CN114298440A - Supply and demand collaborative operation optimization method and control device for hydrogen-containing multi-energy system of building - Google Patents
Supply and demand collaborative operation optimization method and control device for hydrogen-containing multi-energy system of building Download PDFInfo
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Abstract
The invention discloses a supply and demand collaborative operation optimization method and a control device of a hydrogen-containing multi-energy system of a building, wherein the optimization method comprises a day-ahead scheduling stage, a day-in fine adjustment stage, a strategy updating stage and a day-in control stage, and a flow-based model is established and solved in consideration of the room thermal dynamic process by combining the influence of the behavior of personnel on the demand side on the electricity-heat-cold demand of the building, and the comfort level requirement and the load requirement of a user are met by taking hydrogen energy as a main energy source.
Description
Technical Field
The invention belongs to the technical field of building multi-energy systems and particularly relates to a supply and demand collaborative operation optimization method and a control device for a hydrogen-containing multi-energy system of a building.
Background
The non-renewable nature of fossil energy sources such as coal, petroleum, etc. makes it impossible to permanently power humans, and the pollution resulting from their combustion can pose a great hazard to earth and human life. Meanwhile, renewable energy technology is rapidly developed, and energy consumption structures are greatly changed.
With the change of energy consumption structure, the proportion of renewable energy is increasing day by day, and the operation structure of energy system also needs to change urgently. Firstly, the new energy is difficult to be fused with the existing power grid due to the special properties of the new energy, the randomness, the intermittency and the fluctuation of wind power and photovoltaic power generation can bring great challenges to the stable operation of the power grid, and therefore the fluctuation of the output of the new energy needs to be stabilized to promote the fusion of the new energy and the existing power grid. Secondly, with the development of efficient cleaning technologies such as fuel cell technology, hydrogen energy gradually enters human vision by virtue of its advantages of high energy density, cleanness, no pollution, abundant storage, and convenient storage and transfer, and is widely regarded as the most promising clean energy source in the 21 st century.
As an essential part for the survival and social life of residents, the building energy consumption accounts for 20-40% of the primary energy consumption, and even reaches 45% in some developed areas. United nations governmentThe interpersonal Committee for climate Change (IPCC) in its fourth evaluation report indicated that by 2030, the world building world can form 60 million tons of CO per year2Equivalent emission reduction potential is the highest in all departments.
Building energy consumption demand uncertainty is high, and has stronger relevance with personnel's activity, and wherein, illumination demand, hot demand, cold demand, computer and display screen power consumption demand all directly relate to personnel's activity condition. Because human body feels and has more elasticity to the requirement of indoor comfortable temperature, the building also can be regarded as an energy storage equipment, plays certain heat preservation and absorbs the undulant effect of demand.
The existing operation optimization method of the hydrogen-containing multi-energy system is based on energy model solution, the established model is relatively simple, the limitation of a hydrogen storage tank, an electrolytic cell, a compressor and a fuel cell on hydrogen pressure and hydrogen flow is not considered, and the flow mode, the heat exchange temperature limitation and the water pump energy consumption of water serving as a heat energy and cold energy storage medium in the system are not considered, so that the simulation of the system is not accurate, and the provided scheduling scheme is not specific and detailed.
The demand forecasting data used by the existing operation optimization method of the hydrogen-containing multi-energy system are mostly fixed curves simulated by software, the demand calculation method is not clear enough, the demand forecasting stage is separated from the operation optimization stage, operation optimization is not carried out in combination with building structure information and personnel activity information of a demand side, and the demand side information and the heat preservation capacity of a building cannot be well utilized.
The existing multi-energy system control method mostly meets the real-time requirements and determines the output of different devices by using simple priorities, the phenomenon that a supply side is not matched with a demand side due to intermittent output of renewable energy, time-of-use electricity price and uncertainty of energy consumption cannot be well solved, and the loss of the renewable energy and the operation cost of the system cannot be well reduced by using energy storage equipment.
The existing multi-energy system control method mostly controls the power/storage capacity of main equipment of each equipment, and the detailed connection mode and control method description of auxiliary equipment related to the system, such as a heat exchanger, a water valve, a water pump, a radiator and the like, is lacked.
Disclosure of Invention
The invention provides a supply and demand collaborative operation optimization method and a control device for a hydrogen-containing multi-energy system of a building, wherein the method combines the influence of the behavior of personnel on the demand side on the electricity-heat-cold demand of the building, establishes a flow-based model in consideration of the thermal dynamic process of a room and solves the flow-based model, meets the comfort level requirement and the load requirement of a user by taking hydrogen energy as a main energy source, provides a method for quickly fine-tuning the original scheduling strategy to adapt to the real-time demand change, and reduces the fluctuation caused by uncertainty of the demand side.
In order to achieve the aim, the method for optimizing supply and demand collaborative operation of the hydrogen-containing multi-energy system of the building comprises the following steps:
the method for optimizing supply and demand collaborative operation of the hydrogen-containing multi-energy system of the building comprises the following steps:
s1, a day-ahead scheduling stage: generating predicted weather data and a demand curve according to the known building structure information, the work schedule, the historical weather data and the actual historical load information, and generating a day-ahead scheduling strategy according to the predicted weather data and the demand curve; the stage comprises four parts, namely, collecting day-ahead information, predicting the day-ahead demand, establishing an optimization model and formulating a day-ahead scheduling strategy;
s2, a day fine adjustment stage: generating an initial fine-tuning strategy table according to the scheduling strategy and the equipment information by taking the day-ahead scheduling strategy generated by S1 or the scheduling strategy updated by S4 as an initial existing scheduling strategy; obtaining actual electricity-heat-cold demand data according to the real-time running state of equipment in a hydrogen-containing multi-energy system of a building and the use condition of each room; fine tuning and updating the existing scheduling strategy by combining the real demand and the fine tuning strategy table so as to meet the actual electricity-heat-cold demand; updating the fine tuning strategy table;
s3, a daily control stage: according to the existing scheduling strategy obtained in S2, the cooling capacity and the heating capacity of each room are specified, and the target operation state of each device in a future time period is obtained;
s4, strategy updating stage: if the fine-tuning of the existing scheduling policy in S2 cannot meet the real demand, the historical load information, the usage status of each room, and the personnel activity condition are updated, demand prediction is performed on the updated historical load information, the usage status of each room, and the personnel activity condition, an updated scheduling policy is generated, and S2-S3 are performed.
A hydrogen-containing multi-energy system supply and demand collaborative operation optimization control system for a building comprises a central control system and an information acquisition subsystem; the central control system performs centralized management and control on each subsystem and equipment in a mode of sending serial port instructions, and is responsible for operation work of the whole system and instruction generation and sending;
the central control system comprises an operation module, a sensor data processing module, a communication module, a serial port module and a human-computer interaction module; the operation module is connected with the sensor data processing module, the communication module and the serial port module, the sensor data processing module is connected with the operation module, the communication module and the serial port module, and the human-computer interaction module is connected with the communication module and the serial port module; the operation module is used for calculating a scheduling result and generating a control command, and the operation steps of the operation module follow S1, S2, S3 and S4 in the supply and demand cooperative operation optimization method of the hydrogen-containing multi-energy system of the building; the sensor data processing module is used for preprocessing and storing the received information acquired by the information acquisition subsystem and sending the information to the operation module; the communication module and the serial port module are responsible for transmitting signals with each subsystem; the human-computer interaction module is used for assisting an operator in managing the system and updating data; the building information acquisition subsystem consists of sensors arranged inside and outside a building and a sub-node single chip microcomputer, wherein the sensors comprise wall inside and outside temperature sensors, indoor temperature sensors, illumination sensors and infrared sensors.
Compared with the prior art, the invention has the following beneficial effects:
the method provided by the invention carries out modeling based on the flow in the stage of establishing the optimization model, the model based on the flow is more suitable for the physical properties of hydrogen, hot water and cold water in the system, better simulation can be provided for the physical process of energy conversion of the system, the cooperative scheduling between the supply side and the demand side is considered, especially the influence of the energy consumption of the demand side on the operation of equipment at the supply side is considered, the provided scheduling strategy is more detailed, a scheme with higher operability is provided for the actual scheduling, and the economic benefit and the environmental benefit of the system are greatly improved.
The method combines the modeling of the demand side based on the room thermal dynamic state with the modeling of the supply side based on the flow, has more accurate simulation effect on the building, can obtain the heat demand and the cold demand specific to the room, is convenient for implementing different control strategies on different rooms subsequently, simultaneously considers the relationship between the electricity-heat-cold demand in the building and the current indoor temperature, the outdoor temperature and the building structure, and enables the scheduling strategy to utilize the heat preservation capacity of the building to relieve the mismatch condition of the supply and demand sides and reduce the energy consumption under the condition of different renewable energy output and electricity purchasing prices in different periods.
The method combines the influence of the personnel behaviors of the demand side and the use conditions of the rooms (whether the rooms are used or not and the number of people in the rooms) on the electricity-heat-cold demands with the supply side operation optimization method, and calculates the indoor heat-cold demands by replacing the fixed target temperature with the acceptable indoor temperature interval, thereby increasing the flexibility of the scheduling strategy of the supply side.
The method of the invention realizes the fine adjustment of the current scheduling strategy in the day fine adjustment stage so as to meet the actual electricity-heat-cold requirement. The fine-tuning method combines a day-ahead scheduling strategy and a real-time equipment state, considers the startup and shutdown cost of the equipment and the influence of fine tuning on subsequent scheduling, reduces the change of the start-stop state of the equipment and the influence of the fine tuning on the original scheduling strategy while reducing the total energy consumption of the system.
The system provided by the invention considers the recycling of heat generated by the electrolytic cell during working, the heat generated by the electrolytic cell is taken out through the cooling liquid and finally flows to the hot water tank after heat exchange through the heat exchanger, and the capacity efficiency of the whole system is improved.
The system of the invention defines the flowing mode of water as the heat energy and cold energy storage medium, namely the connection mode and the heat exchange temperature limit of the hot water tank, the fan coil, the fuel cell and the heat exchanger thereof, the electrolytic tank and the heat exchanger thereof, the solar heat collector and the water inlet end and the water outlet end of the heat exchanger thereof, and adopts the constant-current and variable-temperature method to model the system.
The system comprises a central control system and a building information acquisition subsystem, the connection modes of the subsystems and the internal devices of the subsystems are defined, and the monitoring content and the control mode of each subsystem are determined according to the characteristics of the hydrogen-containing multi-energy system of the building considering supply and demand coordination.
Drawings
FIG. 1 is a schematic diagram of a method for optimizing supply and demand collaborative operation of a hydrogen-containing multi-energy system of a building according to the invention;
FIG. 2 is a diagram of the construction of the hydrogen-containing multi-energy system and the energy flow of the building according to the present invention;
FIG. 3 is a schematic diagram of the day trimming method of the present invention;
FIG. 4 is a flow chart of a policy update method of the present invention;
FIG. 5 is a schematic diagram of the hydrogen-containing multi-energy system control device for buildings considering supply and demand coordination;
FIG. 6 is a schematic diagram of a fuel cell subsystem of the present invention;
FIG. 7 is a schematic view of a solar collector subsystem of the present invention;
FIG. 8 is a schematic diagram of a cold water tank-electric boiler-absorption chiller subsystem of the present invention;
FIG. 9 is a schematic view of an electrolyzer-compressor-hydrogen storage tank subsystem of the present invention;
FIG. 10 is a schematic view of a photovoltaic power generation subsystem of the present invention.
Detailed Description
The present invention will be described in detail below with reference to the embodiments with reference to the attached drawings. It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict.
The following detailed description is exemplary in nature and is intended to provide further details of the invention. Unless otherwise defined, all technical terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention.
Embodiments of the present invention will be described below with reference to the accompanying drawings.
Referring to fig. 2, a hydrogen-containing multi-energy system for buildings comprises the following components: the system comprises a central control system, an in-building information acquisition subsystem, a fuel cell subsystem, a cold water tank-electric boiler-absorption refrigerator subsystem, an electrolytic bath-compressor-hydrogen storage tank subsystem, a hot water tank subsystem, a photovoltaic power generation subsystem and a solar heat collector subsystem.
The energy circulation mode among the subsystems is as follows:
in the system, solar energy is used for photovoltaic panel power generation in a photovoltaic power generation subsystem and heat generation of a solar heat collector in a solar heat collector subsystem, and due to the renewable property of the solar energy, the solar energy utilization rate of the system is increased as much as possible on the premise that the operation of other equipment and the stability of the system are not influenced. The generated energy of the solar energy can meet the electric demand, can also supply power for power consumption equipment, and can be stored in the form of hydrogen energy through the operation of 'electrolytic hydrogen production-hydrogen storage-fuel cell cogeneration', or stored in the form of hot water/cold water after being supplied to an electric boiler for heating. Wherein, power consumptive equipment includes: electric boiler, water pump, electrolysis trough, compressor.
In the system, the electricity is bought to meet the real-time electricity demand, or the electricity is supplied to an electric boiler and a water pump to indirectly meet the real-time heat-cold demand, and meanwhile, due to the influence of time-of-use electricity prices, namely the prices of electricity purchased in units in different periods are different, the electricity is bought when the electricity price is lower and the electricity is used when the electricity price is higher, so that the economic benefit of the system is improved, the electricity is bought and the electricity-heat-cold demand can be met in the period when the electricity price is higher after the electricity is stored, and the specific method comprises the following steps: the electric boiler heats and stores in the form of heat energy, electrolyzes to produce hydrogen and stores in the form of hydrogen energy.
In the system, hydrogen can be bought for cogeneration of fuel cells or stored in a hydrogen storage tank.
The fuel cell subsystem generates electricity by using hydrogen purchased from a hydrogen market or flowing out of a hydrogen storage tank, the generated electricity can supply electricity demand and power consumption equipment, the generated heat is taken out by cooling liquid and is subjected to heat exchange and cooling by a heat exchanger of the fuel cell subsystem, a water inlet at the other end of the heat exchanger is connected with hot water return water of a fan coil and hot source return water of an absorption refrigerator through a three-way valve, a water outlet is connected with a water inlet of a hot water tank, namely the heat generated by the fuel cell subsystem flows into the hot water tank subsystem after heat exchange.
The electrolytic cell-compressor-hydrogen storage tank subsystem electrolyzes water to produce hydrogen by using electric energy, and the produced hydrogen enters the hydrogen storage tank after being compressed by the compressor. The heat generated when the electrolytic cell electrolyzes hydrogen is taken out by the cooling liquid and is subjected to heat exchange and cooling through the heat exchanger of the electrolytic cell-compressor-hydrogen storage tank subsystem, the water inlet at the other end of the heat exchanger is connected with the water outlet of the hot water tank, and the water outlet is connected with the hot water port of the hot water tank, namely, the heat generated when the electrolytic cell works is utilized to heat the hot water in the hot water tank.
The cold water tank-electric boiler-absorption refrigerator subsystem further heats hot water output by the hot water tank by using the electric boiler, outputs the heated high-temperature hot water to a demand side according to an instruction to meet a heat demand, or enters the absorption refrigerator for refrigeration to meet a cold demand.
The solar heat collector subsystem absorbs solar energy to generate heat, the generated heat is used for heating the antifreeze, the antifreeze flows through the heat exchanger of the solar heat collector subsystem to exchange heat, the water inlet at the other end of the heat exchanger is connected with the water outlet of the hot water tank, the water outlet is connected with the water inlet of the hot water tank, and the heat generated by the solar heat collector is utilized to heat the hot water in the hot water tank.
The photovoltaic power generation subsystem utilizes solar energy to generate power, and the generated power can be supplied to power demand and power consumption equipment.
The electricity utilization form of the electric terminal is line power supply; the electric boiler, the water pump and the compressor are powered by alternating current, the electrolytic cell is powered by direct current, electricity purchased by a power grid is alternating current, and electricity generated by the fuel cell and photovoltaic power generation are both direct current.
The heat terminal uses heat to supply heat for the coil of the air-conditioning fan, and the part of heat is provided by the hot water in the hot water tank after being heated by the boiler. The heat generated by the fuel cell, the solar heat collector and the electrolytic cell is stored in the hot water tank in a hot water mode.
The cold terminal supplies cold for the coil pipe of the air-conditioning fan in a cold mode, part of cold energy is provided by cold water flowing out of the cold water tank, and cold energy generated by the absorption refrigerator is stored in the cold water tank in a cold water mode.
Hydrogen energy is stored in the hydrogen storage tank, can be used to fuel cell cogeneration in order to satisfy the demand for electricity and heat, can acquire by buying hydrogen or electrolysis hydrogen manufacturing, and the hydrogen that the electrolysis cell electrolysis produced need be handled through the compressor before getting into the hydrogen storage tank, and the hydrogen of buying can directly be used to fuel cell cogeneration.
Referring to fig. 1, an embodiment of the present invention provides a supply and demand collaborative operation optimization method for a hydrogen-containing multi-energy system of a building, and an operation process thereof includes the following stages: the method comprises a day-ahead scheduling stage, a day-interior fine-tuning stage, a strategy updating stage and a day-interior control stage, and the related main structures comprise: each subsystem monitoring device, each subsystem controller and central control system, the object interacting with it includes: target building, operator, equipment.
Wherein, each subsystem includes: the system comprises a building information acquisition subsystem, a fuel cell subsystem, a cold water tank-electric boiler-absorption refrigerator subsystem, an electrolytic cell-compressor-hydrogen storage tank subsystem, a hot water tank subsystem, a photovoltaic power generation subsystem and a solar heat collector subsystem; each apparatus includes: the system comprises a hydrogen storage tank, a hot water tank, a cold water tank, a solar thermal collector, a photovoltaic power generation plate, a fuel cell, an electrolytic cell, an electric refrigerator, an electric boiler, an absorption refrigerator, a compressor, a heat exchanger, a water pump and a valve.
Wherein, each stage task in the operation process is as follows:
s1, a day-ahead scheduling stage: and generating predicted weather data and demand curves according to the known building structure information, the working schedule, the historical weather data and the actual historical load information, wherein the predicted demand curves comprise an electric demand curve, a heat demand curve and a cold demand curve, and generating a day-ahead scheduling strategy by considering the supply and demand cooperative operation scheduling design according to the predicted weather data and demand curves. The stage comprises four parts, namely, collecting day-ahead information, predicting the day-ahead demand, establishing an optimization model and formulating a day-ahead scheduling strategy;
s2, a day fine adjustment stage: generating an initial fine-tuning strategy table according to the scheduling strategy and the equipment information by taking the day-ahead scheduling strategy generated by S1 or the scheduling strategy updated by S4 as an initial existing scheduling strategy; receiving the real-time running state of the equipment and the use condition of each room transmitted back by each subsystem monitoring device, and obtaining actual electricity-heat-cold demand data; fine-tuning the existing scheduling strategy by combining the real demand and the fine-tuning strategy table to obtain the existing scheduling strategy so as to meet the actual electricity-heat-cold demand; the fine tuning policy table is updated. Wherein the fine tuning policy table comprises an available action set per period, a cost per action, an expandable upper limit and a contractible lower limit per action. The method comprises the following steps of collecting real-time data, generating actual requirements, generating and updating a fine-tuning strategy table and fine-tuning the existing scheduling strategy;
s3, a daily control stage: according to the existing scheduling strategy obtained in S2, the cooling capacity and the heating capacity of each room are specified, and the target running state of each device in a future time period is obtained, wherein the target running state comprises whether the device is started, the working mode, the working power and the working duration; sending the target running state of each device to each subsystem controller and an air-conditioning fan coil pipe terminal through a serial port module and a communication module in a central control system; and each subsystem controller and the air-conditioning fan coil pipe terminal are controlled according to the target running state.
S4, strategy updating stage: if the fine-tuning of the existing scheduling policy in S2 cannot meet the real demand, the historical load information, the usage status of each room, and the personnel activity condition are updated, so that demand prediction is performed on the updated historical load information, the usage status of each room, and the personnel activity condition, and an updated scheduling policy is generated, and S2-S3 are performed.
S1, the day-ahead scheduling stage includes the following steps:
s101, collecting day-ahead information, wherein the day-ahead information comprises building structure information, a work schedule, historical weather data and actual historical load information to generate a predicted demand curve, and the building structure information comprises a building space distribution diagram, the volume of each room, the wall area, the wall thickness, the wall number, the wall specific heat capacity, the wall density, the window position and the area; the work schedule includes: the service condition of each room and the number of people in the room, the weather information comprises: temperature, illumination intensity and illumination angle.
S102, forecasting the demand in the day ahead, considering the influence of the personnel activities on the electricity-heat-cold demand, dividing the electricity demand into a rigid electricity demand for building operation and a flexible electricity demand for the personnel activities in the building, and respectively forecasting to obtain the initial electricity-heat-cold demand. The building operation rigidity electricity demand refers to a stability demand which is less influenced by daily personnel activities, and is estimated according to building structure information and historical actual loads; the flexible electric demand of personnel activity in the building refers to the demand greatly influenced by personnel activity, and the forecast is carried out by combining the forecast weather information, the personnel scheduling table and the building structure information. Wherein the hot-cold demand is predicted from predicted weather information, personnel schedules, hot comfort zones and building structure information.
S103, establishing an optimization model, establishing the optimization model according to the building structure information and the equipment parameters in the system, wherein the optimization model aims to minimize the total building energy consumption on the premise of meeting the building electricity-heat-cold requirements and the personnel comfort, and takes the electric balance, the heat balance, the cold balance, the hydrogen balance equation, the equipment output equation and the heat comfortable area of each room as constraints.
S104, making a day-ahead scheduling strategy: and solving according to the initial electricity-heat-cold requirements obtained in the step S102 and the optimization model established in the step S103 to obtain a day-ahead scheduling strategy. When solving, the optimization model coupled with the n rooms is decoupled into n models, namely, the original problem is decoupled into a big problem and n subproblems, the n rooms are considered as n nodes which mutually have circulation, the specific constraint in a single room is not considered when solving the big problem, only the energy circulation balance constraint between the nodes of the n rooms is considered, and the subproblems in each room are respectively solved under the condition of meeting the constraint between the nodes, so that each room achieves the best economic benefit, and a day-ahead scheduling strategy is worked out.
Further, the constraints in the optimization model in S103 include:
1. the system and the power grid are in interactive constraint and electric balance equation:
in the hydrogen-containing multi-energy system, heat generated by the fuel cell and the electrolytic cell during cogeneration is taken out by the cooling liquid and exchanges heat with water flow in the heat exchanger, and heat generated by the solar heat collector is taken out by the antifreeze and exchanges heat with the water flow in the heat exchanger, so that the flow rate of water in the three water circulation loops is controlled by the water pump. Therefore, in addition to the conventional power consuming unit, the constraint also takes into account the power consumption of the water pump; meanwhile, the model also limits the buying electric power and the selling electric power according to the transformer capacity, so that the model is more suitable for practical conditions. Wherein,
for the generated power of the fuel cell for the k period,
the generated power of the solar panel in the k period,
for the electric power purchased by the k-period system from the grid,
feeding back the electric power of the power grid for the k-period system,
for the electric power of the electrolytic cell in the period k,
for the electric power of the electric boiler for the period k,
for the electric power of the compressor for the period k,
for the electric power of the water pump in the k time period,
for the amount of electricity required for the building during the k time period, τ is the duration of one time period, βpvIs the photoelectric conversion coefficient of the solar panel, rkIntensity of solar radiation in the k period, SpvMaximum area of the solar panel that can receive light, PtfThe upper bound constraint of buying and selling power to the power grid in a single time period.
The power grid electricity buying flag bit is in k time period, when the system buys electricity from the power grid, the flag bit is 1, otherwise, the flag bit is 0;
and the power grid electricity selling zone bit in the k period is 1 when the system sells electricity to the power grid, otherwise, the power grid electricity selling zone bit is 0.
2. The interaction constraints of the system with the hydrogen market are:
wherein,
the quality of hydrogen purchased from the hydrogen market for the k-period system,
the quality of hydrogen gas purchased from the hydrogen market and stored into the hydrogen storage tank for the period k,
the mass of hydrogen purchased from the hydrogen market for the k period and flowing into the fuel cell to generate electricity.
3. The constraint conditions of the electrolytic cell in the system are as follows:
the model considers the recycling of heat generated in the electrolytic process of the electrolytic cell, adopts a constant-flow temperature-changing method for modeling, and simultaneously considers the temperature of cooling liquid and the water temperature of a water inlet at the other end of the heat exchanger to switch the state of the heat exchanger and the state of a three-way valveThe influence of state, namely when the water temperature at the water inlet at the other end of the heat exchanger is higher than the temperature of the cooling liquid, the heat exchange can not be normally carried out, and in order to ensure that the temperature of the cooling liquid flowing back to the electrolytic bath meets the requirement, air cooling heat dissipation is carried out on the cooling liquid. Wherein,
quality of hydrogen, beta, produced for electrolysis in an electrolysis cell during a period of kelThe conversion coefficient of the electrolysis bath for electrically converting hydrogen,
is the upper limit of the power of the electrolytic cell,
is the heat generated by the cell during the k period, thetaelThe coefficient of heat generation of the electrolytic cell,
pressure of hydrogen production, alpha, for the electrolyzer during the period kelFor the conversion coefficient of the hydrogen mass and the pressure in the electrolytic cell,
andU elrespectively the upper pressure limit and the lower pressure limit of the hydrogen produced by the electrolytic cell,
for the mass of hydrogen gas produced by the electrolytic cell and flowing into the hydrogen storage tank after being compressed by the compressor during the period k,
mass of hydrogen produced by the electrolyzer and flowing into the fuel cell for cogeneration during a period k, c is the specific heat capacity of water, ρwtIs the density of water, Vel,exThe flow rate of the water which participates in the heat exchange of the heat exchanger of the electrolytic cell and flows into the hot water tank in the period tau,
cooling liquid with electrolytic bath in heat exchanger for k periodThe temperature of the water flowing into the hot water tank after heat exchange,
temperature of water flowing into the heat exchanger for the hot water tank for the period k, telFor the temperature of the bath coolant flowing into the heat exchanger, thetaexThe heat exchange efficiency of the heat exchanger is improved.
The use flag bit of the electrolytic cell in the k time period is 1 when the electrolytic cell is used, otherwise, the use flag bit is 0;
the use flag bit of the heat exchanger of the electrolytic cell in the k time period is 1 when the heat exchanger of the electrolytic cell is used, otherwise, the use flag bit is 0; epsilon+A positive infinitesimal amount; m is a positive maximum.
4. The compressor in this system is constrained as follows:
when the model is used for modeling the compressor, the relation between the compression coefficient of the compressor and the hydrogen output pressure is considered, the compressor is divided into different working states according to the hydrogen output pressure to be solved, and the modeling of the compressor is more accurate. Wherein L is the number of working states of the compressor,
the compression factor of the compressor is influenced by the operating conditions of the compressor,
and
is a compressorAnd outputting the lower pressure limit and the upper pressure limit of the hydrogen when the hydrogen generator works in the state I.
The flag bit is the working state of the compressor, and is 1 when the compressor works in the state of l in the period of k, otherwise, is 0.
5. The hydrogen storage tank in the system is subjected to the following constraints and hydrogen balance constraints:
wherein,
the mass of hydrogen gas flowing into the hydrogen storage tank for the period k,
the mass of hydrogen gas flowing out of the hydrogen storage tank for the period k,
the upper limit of the mass of hydrogen gas flowing into or out of the hydrogen storage tank per unit time,
the mass of hydrogen in the hydrogen storage tank at the beginning of the time period t,
is the hydrogen pressure in the hydrogen storage tank at the beginning of the time period t,U htand
a lower pressure limit and an upper pressure limit of hydrogen gas allowed for the hydrogen storage tank.
The flag bit of hydrogen gas flowing into the hydrogen storage tank is 1 when hydrogen gas flows into the hydrogen storage tank in the period k, otherwise, the flag bit is 0;
is the mark position of hydrogen flowing out of the hydrogen storage tank, when the hydrogen flows out of the hydrogen storage tank in the k period, the mark position is 1, otherwise, the mark position is 0, R is the molar gas constant, t ishtIs the hydrogen storage tank temperature.
6. The fuel cell of the system is subject to the following constraints:
when the model is used for modeling the fuel cell, the pressure constraint when the hydrogen produced by the electrolytic cell flows into the fuel cell is considered, namely, the hydrogen can be utilized by the fuel cell only when the pressure of the hydrogen produced by the electrolytic cell is large enough; meanwhile, the influence of the temperature of the fuel cell coolant and the water temperature of the water inlet at the other end of the corresponding heat exchanger on the switching state of the heat exchanger and the switching state of the three-way valve is also considered, namely when the water temperature of the water inlet at the other end of the heat exchanger is higher than the temperature of the fuel cell coolant, heat exchange cannot be normally carried out, and air cooling heat dissipation is required for ensuring that the temperature of the coolant flowing back to the fuel cell meets the requirement. Wherein,
for the mass of hydrogen consumed by the fuel cell during the k period,U fcminimum pressure of input hydrogen allowed for fuel cell, R is molar gas constant, telIs the working temperature of the electrolytic bath,
the electricity generation coefficient of the fuel cell in the k period;
is the fuel cell heat generation coefficient for the period k,
andP fcfor the upper and lower limits of the output power of the fuel cell, VcThe flow rate of water which participates in the heat exchange of the fuel cell heat exchanger and flows into the hot water tank in the period tau,
for a period k of time the temperature of the water flowing to the hot water tank after heat exchange with the fuel cell coolant in the heat exchanger,
the water temperature before heat exchange is participated in k time period, the part of water participated in heat exchange is composed of absorption type refrigerator backwater and fan coil pipe heat source backwater, tfcIs the temperature at which the fuel cell coolant flows into the heat exchanger.
The "electrolyzer-fuel cell" flow flag for the k period is 1 when the flow channel is in use (i.e., the electrolyzer is producing hydrogen and flowing to the fuel cell for cogeneration), and 0 otherwise.
The fuel cell use flag bit is a k period, and is 1 when the fuel cell is used, otherwise, the flag bit is 0;
the fuel cell heat exchanger for period k uses a flag bit that is 1 when the fuel cell heat exchanger is in use and 0 otherwise.
7. The solar collector of the system is subject to the following constraints:
the model considers the flowing mode of the heat generated by the solar heat collector, namely, the heat exchanger corresponding to the solar heat collector heats hot water in the hot water tank, so that the heat is stored in the hot water tank, the influence of the anti-freezing temperature of the solar heat collector and the water temperature of the water inlet at the other end of the corresponding heat exchanger on the switching state of the heat exchanger is considered, namely, when the anti-freezing temperature is lower than the water temperature of the water inlet at the other end of the heat exchanger, the heat exchanger is closed to reduce the power consumption of the water pump. Wherein, csIs the specific heat capacity rho of the antifreeze in the solar heat collectorstcIs the density, V, of the antifreeze in the solar heat collectorstcCapacity of antifreeze,. mu.stc,lossThe heat exchange coefficient of the antifreeze with the outside, AstcThe surface area of the antifreeze liquid pipe contacted with the outside,
is the antifreeze temperature for the period k,
is the ambient temperature for the period k of time,
beta is the heat of the antifreeze fluid participating in the heat exchange of the heat exchanger in the k time periodstcIs the photo-thermal conversion coefficient of the solar heat collector, SstcArea of solar energy absorption for solar heat collecting plate, rkAmount of solar radiation in k time period, pwtIs the density of water, Vstc,exThe flow rate of the water which participates in the heat exchange of the heat exchanger of the solar heat collector and flows into the hot water tank in the period tau,
for flow out of heat exchangers after heat exchange for period kThe temperature of the water is set to be,
temperature of water flowing into the heat exchanger for the hot water tank for the period k, tstcIs the temperature of the antifreeze fluid flowing into the heat exchanger.
For the "solar collector-heat exchanger-hot water tank" flow flag, the flow channel is 1 when used (i.e. when the solar collector heat exchanger is in operation) during the period k, and is 0 otherwise.
8. The hot water tank of this system is subject to the following constraints:
the model takes into account the loss of heat in the hot water tank, and the amount of loss is related to the outdoor temperature. Wherein, VwtIs the volume of hot water in the hot water tank,
is the temperature of hot water in the hot water tank, mu, in a period of klossIs the heat loss coefficient of the hot water tank, AwtIs the contact area between the hot water tank and the outside,
is the indoor ambient temperature for the period k,
andT wtrespectively the upper and lower limits of the water temperature of the hot water tank.
9. The electric boiler of the system is subject to the following constraints:
wherein, VcThe flow of the hot water is output to the electric boiler,
the temperature of the hot water flowing out of the electric boiler is k time periodsebIn order to improve the heat generating efficiency of the electric boiler,
the upper power limit of the electric boiler.
10. The system absorption refrigerator is subject to the following constraints:
wherein, VccThe flow rate of the cold water output for the absorption refrigerator,
the temperature of the cooling water flowing into the absorption refrigerator for the period k,
the temperature of the cooling water flowing out of the absorption refrigerator is k time period,
for the cooling efficiency of the absorption refrigerator for the k period,
for the heat absorbed by the absorption refrigerator for the period k,
in order to be the upper limit of the capacity of the absorption refrigerator,
andT acrespectively an upper limit and a lower limit of the water temperature output by the absorption refrigerator.
11. The cold water tank of this system is subject to the following constraints:
the model takes into account the loss of cold in the cold water tank, and the amount of loss is related to the outdoor temperature. Wherein, VctIs the amount of cold water in the cold water tank k period,
is the temperature of the cold water in the cold water tank k time period,
andT ctrespectively the upper and lower limits of the water temperature of the hot water tank.
12. The water pump of this system is subject to the following constraints:
wherein, betapumpIs the unit flow power consumption coefficient of the water pump, Vel,wIs the flow rate of cooling water flowing through the corresponding heat exchanger of the electrolytic cell in the period tau, Vfc,wIs the flow rate of cooling water flowing through the corresponding heat exchanger of the fuel cell in a period of tau, VstcThe flow rate of the antifreeze solution flowing through the corresponding heat exchanger of the solar heat collector in the period tau.
The cold/hot demand status flag is set for period k, 1 when there is a hot demand, and 0 otherwise.
13. The indoor air thermal dynamics related constraints of the system are as follows:
the model takes into account demand side house wall heat transfer and couples it with a supply side operational optimization method. Wherein,
the heat capacity of the air in the room i, in relation to the volume of the room i,
the temperature of room i in time period k, J is the set of all walls connected to room i,
as the heat transfer coefficient between the wall j and the air,
is the area of the wall j,
is the inner wall temperature h of the wall j in the room i in the period kwinIs the coefficient of thermal conductivity, S, of the windowwinThe area of the window is the area of the window,
is the outside-window ambient temperature for period k,
fan coil outlet flow for room i in time period k, CairIs the specific heat capacity of the air,
is the temperature at the outlet of the fan coil,
the heat released by the demand side lighting, personnel, computers and displays of the room i in the period k.
14. The thermal dynamic constraints on the building interior walls in the system are as follows:
wherein, CwIs the specific heat capacity of the wall, rhowThe density of the wall body is shown as the wall body density,
is the surface area of the wall j in the room i, lwIs the thickness of the wall, κ is the thermal conductivity inside the wall,
the temperature of the outer wall of the wall j in the room i in the period k,
heat generated to the inner wall of the wall j in the room i for illumination.
15. The thermal dynamic constraints on the outer wall of the building in the system are as follows:
wherein,
for the heat exchange coefficient between the building wall and the outdoor,
the heat generated for the time period k for the illumination to the outer wall of the wall j in the room i.
16. The thermal comfort zone constraints of this system are as follows:
the model considers the influence of the using state of rooms in the building on the indoor heat-cold demand. Wherein,
andt roomrespectively an upper limit and a lower limit of the thermal comfort temperature of the room in use,
andt nrespectively, the upper limit and the lower limit of the indoor temperature of other unused buildings.
The user status for indicating the room i in k period is 1 if used and 0 if not used.
17. The electricity demand of the demand side of the system is as follows:
wherein,
for the electrical needs of room i during time period k,
the amount of power consumed by the fan coil during time k for room i,
the amount of power consumed for the illumination of room i during the period k,
the amount of power needed by the computer and display for room i during time period k.
18. The heat production on the demand side of the system is constrained as follows:
wherein,
the total heat production for room i on the demand side during period k,
the heating value for the illumination of room i in the period k,
the heating value of the computer and the display in the room i in the period k,
for the heating value of the person in room i in the k period, μoCoefficient of heat production for personnel, oi,kNumber of people in room i in k time period, μlightIn order to generate the heat coefficient for the illumination,
lighting power consumption for room I in k time period, IlightThe illumination provided for a unit of electrical energy,
for the illumination provided by the solar energy in the k period,
requiring illumination, mu, for each room in the k periodcIs the heat generation coefficient of a computer and a display,
for the power consumption of the computer and the display in the time period k for the room i,
the power of the computer and display for room i during time k.
19. The fan coil model of the system is as follows:
wherein,
is the upper limit of the power of the fan coil, V is the working state set of the fan coil,
and the flag bit is the working state flag bit of the fan coil, and is 1 when the fan coil works in the state v in the k time period, otherwise, the flag bit is 0. G, gvFlow rate of fan coil for operation in v-regime, GratedAs a reference flow rate, the flow rate of the gas,
the fan coil flow for room i during time k,
the fan coil flow for room i during time k,
for room i in the k periodThe output heat of the fan coil is used,
the power consumption of the fan coil in the k time period for the room i,
the total output heat of the fan coil in the whole building in the k period.
20. The thermal balance constraint and the cold balance constraint of the system are as follows:
wherein, Vc,heatingAmount of hot water for thermal comfort needs of a building in the k period, Vc,coolingTo meet the amount of cold water required for thermal comfort of a building during the time period k,
andT heatingrespectively hot water flowing into the fan coilThe upper and lower limits of the temperature are set,
andT arespectively an upper limit and a lower limit of the temperature of the hot water flowing into the absorption refrigerator,T ris the lower limit of the temperature of the heat source backwater of the fan coil,T aris the lower limit of the temperature of the heat source backwater of the absorption refrigerator,
is the cold water dehydration temperature of the fan coil,
the return water temperature of the cold water of the fan coil.
The cold/hot demand status flag is set for period k, 1 when there is a hot demand, and 0 otherwise.
Further, for the step of S104, formulating a day-ahead scheduling policy, the method includes the following steps:
s1041, initializing the multiplier λ by setting the iteration number t to 0t；
S1042 using multiplier lambdatSolving the lagrangian relaxation problem:
in the above formula, LtIs an objective function of the t iteration after relaxation, J is an original problem objective function, K is the total time of operation scheduling,
for the purchase price of electricity in k time period, lambdaUPrice for electricity sold for k time period, lambdaBIs the hydrogen valence,
and
is the Lagrange multiplier of the t time, and I is the total number of rooms in the building.
Correction of multiplier gradient direction:
in the above formula, the first and second carbon atoms are,
is a multiplier
The direction of the corresponding correction gradient is changed,
is a multiplier
The corresponding corrected gradient direction.
S1043, selecting step length StThe conditions are satisfied:
In the above formula, phi*Is the optimal solution of the original problem.
Updating Lagrange multiplier:
s1044, checking whether the Lagrange multiplier update meets the precision requirement:
||λt+1-λt||＜ε
in the above equation,. epsilon.represents the accuracy requirement for the solution.
If yes, the step is proceeded to S1045; otherwise, the process proceeds to S1042.
And S1045, constructing an overall feasible solution of the optimization model, wherein the overall feasible solution describes the running state of each device in each time period, namely the day-ahead scheduling strategy, and finishing the algorithm.
Referring to fig. 3, the steps and explanations of the in-day fine tuning phase are as follows:
s201, collecting real-time data: the collected information comprises real-time indoor and outdoor temperature information of each room, the using state of each room, the number of people in each room, personnel distribution information, illumination information, production information and the working state of each device, wherein the working condition of each device comprises the real-time capacity, power and working mode of each device.
S202, generating an actual demand: when the heat and cold loads of a plurality of rooms exist on the demand side, predicting the trajectory and distribution of the personnel by utilizing a personnel trajectory prediction method based on the social long-term and short-term memory artificial neural network according to the data collected in the S201 and the historical personnel trajectory database, and further obtaining the actual heat-cold demand of each room; and generating the actual electricity demand of the current time period according to the historical actual electricity demand and the data collected in the step S201.
S203, generating and updating a fine adjustment strategy table: for any one scheduling strategy, including a day-ahead scheduling strategy and a real-time scheduling strategy, a complete energy circulation path which starts with 'taking in the price from the outside of the system as x element of electricity/hydrogen/light energy' and ends with 'meeting the unit electricity/heat/cold demand' is taken as an action, then x is the unit cost of the action, and then the electricity/heat/cold demand met by the action in the scheduling strategy is the current quantity of the action. As shown in table 1, the fine tuning policy table includes: available action set per period, unit cost per action, current number, expandable upper limit, and contractible lower limit. The expandable upper limit and the contractible lower limit refer to the upper limit and the lower limit which can increase and decrease the action quantity under the condition of not influencing the normal operation of the original scheduling strategy.
TABLE 1
For the formulation of the available action set in the initial fine-tuning strategy table, the existing action in the scheduling strategy before each time interval is selected as the available action, so that the change of the on-off state of the equipment by fine-tuning is avoided. And searching a specific energy circulation path meeting the electricity-heat-cold requirements in the day-ahead scheduling strategy and generating an available action set for each time interval k, and adding a plurality of possible energy circulation paths into the available action set if part of the requirements are met by the energy storage equipment. The fine adjustment strategy table is determined by the running state of the equipment, the parameters of the equipment and the scheduling strategy of the whole time period. And updating the fine-tuning strategy when the fine-tuning strategy is executed and the running condition of the equipment is changed.
S204, fine-tuning the existing scheduling strategy: and comparing the actual demand obtained in the step S202 with demand data applicable to the day-ahead scheduling scheme, and when the actual electricity-heat-cold demand cannot be met by the existing scheduling strategy, finely adjusting the existing scheduling strategy by combining a fine adjustment strategy table. When the demand is increased, expanding the action with the minimum cost in the available actions in the period; when the demand is reduced, the action with the largest cost in the available actions in the period is contracted; when the available action can not meet the actual demand, the current electricity-heat-cold demand is met by supplying electricity to the electric boiler for heat production or supplying electricity to the electric boiler for heat production and supplying the electricity to the absorption refrigerator for refrigeration; and confirming the feasibility of each current device by combining the current running condition of each device, and generating and updating the existing scheduling strategy.
Referring to fig. 4, the policy update phase includes the steps of:
s401, in the day fine adjustment stage, after the fine adjustment strategy table is generated and updated, when the times that the available actions before the time period t0 cannot meet the actual requirements exceed the standard, the scheduling strategy updating operation is triggered, the scheduling strategy is updated in the time period t1 after t minutes, a scheduling scheme updating flag bit is set, and the current initial state set M1 of each device and the latest scheduling strategy F1 in the time period are recorded.
S402, taking each equipment state set M2 after t minutes as an initial state, carrying out a new round of optimization solution on the optimization model, and recording a new scheduling strategy obtained by the solution as F2. M2 is derived from the device state set M1 and the scheduling policy F1.
And in the time periods S403 and t 0-t 1, the fine adjustment of the original strategy is stopped, and the difference between the predicted demand and the actual demand is met by heat generation of a power purchasing and power supplying electric boiler or heat generation of the power purchasing and power supplying electric boiler and refrigeration of an absorption refrigerator, so that the states of all equipment when the new strategy starts scheduling are consistent with the predicted equipment state set M2.
And S404, scheduling by using the updated running scheduling strategy F2 from the time t 1.
Referring to fig. 5, the hydrogen-containing multi-energy system according to the present invention includes: the system comprises a central control system, a building information acquisition subsystem, a fuel cell subsystem, a cold water tank-electric boiler-absorption refrigerator subsystem, an electrolytic bath-compressor-hydrogen storage tank subsystem, a hot water tank subsystem, a photovoltaic power generation subsystem and a solar heat collector subsystem.
The central control system performs centralized management and control on each subsystem and equipment in a serial port instruction sending mode, and is responsible for operation work and instruction generation and sending of the whole system. The central control system is composed of a power supply module, an operation module, a sensor data processing module, a communication module, a serial port module, a clock module and a human-computer interaction module, and information transmission is carried out between the central control system and each subsystem based on a ZigBee protocol. The power supply module is connected with all the modules, the operation module is connected with the sensor data processing module, the communication module, the serial port module, the power supply module and the clock module, the sensor data processing module is connected with the operation module, the communication module, the serial port module, the power supply module and the clock module, the human-computer interaction module is connected with the communication module, the serial port module, the power supply module and the clock module, and the clock module is connected with all the modules. The power supply module is responsible for stably supplying power to the central control system so as to ensure the normal operation of the central control system; the operation module is responsible for calculating a scheduling result and generating a control command, and the operation steps follow S1, S2, S3 and S4 in the supply and demand cooperative operation optimization method of the hydrogen-containing multi-energy system of the building; the sensor data processing module is used for preprocessing and storing the received information of each sensor and sending the information to the operation module; the communication module and the serial port module are responsible for transmitting signals with each subsystem, and the stability and timeliness of the transmitted signals are considered; the clock module provides periodic pulses for the system; the human-computer interaction module is responsible for assisting an operator in managing the system and updating data.
The building information acquisition subsystem consists of sensors arranged inside and outside a building and a sub-node single chip microcomputer, the data monitoring and transmission process is controlled by the CC530 single chip microcomputer, and the related sensors comprise a wall inside and outside temperature sensor, an indoor temperature sensor, an illumination sensor and an infrared sensor.
The connection and information transmission mode among the structures is as follows:
each subsystem monitoring device monitors data through sensors arranged in each device and environment, and is connected with a sensor data processing module in the central controller, and the monitored device data comprises: the water flow of the hot water tank, the temperature of the hot water tank, the water flow of the cold water tank, the temperature of the cold water tank, the power of an absorption refrigerator, the power of an electric boiler, the pressure of a hydrogen storage tank, the power of a fuel cell, the power of a compressor, the power of an electrolytic cell, the power of photovoltaic power generation, the heat generation capacity of a solar heat collector, the power of each water pump, the water temperature and the flow of water at an inlet and an outlet of each heat exchanger and the water temperature and the flow of an inlet and an outlet of each valve; the monitored environmental data comprises: indoor temperature of each room, building inner wall temperature, building outer wall temperature, service conditions of each room, number of indoor personnel, illumination intensity and illumination angle.
The method comprises the steps that an operator stores building structure information, a work schedule, historical weather data, historical actual load information, historical room use states and historical personnel activity conditions in a central controller, the central controller is updated in time through a man-machine interaction module in the central processor, an operation module in the central processor carries out day-to-day prediction requirements according to the information, a day-to-day scheduling strategy is formulated according to prediction results, and control instructions are generated and transmitted to each subsystem controller by combining information monitored by a current monitoring device. Each subsystem controller is connected with an operation module in the central processing unit, receives a control instruction sent by the central processing unit, and controls each device according to the control instruction.
Referring to fig. 6, the fuel cell subsystem is composed and controlled as follows:
the fuel cell subsystem is composed of a fuel cell, a fuel cell monitoring device, a DC/DC converter, a fuel cell controller, a circulating water pump, a three-way valve, a heat exchanger and a radiator. The fuel cell is connected with the DC/DC converter, the three-way valve is respectively connected with a cooling liquid outlet of the fuel cell, the air-cooled radiator and a water inlet of the heat exchanger, and a water outlet of the heat exchanger is connected with an inlet of the hot water tank. When the scheduling instruction shows that the heat of the fuel cell cogeneration needs to be utilized, the cooling liquid flows into the heat exchanger through the three-way valve, exchanges heat with the hot water return of the fan coil and the heat source return of the absorption refrigerator in the heat exchanger, and finally flows into the hot water tank in a hot water mode; when the heat production quantity of the fuel cell exceeds the instruction required value, part of the cooling liquid enters the air-cooled radiator through the three-way valve for heat dissipation so as to ensure that the temperature of the cooling liquid flowing back to the fuel cell is normal. The fuel cell monitoring device is connected with a fuel cell controller, an ammeter, a voltmeter, a temperature sensor of a cooling liquid inlet and outlet, a flow meter of the cooling liquid inlet and outlet, a temperature sensor of a heat exchanger water inlet and outlet, a flow meter of the heat exchanger water inlet and outlet and an A/D conversion module. The fuel cell controller is connected with the central control system, the fuel cell monitoring device, the DC/DC converter, the heat exchanger, the three-way valve and the circulating water pump.
The output voltage of the fuel cell has large fluctuation and slow dynamic response, so a DC/DC converter is required to convert the output voltage into a stable power supply of 48V; the fuel cell controller combines the target operation state of the fuel cell sent by the central control system and the output voltage of the fuel cell monitored by the fuel cell monitoring device to carry out PID control on the DC/DC converter so as to control the output power of the fuel cell; and controlling the on-off state of the heat exchanger, the on-off state of the three-way valve and the power of the circulating water pump in the subsystem according to the running state of the heat exchanger corresponding to the fuel cell, which is sent by the central control system, so as to control the flow direction of the cooling liquid of the fuel cell and the temperature of the hot water flowing to the hot water tank.
Referring to fig. 7, the solar collector subsystem is composed and controlled as follows:
the solar heat collector subsystem consists of a solar heat collector, a solar heat collector controller, a solar heat collector monitoring device, a heat exchanger and a circulating water pump. The solar heat collector monitoring device is connected with a solar heat collector controller, a temperature sensor of a heat exchanger water inlet and outlet, a flow sensor of the heat exchanger water inlet and outlet and an illumination sensor. The solar heat collector controller is connected with the central control system, the circulating water pump and the heat exchanger. The solar heat collector controller controls the power of the circulating water pump and the on-off state of the heat exchanger according to a control instruction sent by the central control system, so that whether heat exchange is carried out or not and the temperature of hot water flowing to the hot water tank are controlled.
Referring to fig. 8, the composition and control method of the cold water tank-electric boiler-absorption chiller subsystem is as follows:
the cold water tank-electric boiler-absorption refrigerator subsystem is composed of a cold water tank, an electric boiler, an absorption refrigerator, a three-way valve, a cold water tank-electric boiler-absorption refrigerator monitoring device and a cold water tank-electric boiler-absorption refrigerator controller. Wherein, the water inlet of the cold water tank is connected with the absorption refrigerator subsystem through the heat exchanger, and the water outlet of the cold water tank is connected with a user cold demand terminal. The three-way valve is respectively connected with the water outlet of the electric boiler, the water inlet of the absorption refrigerator and the heat demand terminal, and the flow direction of hot water flowing out of the electric boiler is controlled by controlling the on-off state of the three-way valve. Energy utilization equipment of the heat demand terminal and the cold demand terminal are air-conditioning fan coil pipes, and required heat and cold are directly provided in the form of water. The cold water tank-electric boiler-absorption refrigerator monitoring device is connected with a cold water tank-electric boiler-absorption refrigerator controller, a cooling water inlet temperature sensor, a cooling water outlet temperature sensor, a heat source water inlet temperature sensor, a heat source water flowmeter, a solution pump flowmeter, a thermometer and a flowmeter at an electric boiler water outlet, and the cold water tank-electric boiler-absorption refrigerator controller is connected with a central control system, the cold water tank-electric boiler-absorption refrigerator monitoring device, an electric boiler, a hot water pump flowing into an absorption refrigerator generator and a heat exchanger pump. The cold water tank-electric boiler-absorption type refrigerating machine controller is controlled by combining a control instruction sent by the central control system, wherein the control instruction comprises an electric boiler working state, an absorption type refrigerating machine working state, a cold water tank working state, a three-way valve switching state, electric boiler working power, absorption type refrigerating machine working power, circulating water pump working power and an absorption type refrigerating machine switching state, and further controls the water inlet temperature of the cold water tank and the hot water/cold water temperature flowing to a hot/cold demand terminal.
The water inlet of the hot water tank is respectively connected with the electrolytic cell-compressor-hydrogen storage tank subsystem, the solar heat collector subsystem and the fuel cell subsystem through the heat exchanger, and the water outlet of the hot water tank is connected with the cold water tank-electric boiler-absorption refrigerator subsystem.
Referring to fig. 9, the composition and control method of the electrolyzer-compressor-hydrogen storage tank subsystem is as follows:
the electrolytic cell-compressor-hydrogen storage tank subsystem: the device consists of an electrolytic cell, a compressor, a hydrogen storage tank, an AC/DC converter, a heat exchanger, a circulating water pump, a radiator, a three-way valve, an electrolytic cell-compressor-hydrogen storage tank monitoring device and an electrolytic cell-compressor-hydrogen storage tank controller. The electrolytic cell is connected with the AC/DC converter and the compressor, the compressor is connected with the hydrogen storage tank and the electrolytic cell, the hydrogen storage tank is connected with the compressor and the fuel cell in the fuel cell subsystem, the circulating water pump is connected with the electrolytic cell and the hot water tank in the water tank-electric boiler-absorption refrigerator subsystem, and the three-way valve is respectively connected with the electrolytic cell cooling liquid outlet, the air-cooled radiator and the heat exchanger inlet end. The electrolytic bath-compressor-hydrogen storage tank monitoring device is connected with an electrolytic bath-compressor-hydrogen storage tank controller, pressure sensors, temperature sensors, flow sensors and gas purity sensors in all devices, and a voltmeter, an ammeter and a wattmeter of the electrolytic bath and the compressor. The electrolytic cell-compressor-hydrogen storage tank controller is connected with the central controller, the electrolytic cell-compressor-hydrogen storage tank monitoring device, the electrolytic cell, the motor in the compressor and the hydrogen pump. The heat generated in the electrolytic process of the electrolytic tank is taken out through the cooling water and is transferred to the water in the hot water tank through the heat exchanger, and when the heat is not transferred through the heat exchanger, the cooling liquid flows into the radiator to dissipate the heat in order to ensure the normal operation of the electrolytic tank. The power supply used for the electrolysis of water is a direct current power supply, so that an AC/DC converter is used for conversion. The electrolytic cell-compressor-hydrogen storage tank controller controls the electrolytic current of the electrolytic cell according to the working state of the electrolytic cell sent by the central controller, so as to control the hydrogen production; the quality of hydrogen flowing into the hydrogen storage tank is controlled by controlling the rotating speed of a motor of the compressor and the delivery quantity of compressed gas of the hydrogen pump by combining the real-time hydrogen pressure in the hydrogen storage tank with the working state of the compressor sent by the central controller; and controlling the power of a circulating water pump, the on-off state of the heat exchanger and the on-off state of a three-way valve in the subsystem according to the working state of the heat exchanger sent by the central controller, and further controlling the flow direction of the cooling water of the electrolytic bath and the temperature of the hot water flowing into the hot water tank.
Referring to fig. 10, the photovoltaic power generation subsystem is composed and controlled as follows:
the photovoltaic power generation subsystem: the photovoltaic power generation monitoring device comprises a photovoltaic power generation board, a DC/DC converter, a photovoltaic power generation monitoring device and a photovoltaic power generation controller. The photovoltaic power generation panel is connected with the unidirectional DC/DC converter. The photovoltaic power generation monitoring device consists of current and voltage acquisition equipment, temperature detection equipment, wind speed detection equipment and illumination intensity detection equipment, and is connected with the photovoltaic power generation controller. The photovoltaic power generation controller is connected with the central control system, the unidirectional DC/DC converter and the photovoltaic power generation monitoring device, when the power generation power of the photovoltaic panel in a control instruction sent by the central control system is larger than the maximum power generation power of the photovoltaic panel, the unidirectional DC/DC converter is controlled to work in a maximum power point tracking mode, and when the power generation amount of the photovoltaic panel is too large and the photovoltaic panel needs to abandon the power, the unidirectional DC/DC converter is controlled to work in a constant voltage mode. The power generation amount of the photovoltaic power generation panel is related to illumination and temperature and is a nonlinear power supply, so that a unidirectional DC/DC converter is used for converting 150-350V direct current into stable 48V user side required direct current.
Claims (10)
1. The method for optimizing supply and demand collaborative operation of the hydrogen-containing multi-energy system of the building is characterized by comprising the following steps of:
s1, a day-ahead scheduling stage: generating predicted weather data and a demand curve according to the known building structure information, the work schedule, the historical weather data and the actual historical load information, and generating a day-ahead scheduling strategy according to the predicted weather data and the demand curve; the stage comprises four parts, namely, collecting day-ahead information, predicting the day-ahead demand, establishing an optimization model and formulating a day-ahead scheduling strategy;
s2, a day fine adjustment stage: generating an initial fine-tuning strategy table according to the scheduling strategy and the equipment information by taking the day-ahead scheduling strategy generated by S1 or the scheduling strategy updated by S4 as an initial existing scheduling strategy; obtaining actual electricity-heat-cold demand data according to the real-time running state of equipment in a hydrogen-containing multi-energy system of a building and the use condition of each room; fine tuning and updating the existing scheduling strategy by combining the real demand and the fine tuning strategy table so as to meet the actual electricity-heat-cold demand; updating the fine tuning strategy table;
s3, a daily control stage: according to the existing scheduling strategy obtained in S2, the cooling capacity and the heating capacity of each room are specified, and the target operation state of each device in a future time period is obtained;
s4, strategy updating stage: if the fine-tuning of the existing scheduling policy in S2 cannot meet the real demand, the historical load information, the usage status of each room, and the personnel activity condition are updated, demand prediction is performed on the updated historical load information, the usage status of each room, and the personnel activity condition, an updated scheduling policy is generated, and S2-S3 are performed.
2. The method for optimizing supply and demand collaborative operation of the building hydrogen-containing multi-energy system according to claim 1, wherein the step S1 includes the steps of:
s101, collecting building structure information, a work schedule, historical weather data and actual historical load information to generate a predicted demand curve;
s102, forecasting the electricity-heat-cold demand according to the information collected in the S101 by combining the influence of the personnel activities on the electricity-heat-cold demand to obtain an initial electricity-heat-cold demand; wherein the hot-cold demand is predicted by the predicted weather information, the personnel schedule, the hot comfortable area and the building structure information;
s103, establishing an optimization model according to the building structure information and equipment parameters in the system, wherein the optimization model aims to minimize the total building energy consumption on the premise of meeting the building electricity-heat-cold requirements and the personnel comfort, and takes an electric balance, a heat balance, a cold balance, a hydrogen balance equation, an equipment output equation and a heat comfortable area of each room as constraints;
and S104, solving according to the initial electricity-heat-cold requirements obtained in the S102 and the optimization model established in the S103 to obtain a day-ahead scheduling strategy.
3. The method for optimizing supply and demand collaborative operation of the hydrogen-containing multi-energy system of the building according to claim 2, wherein in the step S103, the electrical balance equation is as follows:
wherein,
for the generated power of the fuel cell for the k period,
the generated power of the solar panel in the k period,
for the electric power purchased by the k-period system from the grid,
feeding back the power of the power grid for the k-period system,
for the electric power of the electrolytic cell in the period k,
for the electric power of the electric boiler for the period k,
for the electric power of the compressor for the period k,
for the electric power of the water pump in the k time period,
for the amount of electricity required for the building during the k time period, τ is the duration of one time period, βpvIs the photoelectric conversion coefficient of the solar panel, rkIntensity of solar radiation in the k period, SpvMaximum area of the solar panel that can receive light, PtfThe upper bound constraint of buying and selling power to the power grid in a single time period;
the power grid electricity buying flag bit is in k time period, when the system buys electricity from the power grid, the flag bit is 1, otherwise, the flag bit is 0;
the power grid electricity selling zone bit in the k time period is 1 when the system sells electricity to the power grid, otherwise, the power grid electricity selling zone bit isIs 0.
4. The method for optimizing supply and demand collaborative operation of a building hydrogen-containing multi-energy system according to claim 2, wherein in the step S103, the fuel cell is constrained as follows:
wherein,
the mass of hydrogen consumed by the fuel cell for the k period,U fcminimum pressure of input hydrogen allowed for fuel cell, R is molar gas constant, telIs the working temperature of the electrolytic bath,
the electricity generation coefficient of the fuel cell in the k period;
is the fuel cell heat generation coefficient for the period k,
andP fcfor the upper and lower limits of the output power of the fuel cell, VcThe flow rate of water which participates in the heat exchange of the fuel cell heat exchanger and flows into the hot water tank in the period tau,
for a period k of time the temperature of the water flowing to the hot water tank after heat exchange with the fuel cell coolant in the heat exchanger,
the water temperature before heat exchange is participated in k time period, the part of water participated in heat exchange is composed of absorption type refrigerator backwater and fan coil pipe heat source backwater, tfcThe temperature at which the fuel cell coolant flows into the heat exchanger;
an "electrolyzer-fuel cell" flow flag for a period k, which is 1 when the flow channel is in use, and 0 otherwise;
the fuel cell use flag bit is a k period, and is 1 when the fuel cell is used, otherwise, the flag bit is 0;
the fuel cell heat exchanger for period k uses a flag bit that is 1 when the fuel cell heat exchanger is in use and 0 otherwise.
5. The method for optimizing supply and demand collaborative operation of the hydrogen-containing multi-energy system of the building according to claim 2, wherein in the step S103, the electrolytic cell is constrained as follows:
wherein,
quality of hydrogen, beta, produced for electrolysis in an electrolysis cell during a period of kelThe conversion coefficient of the electrolysis bath for electrically converting hydrogen,
is the upper limit of the power of the electrolytic cell,
is the heat generated by the cell during the k period, thetaelIn order to obtain the heat generation coefficient of the electrolytic cell,
pressure of hydrogen production, alpha, for the electrolyzer during the period kelFor the conversion coefficient of the hydrogen mass and the pressure in the electrolytic cell,
and UelRespectively the upper pressure limit and the lower pressure limit of the hydrogen produced by the electrolytic cell,
for the mass of hydrogen gas produced by the electrolytic cell and flowing into the hydrogen storage tank after being compressed by the compressor during the period k,
mass of hydrogen produced by the electrolyzer and flowing into the fuel cell for cogeneration during a period k, c is the specific heat capacity of water, ρwtIs the density of water, Vel,exThe flow rate of the water which participates in the heat exchange of the heat exchanger of the electrolytic cell and flows into the hot water tank in the period tau,
for the temperature of the water flowing into the hot water tank after exchanging heat with the electrolytic bath cooling liquid in the heat exchanger during the period k,
temperature of water flowing into the heat exchanger for the hot water tank for the period k, telFor the temperature of the bath coolant flowing into the heat exchanger, thetaexHeat exchange efficiency of the heat exchanger;
the use flag bit of the electrolytic cell in the k time period is 1 when the electrolytic cell is used, otherwise, the use flag bit is 0;
the use flag bit of the heat exchanger of the electrolytic cell in the k time period is 1 when the heat exchanger of the electrolytic cell is used, otherwise, the use flag bit is 0; epsilon+A positive infinitesimal amount; m is a positive maximum.
6. The optimization method for supply and demand collaborative operation of the building hydrogen-containing multi-energy system according to claim 2, wherein in the step S104, in solving the optimization model, the optimization model coupled with n rooms is decoupled into n models, the n rooms are considered as n nodes having mutual circulation, in solving, specific constraints in a single room are not considered, only energy circulation balance constraints among the n room nodes are considered, and sub-problems in each room are solved respectively under the condition that the constraints among the nodes are satisfied.
7. The method for optimizing supply and demand collaborative operation of the building hydrogen-containing multi-energy system according to claim 1, wherein the step S2 includes the steps of:
s201, collecting real-time data: the real-time data comprises indoor temperature and outdoor temperature information of each room, the using state of each room, the number of people in each room, personnel distribution information, illumination information, production information and the working state of each device, wherein the working condition of each device comprises the capacity, power and working mode of each device;
s202, generating an actual demand: when the heat and cold loads of a plurality of rooms exist on the demand side, predicting the personnel track and distribution according to the data collected in the S201 and the historical personnel track database to obtain the actual heat and cold demands of each room; generating an actual electricity demand of the current time period according to the historical electricity demand and the data collected in S201;
s203, generating and updating a fine tuning strategy table, wherein the fine tuning strategy table comprises: available action sets for each time period, unit cost for each action, current number, expandable upper limit, and contractible lower limit;
s204, fine-tuning the existing scheduling strategy: and comparing the actual demand obtained in the step S202 with demand data applicable to the day-ahead scheduling scheme, and when the actual electricity-heat-cold demand cannot be met by the existing scheduling strategy, finely adjusting the existing scheduling strategy by combining a fine adjustment strategy table.
8. The method for optimizing supply and demand collaborative operation of a building hydrogen-containing multi-energy system according to claim 7, wherein in the step S204, when demand increases, the action with the lowest cost in the available actions in the period is expanded; when the demand is reduced, the action with the largest cost in the available actions in the period is contracted; when the action concentrated by the available action can not meet the actual demand, the current electricity-heat-cold demand is met by buying electricity, supplying the electricity to an electric boiler to generate heat or supplying the electricity to the electric boiler to generate heat and supplying the heat to an absorption refrigerator to refrigerate; and confirming the feasibility of each current device by combining the current running condition of each device, and generating and updating the existing scheduling strategy.
9. The method for optimizing supply and demand collaborative operation of the building hydrogen-containing multi-energy system according to claim 1, wherein the step S4 includes the steps of:
s401, in a day fine adjustment stage, when the times that actions in an available action set cannot meet actual requirements before a time period t0 exceed the standard, triggering scheduling strategy updating operation, updating the scheduling strategy at a time period t1 after t minutes, setting a scheduling strategy updating flag bit, and recording current initial state sets M1 of each device and the latest scheduling strategy F1 at the time period;
s402, taking each equipment state set M2 after t minutes as an initial state, carrying out a new round of optimization solution on the optimization model, and obtaining a new scheduling strategy F2;
in the time period of S403 and t 0-t 1, the fine adjustment of the original strategy is stopped, and the difference between the predicted demand and the actual demand is met by heat production of a power purchasing and power supplying electric boiler or heat production of the power purchasing and power supplying electric boiler and refrigeration of an absorption refrigerator, so that the states of all equipment when the new strategy starts scheduling are consistent with the predicted equipment state set M2;
and S404, scheduling by using the updated running scheduling strategy F2 from the time t 1.
10. The hydrogen-containing multi-energy system supply and demand collaborative operation optimization control system for the building is characterized by comprising a central control system and an information acquisition subsystem;
the central control system performs centralized management and control on each subsystem and equipment in a serial port instruction sending mode, and is responsible for operation work of the whole system and instruction generation and sending;
the central control system comprises an operation module, a sensor data processing module, a communication module, a serial port module and a human-computer interaction module;
the operation module is connected with the sensor data processing module, the communication module and the serial port module, the sensor data processing module is connected with the operation module, the communication module and the serial port module, and the human-computer interaction module is connected with the communication module and the serial port module;
the operation module is used for calculating a scheduling result and generating a control command, and the operation steps of the operation module follow S1, S2, S3 and S4 in the supply and demand cooperative operation optimization method of the hydrogen-containing multi-energy system of the building; the sensor data processing module is used for preprocessing and storing the received information acquired by the information acquisition subsystem and sending the information to the operation module; the communication module and the serial port module are responsible for transmitting signals with each subsystem; the human-computer interaction module is used for assisting an operator in managing the system and updating data;
the building information acquisition subsystem consists of sensors arranged inside and outside a building and a sub-node single chip microcomputer, wherein the sensors comprise wall inside and outside temperature sensors, indoor temperature sensors, illumination sensors and infrared sensors.
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| CN117674375A (en) * | 2023-11-15 | 2024-03-08 | 燕山大学 | New energy multi-energy complementary hydrogen production system energy management method |
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| CN115079564A (en) * | 2022-07-21 | 2022-09-20 | 清华四川能源互联网研究院 | Decarburization path planning optimization method for regional hydrogen generation system |
| CN117674375A (en) * | 2023-11-15 | 2024-03-08 | 燕山大学 | New energy multi-energy complementary hydrogen production system energy management method |
| CN117674375B (en) * | 2023-11-15 | 2024-06-07 | 燕山大学 | New energy multi-energy complementary hydrogen production system energy management method |
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