamic programming according to claim 1, characterized in that: the heat pump adopts an air source heat pump, and the heat source is heat supply of the heat pump, waste heat of the fuel cell and heat storage of the heat storage tank.
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