WO2022124447A1

WO2022124447A1 - Smart grid monitoring method for heat-pump cooling and heating system using building-integrated photovoltaic and thermal hybrid

Info

Publication number: WO2022124447A1
Application number: PCT/KR2020/018074
Authority: WO
Inventors: 김국정; 하태진
Original assignee: (주)비온시이노베이터
Priority date: 2020-12-09
Filing date: 2020-12-10
Publication date: 2022-06-16

Abstract

The present invention relates to a smart grid monitoring method for a heat-pump cooling and heating system using a building-integrated photovoltaic and thermal hybrid, the method being capable of effectively managing electricity supply in a building by estimating the amount of power generation by the photovoltaic and thermal hybrid that produces heat source and electricity required for the building and analyzing electrical and thermal load demand patterns within the building.

Description

Smart grid monitoring method for heat pump heating/cooling system using building-integrated solar power complex

The present invention relates to a smart grid monitoring method for a heat pump heating/cooling system using a building-integrated photovoltaic complex, and more particularly, predicts the amount of power generation of a solar thermal complex that produces a heat source and electricity required for a building, and the electricity and heat load in the building It relates to a smart grid monitoring method for a heat pump heating and cooling system using a building-integrated solar thermal composite that can effectively manage the electricity supply in a building by analyzing demand patterns.

In particular, the present invention intends to improve the amount of power generation by installing a solar cell module cleaning system corresponding to a solar thermal complex.

To clean the solar cell array installed in the entire solar cell array power plant if there is a difference of more than a reference value in the amount of power generated by the first solar cell module equipped with the cleaning system compared to the amount of power generated by the second solar cell module without the cleaning system device A monitoring system is provided.

In recent years, the use of solar power generation facilities capable of generating electric power using solar energy has become increasingly common.

Solar cells using such solar energy do not use fossil fuels such as coal or petroleum, but are in the spotlight as a new alternative energy source in the future because they use sunlight or solar heat, which are pollution-free and infinite energy sources.

These solar cells are currently being used to obtain electricity generated by solar power plants, buildings, automobiles, and the like.

In general, photovoltaic power generation is a technology for directly producing electricity using a solar cell (PV: Photovoltaic). The solar cell is a semiconductor device that converts light energy into electrical energy using the photoelectric effect, and is composed of two semiconductor thin films each having positive (+) and negative (-) polarity. In addition, a plurality of solar cells (cells) are connected in series/parallel to generate the voltage and current required by the user, and the user can use the power generated by the solar cell.

Although photovoltaic power generation has various application fields, among them, interest in building integrated photovoltaic (BIPV) technology, which uses solar cells (PV) as a building envelope finishing material, is on the rise.

Recently, registration patent No. 10-1687700, which uses a photovoltaic-thermal technology that produces heat and electricity at the same time to minimize the reduction in efficiency due to the temperature rise of BIPV (Building Integrated Photovoltaic), etc. is being developed

The registration patent No. 10-1687700 (Registration date: December 13, 2016) is provided with an outer frame embedded in the outer wall surface of a building so that it can be mounted integrally and forming an air gap space in which a fluid can move therein; , a photovoltaic module installed on the upper part of the outer frame and receiving sunlight to generate electric energy while transferring heat to the air gap space to collect heat and a lower baffle sheet positioned to protrude toward the air gap space and in contact with the fluid but having a plurality of baffle members capable of guiding a curved movement path of the fluid, the baffle member being introduced into the air gap space and arranging in a zigzag form spaced apart from each other in the flow direction of the fluid so that the fluid can flow along the movement path of the wave, wherein the baffle member is contactable in response to the movement direction of the fluid toward the air gap space The inclined guide surface of the baffle member is curved upward and inclined at an acute angle from the lower baffle sheet. A first inclined surface forming, a second inclined surface formed on the opposite side of the first inclined surface and forming an inclination angle of 55 to 65° with respect to the lower baffle sheet, and a gentle curved surface between the first inclined surface and the second inclined surface It is a modular type air-type building-integrated solar thermal system that includes a curved surface that forms and induces a fluid.

The technology using Building Integrated Photovoltaic-Thermal (BIPVT) as in the above registered patent has a simple manufacturing process and uses air as a heating medium unlike conventional solar collectors. It does not occur and can be used easily.

In addition, the heat pump air conditioning system maintains the indoor temperature lower than the outdoor temperature in summer and maintains the indoor temperature higher than the outdoor temperature in winter to adjust the indoor temperature to a comfortable state for activity. In other words, such a heat pump is a technology and heating/cooling system that uses Freon refrigerant to raise the heat from a low temperature place to a high place. Gas engine heat pump).

By using such a heat pump heating/cooling system in conjunction with the Building Integrated Photovoltaic Thermal (BIPVT) technology, it is possible to increase the heating COP and overall energy efficiency of the heat pump.

The smart grid is a next-generation power grid that improves energy efficiency by combining information and communication technology (ICT) with the existing power grid to enable two-way communication of power generation and consumption information in real time. In addition, the smart grid utilizes electricity and information and communication technology to intelligently and sophisticate the power grid to provide high-quality power service and maximize energy use efficiency.

Accordingly, there is a need for a technology for a smart grid capable of monitoring and managing a heat pump heating/cooling system using the building-integrated solar power complex.

The present invention has been proposed to solve the above-mentioned problems, and predicts the amount of power generation of a photovoltaic complex that produces a heat source and electricity required for a building according to temperature and weather by date and time, and analyzes electricity and heat load demand patterns in the building An object of the present invention is to provide a smart grid monitoring method for a heat pump heating and cooling system using a building-integrated solar power complex.

In addition, it is an object to provide a smart grid monitoring method for a heat pump heating and cooling system using a building-integrated solar thermal composite that can maximize self-consumption in a building and suppress the load by monitoring the heat pump heating and cooling system using the solar thermal composite. .

The smart grid monitoring method for a heat pump heating and cooling system using a building-integrated solar power complex according to the present invention for achieving the above object is to select a power generation forecast day including weather and temperature for each time period that a user wants to predict the power generation amount step, collecting the existing generation amount data stored one week before the generation amount prediction date from among the existing generation amount data including the weather and temperature for each date and time period stored in advance before the generation amount prediction date, each of the collected existing generation amount data Filtering the existing power generation data belonging to a weather region similar to the weather included in the power generation forecasting date, and extracting the maximum power generation data and the average power generation data from the filtered existing power generation data, and then excluding the generation amount data with an error rate of 10% or more and comparing the weather for each time period based on the average power generation data and extracting existing power generation data of weather that is close to the weather included in the power generation forecasting date, and the extracted existing power generation data based on the average power generation data. Normalizing, estimating the generation amount by time period using the normalized existing generation amount data, selecting a demand forecast date including weather and temperature for each time period for which the user wants to predict the demand amount, the demand forecast date classifying into weekdays and weekends; and, when the demand forecasting date is a weekday, from among the existing demand data including the weather and temperature for each time and date stored in advance before the demand forecasting date, the existing data stored one week before the demand forecasting date Collecting demand data, and when the demand forecasting date is a weekend, from among the existing demand data including the weather and temperature by date and time period stored in advance before the demand forecasting date, the existing data stored three weeks before the demand forecasting date collecting demand data; filtering existing demand data belonging to a weather region similar to the weather included in the demand forecast day from among the collected existing demand data; Extracting the maximum demand data and the average demand data from the quantity data and then excluding the demand data with an error rate of 10% or more, and comparing the weather for each time period based on the average demand data to determine the approximate weather with the weather included in the demand forecast day extracting the existing demand data; normalizing the extracted existing demand data based on the average demand data; and predicting the demand for each time period using the normalized existing demand data.

As described above, according to the present invention, it is possible to predict the amount of power generation of the solar power complex that produces the heat source and electricity required for a building according to the temperature and weather by date and time, and analyze the electricity and heat load demand pattern in the building. It is possible to effectively manage the electricity supply, which has the effect of greatly improving the convenience of management.

In addition, by monitoring the heat pump heating/cooling system using the solar power complex, it is possible to maximize the self-consumption in the building and suppress the load, thereby providing flexibility to the power system, thereby improving the quality of the product.

1 is a flowchart for predicting power generation of a smart grid monitoring method for a heat pump heating/cooling system using a building-integrated solar power complex according to an embodiment of the present invention;

2 is a flowchart for analyzing electricity and heat load demand patterns of a smart grid monitoring method for a heat pump heating/cooling system using a building-integrated solar power complex according to an embodiment of the present invention;

3 is a power generation prediction screen of a smart grid monitoring method for a heat pump heating/cooling system using a building-integrated solar power complex according to an embodiment of the present invention;

4 is a demand forecast screen of a smart grid monitoring method for a heat pump heating/cooling system using a building-integrated solar power complex according to an embodiment of the present invention.

Hereinafter, a smart grid monitoring method for a heat pump heating/cooling system using a building-integrated solar thermal composite according to the present invention will be described in detail with reference to the accompanying drawings.

1 is a flow chart for predicting power generation of a smart grid monitoring method for a heat pump heating/cooling system using a building-integrated solar power complex according to an embodiment of the present invention, and FIG. 2 is a building-integrated solar system according to an embodiment of the present invention. It is a flowchart for the analysis of electricity and heat load demand pattern of a smart grid monitoring method for a heat pump heating/cooling system using an optical thermal composite, and FIG. 3 is a heat pump heating and cooling system using a building-integrated solar power composite according to an embodiment of the present invention is a power generation forecasting screen of a smart grid monitoring method for

In adding reference numerals to the components of the drawings, only the same components are to have the same reference numerals as possible even if they are displayed on different drawings, and a known function determined to unnecessarily obscure the gist of the present invention. and detailed description of the configuration will be omitted. Also, directional terms such as 'top', 'bottom', 'front', 'back', 'lead', 'front', 'rear', etc. are used in connection with the orientation of the disclosed figure(s). Since components of embodiments of the present invention may be positioned in various orientations, the directional terminology is used for purposes of illustration and not limitation.

A smart grid monitoring method for a heat pump heating/cooling system using a building-integrated solar power complex according to a preferred embodiment of the present invention, as shown in Figs. choose a job

It is preferable to include the weather and temperature for each time period in the power generation forecasting date, and the power generation forecasting date may be classified by day of the week.

Existing generation amount data stored one week before the generation amount prediction date from among the existing generation amount data before the generation amount prediction date is collected.

In this case, the existing power generation data should be stored in the weather, temperature, and demand by date and time.

In addition, when there is no existing generation amount data one week before the generation amount prediction date among the existing generation amount data before the generation amount prediction date, it is preferable to collect the existing generation amount data one week before the generation amount prediction date one year before.

Existing power generation data belonging to a weather region similar to the weather included in the power generation forecasting date, respectively, from among the collected existing power generation data is filtered.

As for the weather, information on rain, snow, cloud, sun, wind, sunny, cloudy is stored, and it is preferable to predetermine similar weather such as rain and snow as a group in advance.

In this case, it is possible to further filter the existing generation amount data that falls within a certain range of the temperature included in the generation amount prediction date from among the collected existing generation amount data.

The maximum generation amount data and the average generation amount data are extracted from the filtered existing generation amount data to derive an error rate between the maximum generation amount data and the average generation amount data. Thereafter, the generated data with an error rate of 10% or more is excluded.

Based on the average power generation data, the weather for each time period is compared to extract the existing power generation data of the weather included in the power generation forecast date and approximate weather. That is, by extracting the existing power generation data of the same weather as the weather included in the power generation forecasting date, the reliability of the existing power generation data can be improved.

The extracted existing power generation data is normalized based on the average power generation data.

By normalizing the extracted existing generation amount data, it is possible to make the existing generation amount data easy to use by transforming it according to a certain rule.

The amount of power generation for each time period is predicted using the normalized existing power generation data. It is possible to predict the amount of power generation for the power generation prediction date.

Select a demand forecast date including weather and temperature for each time period that the user wants to forecast demand.

The demand forecast days are classified into weekdays and weekends. In other words, the demand forecast day is first classified as a day of the week and then classified into weekdays and weekends.

In this case, if the demand forecast day is a public day, it is preferable to classify it as a weekend.

When the demand forecast date is a weekday, the existing demand data stored one week before the demand forecast date is collected from among the existing demand data including the weather and temperature for each time and date previously stored before the demand forecast date.

Here, when there is no existing demand data before the demand forecast date, the existing demand data that is one week before the demand forecast date of one year before is collected.

In addition, when the demand forecast date is a weekday, it is preferable to collect the existing demand data that is a weekday before the demand forecast date.

When the demand forecasting date is a weekend, existing demand data stored three weeks before the demand forecasting day is collected from among the existing demand data including the weather and temperature for each time and date stored in advance before the demand forecasting date.

Here, when there is no existing demand data before the demand forecast date, the existing demand data from three weeks to one week before the demand forecast date of one year before must be collected.

In this case, when the demand forecast date is a weekend, it is preferable to collect existing demand data that is a weekend before the demand forecast day from three weeks before the demand forecast date.

Existing demand data belonging to a weather region similar to the weather included in the demand forecast date, respectively, from among the collected existing demand data is filtered.

After extracting the maximum demand data and the average demand data from the filtered existing demand data, the demand data with an error rate of 10% or more is excluded.

The error rate may be derived using the maximum demand data and the average demand data.

Based on the average demand data, the weather for each time period is compared to extract the existing demand data of the weather included in the demand forecast date and approximate weather.

In other words, by comparing the weather for each time period based on the average demand data, the existing demand data of the same weather as the weather included in the demand forecasting day is extracted.

Based on the average demand data, the extracted existing demand data is normalized so that it can be modified and used according to a certain rule.

A demand amount for each time period is predicted using the normalized existing demand amount data.

It is possible to monitor the predicted power generation amount and demand amount. At this time, the predicted power generation and demand may be converted into a table or graph for each time period and displayed as shown in FIGS. 3 to 4, and may be provided by predicting the power generation and demand by receiving weather information from the Meteorological Administration. .

Through the smart grid monitoring method for the heat pump heating and cooling system using the building-integrated solar power complex as described above, the amount of power generation of the photovoltaic complex that produces the heat source and electricity required for the building is predicted according to the temperature and weather by date and time, and the building It is possible to analyze the electricity and heat load demand patterns in the building, so it is possible to effectively manage the electricity supply in the building, which has the effect of greatly improving the convenience of management.

In addition, by monitoring the heat pump heating/cooling system using the solar power complex, it is possible to maximize the self-consumption in the building and suppress the load, thereby providing flexibility to the power system and improving the quality of the product.

Next, look at the configuration corresponding to the claim.

a current sensor unit 310 installed for each cable 301 introduced from the solar power plant connection panel 200 to the inverter 300; a communication unit 320 for collecting and transmitting current data measured by the current sensor unit 310; and

The current data is classified according to the connection panel 200 that is introduced for each inverter 300, and the current data of the connection panel 200 for each inverter is compared with each other to form a solar cell array 100, a connection panel 200, and an inverter. In the photovoltaic system monitoring device comprising a monitoring unit 400 for determining whether at least any one of (300) is abnormal,

The solar cell array 100 outputs a DC current and provides it to the connection panel 200, but a first solar cell in which the upper plane of the first solar cell module 100a is periodically cleaned in a position close to the solar cell array 100 The amount of power generated by the module (100a) is installed in the vicinity of the solar cell array (100),

It includes a monitoring unit for cleaning the entire plane of the solar cell array 100 when there is a difference of more than a reference value compared to the amount of power generated by the second solar cell module 100b that is not to clean the upper plane of the second solar cell module 100b.

A driving motor 500 is installed on one side of the first solar cell module 100a.

A water tank 530 that temporarily receives rainwater on one side of the first solar cell module 100a, and the water tank 530 When the driving motor 500 is driven through the control unit according to the on/off signal of the buoyancy level sensor 540a and the buoyancy level sensor 540 with a buoy 540 rising and falling by the buoyancy therein, the driving unit ( The brush connected to the 510 is moved to clean the plane of the first solar cell panel 100a.

As shown in the present invention, a brush mounted on the first solar cell panel 100a on one side of the solar cell module 100 corresponding to the solar cell array 100 in which a plurality of solar cell modules 100 are configured as an array. It can be cleaned by mounting a brush as shown in 520 .

In an embodiment of the above configuration, when water flows into the water tank 530, the buoy 540 rises along with the water level, and the switch of the buoyancy water level sensor 540a is turned on to control the power produced by the solar module (not shown) Power is supplied to the driving motor 500 through the

Then, the driving motor 500 operates the brush 520 to clean the solar stop module.

The buoy 540 rises only when the water level rises to turn on the switch 540b, and when there is no water level, the buoy descends and the switch 540b is turned off.

Therefore, when the water level continues, the driving motor 500 continuously operates, so to stop it, a timer (not shown) is configured in the control unit to supply power to the driving motor 500 for a certain period of time and then cut off the power.

In an embodiment, the cleaning of the first solar cell module 100a for data measurement can be compared with the first solar cell module 100a as a comparison target and the second solar cell module 100b as the comparison target even when cleaning only on a rainy day .

That is, not every day, but sometimes quarterly.

The following is an explanation of the cleaning period.

There is no need to fill the water tank 530 with water because it is cleaned on a rainy day of the present invention.

So, in the present invention, when it rains, the small water tank 530 is filled with water, and thereby the water level rises with the buoy 540 to turn on the switch 540b.

Therefore, when the power produced by the solar cell module is applied and the driving motor 500 operates, the brush 520 connected to the driving unit 510 moves left and right or up and down to move the first solar cell

The plane of the module 100a is cleaned.

A difference occurs between the cleaned first solar cell module 100a and the uncleaned second solar cell module 100b in power production.

That is, if the amount of power generated by the first solar cell module 100a differs by more than a reference value compared to the amount of power generated by the second solar cell module 100b without the cleaning system device, the solar cell array installed in the entire solar cell array power plant is cleaned. It is monitored and transmitted to the manager.

The water tank 530 includes a sieve 550 for blocking the inflow of foreign substances to the upper side of the water tank 530 so that water flows into the water from the upper side;

a buoy 540 rising by the water level inside the water tank 530;

A switch 540b that is connected to the buoy 540 and operates when the buoy 540 rises to turn on/off the electric temperature, and the driving motor 500 through the control unit according to the on/off signal of the switch 540b. is a working configuration.

The driving motor 500 receives power from the solar cell module 100a.

A drain port 560 is formed at the bottom of the water tank 530, but when the amount of water (including rainwater) flowing into the upper side of the water tank 530 is less than the amount discharged through the drain hole 560, the water level in the water tank 530 rises. It is a structure in which the buoy 540 is raised by the raised water level to do so.

The reason for doing this is to keep the water tank 530 empty when water does not continuously flow into the water tank. If it is not left empty, freezing may occur and foreign substances introduced into the water tank may accumulate.

That is, the water inlet at the upper side of the water tank 530 is large, and the drain port 560 at the lower end of the water tank 530 is smaller than the water inlet.

The reason for operating the buoyancy level sensor 540a by the buoy is to prevent the driving motor from operating when the operating part such as the buoy is frozen in winter.

For example, when a humidity sensor is applied, even if the driving motor 500 and the driving unit 510 are frozen on a snowy day, the driving motor may operate when humidity is detected.

In particular, when heavy snow falls, the brush cannot sweep the snow, and when the drive motor operates, the drive motor overheats.

Then, the driving motor is heated by the power applied to the driving motor 500, which can lead to a fire, and can operate even in moisture caused by dew.

Of course, adding complex functions to the control unit can solve the problem, but it is unrealistic in terms of cost.

As shown in the figure, one side of the brush is connected to the driving unit 510 and when the driving motor rotates, the other side of the brush rotates to clean the first solar cell module. can

The buoyancy level sensor 540a is a switch water level sensor that can be purchased in the market, and is a product configured to be switched on when the buoy rises.

That is, it is one body with the switch.

If you search for 'switch water level sensor' on Google, Daum, Naver, etc., you can easily check it and check its function.

a water channel 610 for forming a rain gutter (which may be a solar cell array) at the lower end of the solar cell array 100 and collecting water flowing down from the rain gutter 600 to be discharged to one side; an operation unit 620 that moves or descends when it is interfered with by rainwater flowing into the waterway 610 on one side of the waterway 610;

a lever device 624 having one side of the lever 621 connected to the operation unit 620, a weight 622 formed on the other side of the lever 621, and a central portion of the lever connected by a hinge 623;

Interfered with the lever device 624 on the upper or lower side of the lever device

a switch 630 that turns on/off the power;

When the switch 630 is turned on/off, it is configured with a driving motor 500 that operates by receiving or blocking power produced by the solar cell module.

In the embodiment of the above configuration, the operation unit 620 descends a certain distance by the water pressure of water flowing down into the water channel 610 . Therefore, as shown, when the lever device 624 rotates about the hinge 623 as a central axis, the weight 622 of the lever device 624 rises to turn on the switch 630 located above the weight. Then, the power of the solar cell array 100 is applied to the driving motor.

However, if there is no rainwater, the moving part returns to its original position. At this time, the switch is turned off.

In this process, as power is applied to the driving motor, the brush cleans the plane of the first solar cell module as described above.

Since the solar cell array 100 is installed at an angle, the solar cell array 100 may serve as a drip tray.

Claims

Selecting a power generation forecasting date including weather and temperature for each time period that the user wants to predict the amount of power generation;

Collecting the existing generation amount data stored one week before the generation amount prediction date from among the existing generation amount data including weather and temperature for each date and time period stored in advance before the generation amount prediction date;

filtering the existing power generation data belonging to a weather region similar to the weather included in the power generation forecasting date, respectively, from among the collected existing power generation data;

extracting maximum power generation data and average power generation data from the filtered existing power generation data, and then excluding power generation data with an error rate of 10% or more;

Comparing the weather for each time period based on the average power generation data and extracting the existing power generation data of the weather included in the power generation forecast date and approximate weather;

Normalizing the extracted existing power generation data based on the average power generation data;

Predicting the generation amount by time period using the normalized existing generation amount data;

Selecting a demand forecasting date including weather and temperature for each time period that the user wants to predict the demand for;

classifying the demand forecast days into weekdays and weekends;

When the demand forecasting day is a weekday, collecting existing demand data stored one week before the demand forecasting day from among the existing demand data including weather and temperature for each date and time period stored in advance before the demand forecasting day;

When the demand forecast date is a weekend, collecting existing demand data stored three weeks before the demand forecast day from among the existing demand data including weather and temperature for each date and time period stored in advance before the demand forecast day,

filtering the existing demand data belonging to a weather region similar to the weather included in the demand forecast date from among the collected existing demand data, respectively;

Extracting the maximum demand data and the average demand data from the filtered existing demand data, and then excluding the demand data with an error rate of 10% or more;

Comparing the weather for each time period based on the average demand data, and extracting the existing demand data of the weather included in the demand forecast date and approximate weather;

Normalizing the extracted existing demand data based on the average demand data;

A smart grid monitoring method for a heat pump heating and cooling system using a building-integrated solar thermal complex, comprising the step of predicting demand by time period using the normalized existing demand data.