A Green and Energy-Efficient Smart Building Driven by Photovoltaic

Thermal Panels Connected to the Grid

Amirmohammad Behzadi

and Sasan Sadrizadeh

1,2 b

Department of Civil and Architectural Engineering, KTH Royal Institute of Technology, Stockholm, Sweden

School of Business, Society and Engineering, Mälardalen University, Västerås, Sweden

Keywords: Renewable Energy Resource, Efficient Energy Use, Smart Energy Storage, Energy Monitoring, Smart

Building.

Abstract: The present paper introduces a new smart building system driven by photovoltaic thermal panels. The concept

is to improve the contribution of renewable energy in the local matrix for peak load shaving by having a two-

way connection with the local electricity network via a rule-based energy monitoring control design. Besides,

the feasibility of removing the electrical storage unit with high investment cost is studied by establishing a

dynamic interaction between the energy production and usage components to reduce the energy costs over

the year. The system has intelligent thermal energy storage integrated with an electrically-driven coil, heat

exchanger, pumps, and several smart valves and control units. The transient system simulation (TRNSYS)

package is implemented to assess the practicality of the suggested intelligent model for a building complex in

Malmo, Sweden. According to the parametric outcomes, by raising the panel area, while the generated

electricity increases, the solar utilization factor falls, indicating conflictive changes among performance

metrics. The results also show that the renewable resource covers the building's heating and electricity

demands for the majority of the year and that a significant amount of energy is sold to the neighbourhood

electricity grid, demonstrating the viability of the introduced intelligent model.

1 INTRODUCTION

Greenhouse gas emissions from buildings are widely

recognized as a major contributor to the

environmental problem because of the rising demand

for power and heating in homes. More than thirty

percent of all of the natural gas used in the globe is

used in buildings, and buildings account for forty

percent of all domestic primary energy use. As a

result, many nations have started implementing

energy conservation measures in their building

infrastructure, such as the 20-20-20 strategy that aims

to increase building energy efficiency by 20%

(Nourozi, Wang and Ploskić, 2019). Moving

practically toward the genuine concept of a smart

building is a viable and promising solution to the

issues associated with the high levels of

environmental pollution and energy use in the

building sector. Two key features of smart buildings

are having an individual renewable-driven energy

https://orcid.org/0000-0002-8118-8329

https://orcid.org/0000-0002-9361-1796

supply and intelligent interaction with the local

electricity/heating networks.

As part of its functionality, smart buildings are

expected to communicate both ways with the energy

distribution grids that supply them. The building's

energy system can buy energy from the networks

when it is not producing enough, and it can sell

energy to the networks when it produces more than it

needs. Al-Saqlawi et al. (Al-Saqlawi, Madani and

Mac Dowell, 2018) proposed and comprehensively

assessed the performance of a building driven by PV

with two-way interaction with the electricity network.

Their results showed that the suggested system is

more cost effective than an off-grid one, revealing the

importance of making the exisiting buildings smarter

via the clever connection with the local energy

distribution network. Baneshi and Hadinfard

(Baneshi and Hadianfard, 2016) analyzed the techno-

economic facets of a building consisting of PV panels

with and without a battery to determine which was

106

Behzadi, A. and Sadrizadeh, S.

A Green and Energy-Efﬁcient Smart Building Driven by Photovoltaic Thermal Panels Connected to the Grid.

DOI: 10.5220/0011884800003491

In Proceedings of the 12th International Conference on Smart Cities and Green ICT Systems (SMARTGREENS 2023), pages 106-112

ISBN: 978-989-758-651-4; ISSN: 2184-4968

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

more efficient. Using a battery increases energy costs

by 33%, proving that the grid-connected system is

preferable to the off-grid one. Syed et al. (Syed,

Hansen and Morrison, 2020) conducted a

performance evaluation of a smart building equipped

with PV panels that interacted with the regional

electrical grid. According to their results, the

suggested solar-based system can meet 75% of the

electricity needs, and that two-way communication

with the grid performs better than an independent off-

grid system. In order to reduce the building's energy

expenses, Sharma et al. (Sharma, Kolhe and Sharma,

2020) introduced an innovative smart energy system

driven by PV panels having two-way connections

with the local grid. They showed that the smart

interaction with the local energy network mitigates

the energy bills and aids in shaving the peak load.

While there are many different types of renewable

energy systems for buildings, solar-powered systems

are by far the most common and widely used option.

Systems that harness solar energy generate carbon-

free power or heat from the sun's rays, which is good

for the environment (from the CO

emission point of

view) (Behzadi et al., 2022). Photovoltaic (PV)

panels are one type of solar technology that produces

clean, green energy. Solar PV is environmentally

beneficial because it produces no damaging

greenhouse gases while producing electricity.

Photovoltaic thermal (PVT) panels have improved

performance, integration potential, and overall

efficiency than PV panels due to their ability to

capture useable thermal energy from the same area.

PVT panels provide the added benefits of reliability

and lifetime due to their ability to run with minimal

degradation for over twenty years (Gholamian et al.,

2020). Lately, Zarei et al. (Zarei et al., 2020) assessed

and compared the techno-economic indicators of a

solar-driven building system comprising PVT panels

with the same system integrated with PV. According

to their observations, the PVTs were 11% more

efficient due to their ability to generate heat and

electricity simultaneously. Tse et al. (Tse, Chow and

Su, 2016) conducted a techno-economic evaluation of

PVT panels and a hybrid PV system combined with a

solar thermal collector. They proved that PVT panels

are superior due to the reduced payback time and

increased performance efficiency to meet the

electricity/heating of an office building. In another

research, Kamel et al. (Kamel, Elbanhawy and Abo

El-Nasr, 2019) found that PVT panels outperform

hybrid PVs and solar collectors for residential use due

to their less product unit costs and superior efficiency.

Buonomano et al. (Buonomano et al., 2017) used

TRNSYS software to analyze the interaction between

a hybrid building system equipped with PVT panels

and a thermal energy storage tank. After calculating,

they determined a 68.8% decrease in energy use and

a 90.2% decrease in carbon dioxide emissions,

showing the excellence of PVT-driven energy

systems to achieve an efficient and green building.

Behzadi and Arabkoohsar (Behzadi and

Arabkoohsar, 2020) proposed an intelligent building

equipped with PVT and concluded that significant

savings can be made on the building's energy bills by

producing both heat and electricity.

The present study proposes a novel solar-based

smart building combined with photovoltaic thermal

panels to shave the peak load and improve the

renewable contribution in the neighboring energy

grid. The system is equipped with thermal energy

storage with an electrically-driven coil to smooth out

the solar energy's rapid changes, making the energy

accessible whenever and wherever it is needed and

providing a dynamic operation for best use. By

developing a dynamic interaction between energy

production/usage/local grid via a rule-based energy

monitoring unit, this research looks at the potential

savings in annual energy expenses that could be

realized by eliminating the costly electrical storage

unit. A Swedish city Malmo, which benefits from

abundant solar radiation, undergoes a full

performance evaluation using TRNSYS software to

investigate the viability of the proposed model to

satisfy a residential building's complex heating and

electricity demands. The transient assessment and

parametric examination are accomplished to analyze

the impact of local ambient conditions and main

decision variables.

2 SYSTEM DESCRIPTION

Figure 1 demonstrates the simple schematic is the

proposed intelligent building energy system. As

shown, the system is driven by photovoltaic thermal

panels generating electricity and heating with a

promising performance efficiency. The most

significant aspect of this model is the smart rule-

based controllers designed to effectively monitor the

energy generation, usage, and transfer to the local

electricity and heating networks. According to the

figure, the rule-based controllers determine that the

heating produced by the solar panels could either

charge the thermal energy storage tank to supply the

building's demand or be sent to the heat exchanger to

be sold to the local district heating network. Besides,

the smart control unit regulates the electricity flow

between the solar panels, electricity grid, thermal

A Green and Energy-Efﬁcient Smart Building Driven by Photovoltaic Thermal Panels Connected to the Grid

107

energy storage, and the building's load. In this regard,

the priority is charging the tank and changing the

generated electricity to the hot water via the electrical

coil for domestic uses.

If the tank is full, the produced electricity provides

the building's demand based on the rule-based

strategy. Otherwise, the additional generation is sold

to the local electricity grid to mitigate the energy cost

and provoke the householders to adopt their own

energy plant. Because the suggested smart model has

a two-way connection with the electricity network,

the system can purchase electricity when there is no

solar radiation, or the solar intensity is not high

enough to run the tank and supply the load. The

proposed system is equipped with several controllers

and intelligent valves for clever switching between

different modes and effectively manages the energy

flow between the components and grids.

Figure 1: A simple schematic of the suggested new smart

building model.

3 METHODOLOGY

In order to conduct the performance analysis of the

proposed intelligent building system, a transient

software simulator (TRNSYS), as a prominent tool

for simulating renewable energy systems in transient

mode, is applied. The thermodynamic formulation of

thermal energy storage, photovoltaic thermal panels,

heat exchangers, pumps, and smart valves are

calculated and validated with the experimental data.

Moreover, TRNBuild software is used to calculate the

building's load needed to perform the transient

simulation. The mass/energy balance computations

contemplating each component as a control volume

are assessed to conduct the thermodynamic analysis

transiently as expressed as follows (Arabkoohsar,

Behzadi and Nord, 2021):

∑

𝑚





∑

𝑚





(1)

𝑄



−𝑊



∑

𝑚





ℎ



−

∑

𝑚





ℎ



(2)

These equations have three independent variables: 𝑚 ,

which stands for the mass flow into and out of each

component; 𝑊



, which stands for the power produced/

used by each component; and 𝑄



, which denotes the

heating transferred from/to each piece of equipment.

Transient system modeling and control are the

main uses of the simulation program TRNSYS.

TRNSYS is separated into two parts. The first is a

system calculation engine that loads and analyzes the

input file, continuously calculates the system, validates

for convergence, and displays system variables. The

second component of TRNSYS is a wide library of

components, each of which models a distinct

subsystem's performance (Behzadi, Arabkoohsar and

Perić, 2021). The standard library contains about one

hundred fifteen models, including pumps, buildings

solar technologies, weather data processors, and

typical heating, ventilation, and air conditioning

equipment. Users can modify existing components in

models or add new ones, expanding the possibilities of

the environment. The following is a detailed discussion

of the components utilized to model the suggested

building energy system (KLEIN and A., 1988).

By including a PV module in the conventional

flat-plate collector (type 1), Type 50 simulates a

photovoltaic thermal panel. It replicates a combined

collector and considers both Florschuetz's research

and work for flat plate collectors running at maximum

power and a study provided in a report by Arizona

State University for concentrating combination

collectors. In the latter approach, peak power or

current output at a specified voltage is solved using

the I-V curves of the cells (or array). Type 158

describes a vertical, constant-volume storage tank.

The working fluid's heat may be lost to the

atmosphere through the top, bottom, and sides. In

order to simulate stratification, the tank is divided into

a user-specified number of isothermal nodes. Type 91

is a sensible heat exchanger with no capacitance,

regardless of the installation. The input temperatures

of the fluid on both the cold and hot sides can be used

to determine the maximum practical heat transfer in

this section. Type 2 represents the one-shot controller

with a binary input of either 1 or 0. The current

temperature is compared to two threshold values to

establish the control signal's strength. Estimates of the

current signal value are derived from the value of the

input control function at a prior time step. Hysteresis

can be triggered by linking a controller's input and

output control signals. Moreover, the proposed smart

model also includes numerous intelligent valves for

diverse uses, such as mixer, diverter, and tempering

valves. Depending on the location of a control valve,

the Type 11f simulated flow diverter divides a single

liquid input into two liquid outputs. The tempering

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valve is expressed by type 11b and determines the

outlet split ratio which is necessary to achieve a

specific temperature.

4 RESULTS AND DISCUSSION

In order to evaluate the viability of the suggested

solar-driven smart system for a building complex in

Malmo, Sweden, transient simulation and parametric

study are performed. First, the variation of the most

critical performance indicators with the key decision

variables is examined and compared via the

parametric investigation. Then, the hourly, monthly,

seasonal, and yearly changes in system performance

are evaluated to assess the impact of the ambient

condition in detail.

The impact of the panels' area as a key design

variable affecting the PVT and the entire system's

performance is shown in Figure 2. According to the

figure, by picking up a higher area (from 100 m2 to

200 m2), the annual electricity generated via the solar

system increases drastically by about 9500 kWh. This

is reasonable because the output products are directly

affected by the panels' surface. The figure further

shows that a lower solar utilization factor is obtained

by raising the panels' area, which is unfavorable. The

trend shows that the output production rise is lower

than the input energy increment; therefore, a lower

utilization factor is attained, as depicted in Figure 2.

Figure 2: The variation of solar utilization factor and the

produced electricity with the panels' area.

Figure 3 demonstrates the effect of heat exchanger

effectiveness on the solar utilization factor and the

heat transferred to the district heating network. By

definition, effectiveness is a ratio of the actual heat

transfer through the heat exchanger to the maximum

heat transfer that may happen there. Ergo, more heat

exchanger effectiveness results in higher heating

transferred (by around 600 kWh) from the proposes

smart system to the district heating network. Besides,

the figure indicates that raising the effectiveness from

0.85 to 0.95 increases the utilization factor by about

0.007, which is negligible. This is justifiable since the

increment in transferred heating is not considerable

compared to the other terms in the utilization factor

equation.

Figure 3: The variation of solar utilization factor and the

transferred heating with the heat exchanger effectiveness.

Carefully constructed thermal storage systems

usually result in cheaper capital costs as well as

decreased energy usage. Thermal stratification

improves storage tank effectiveness by allowing

warmer layers to be heated by energy at moderate

temperatures. The basis for the operation of a

stratified TES tank is the thermal stratification

process. Due to the inverse relationship between

water density and temperature, stratification is a

natural occurrence. As a result, heated water always

floats above cold water. In order to simulate

stratification and forecast how a unit would behave in

terms of losses, charges, and discharges over time,

nodes are added to the storage model. Figure 4

presents the temperature changes over the nodes of

thermal energy storage over December. As shown in

the figure, the stratification causes a constant

temperature difference between neighbouring nodes.

According to the figure, the temperature difference

between the highest and lowest nodes at some hours

increases by about 18°C.

Figure 4: The temperature variation over the nodes of

thermal energy storage in December.

100 120 140 160 180 200

0.34

0.36

0.38

0.4

0.42

0.44

8000

10000

12000

14000

16000

18000

(

)

SUF (-)

SUFSUF

Electricit

(

kWh

)

Ele ct r ici tyEle ct r ici ty

0.85 0.87 0.89 0.91 0.93 0.95

0.38

0.382

0.384

0.386

0.388

8000

8100

8200

8300

8400

8500

8600

8700

Effectiveness

(

)

SUF (-)

Heating (kWh)

HeatingHeating

SUFSUF

8064 8232 8400 8568 8736

Time (h)

Temperature (°C)

Node 1Node 1

Node 2Node 2

Node 3Node 3

Node 4Node 4

Node 5Node 5

Node 6Node 6

A Green and Energy-Efﬁcient Smart Building Driven by Photovoltaic Thermal Panels Connected to the Grid

109

In Figure 5, the temperature changes over the

nodes of thermal energy storage in July are depicted

and compared. While above-55°C water is being

charged into the thermal energy storage at the first

node, cold water is discharged from the tank with a

temperature of around 22°C (at the sixth node).

Figure 5 shows a constant temperature difference

between the nodes because of the stratification over

the year.

Figure 5: The temperature variation over the nodes of

thermal energy storage in July.

Figure 6 illustrates the hourly variation and the time

duration curve of the electricity generated via

photovoltaic thermal panels to present the impact of

the ambient condition on the panels' performance

over the year. As presented, the produced electricity

increases from winter to summer by raising the solar

intensity. So, the share of the proposed smart

renewable-based energy system on the local

electricity grid increases, and the building's energy

cost could be paid off on warm days. The figure

further shows that the highest produced electricity

equals 14,400 Wh, and about 43% of the year, the

solar system could generate the electricity to either

charge the tank, satisfy the demand, or sell to the local

grid.

Figure 6: The hourly changes and time duration curve of the

generated electricity.

In Figure 7, the hourly variation/duration curve of the

heating generated via the panels is indicated to show

the ambient condition impact on the other vital

performance metric, heating production. Like the

electricity trend, heating production rises from cold

months to warm months due to the ambient

temperature/solar intensity increment. According to

this trend, the suggested smart model can sell

significant hot water to the local district heating

network in summer. The figure demonstrates that the

suggested system can charge the tank or sell the

additional production to the local district heating

network for about 21% of the year. Also, the

maximum hourly heating generation of 45,550 Wh is

achieved at an hour in summer.

Figure 7: The hourly changes and time duration curve of the

generated heating.

Figure 8 represents the monthly changes in the solar

utilization factor and the heating transferred to the

district heating network through the heat exchanger.

According to the figure, the photovoltaic thermal

panels will generate higher heating when the solar

intensity rises from winter to summer. Hence, the

thermal energy storage tank is filled, the building's

demand is provided, and the maximum extra heat of

2,310 kWh is transferred to the local district heating

network in July. The figure further depicts that in cold

months, from October to February, the heating

transferred to the network is almost zero, revealing

the role of a rule-based model for smartly monitoring

energy production/usage over the year. On the other

hand, the figure illustrates that the solar utilization

factor trend is not easily forecasted because of the

simultaneous increase/reduction in/off useful

production and input energy. It decreases from 0.43

in November to 0.28 in February and then increases

to 0.46 in August.

4380 4526 4672 4818 4964 511

Time (h)

Temperature (°C)

Node 1Node 1

Node 2Node 2

Node 3Node 3

Node 4Node 4

Node 5Node 5

Node 6Node 6

0 10 20 30 40 50 60 70 80 90 10

5000

10000

15000

Time

(

)

Electricity (Wh)

Durati onDurati on

HourlyHourly

0 10 20 30 40 50 60 70 80 90 10

10000

20000

30000

40000

50000

Time

(

)

Heating (Wh)

DurationDuration

HourlyHourly

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Figure 8: The monthly variation of solar utilization factor

and the heating transferred through the heat exchanger.

4 CONCLUSIONS

An innovative smart building system powered by

photovoltaic thermal panels is presented and wholly

analyzed in this research. The idea is to have a two-

way interaction with the local electricity network and

a one-way connection with the disctrict heating gird

via a rule-based energy monitoring control design to

increase the proportion of renewable energy for peak

demand reduction. Additionally, by establishing a

dynamic interaction between energy production and

usage parts to lower energy prices annually, the

viability of eliminating the electrical storage unit with

a high investment cost is investigated. The suggested

system has an electrically driven coil integrated with

thermal energy storage, a heat exchanger, pumps,

several smart valves, and control units. The

recommended intelligent model for a building

complex in Malmo, Sweden, is tested using the

transient system simulation (TRNSYS) program. For

this, a parametric analysis is used to assess how

important choice variables affect the model's

performance. Additionally, the effect of local weather

change is investigated by extracting results on

hourly/monthly/seasonal/annual basis. According to

the results, the panel's physical appearance must be

selected carefully. In this regard, while the electricity

generation is increased, the solar utilization factor

decreases by picking up a higher panel area. The

parametric results further show that the heat

exchanger's effectiveness has a neutral effect on the

solar utilization factor, and the heat transferred to the

district heating network increases by raising the

effectiveness from 0.85 to 0.95. On warmer days, the

proposed system can sell considerable additional

electricity/heating production to the local energy

networks, signifying a rule-based model's role in

effectively monitoring the energy production, usage,

and storage components.

ACKNOWLEDGEMENTS

The authors are grateful to the Swedish Energy

Agency (Energimyndigheten) for financing this

research study. This article is drafted in line with

Annex 37 (Smart Design and Control of Energy

Storage Systems).

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