**A Study on Assessment of Power Output by Integrating Wind Turbine and Photovoltaic Energy Sources with Futuristic Smart Buildings**

Akira Nishimura and Mohan Kolhe

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/58880

## **1. Introduction**

Fossil fuel reserves are limited and intensive burning of hydro-carbon based fuel sources are impacting on global climate. In all over the world, there is continuous encouragement to increase the penetration of environment friendly energy sources for fulfilling growing energy demand and also to minimize the use of hydro-carbon based power plants. Renewable energy sources such as wind, photovoltaic (PV), solar thermal, geothermal, bio-energy are drawing attention from the world as alternative environment friendly energy sources. The energy density of these renewable energy sources is low. Most of them are dependent on nature and are found intermittent. It is very important to develop proper strategies to integrate these renewable energy sources into the power system network for fulfilling the energy demand. As cities around the world are experiencing exponential growth and there is urgent need to ensure that cities should expand sustainably, operate efficiently, and maintain a high quality life of residents. One of a city's most important critical infrastructures of a city is reliable power supply network. The smart city is an effective way to integrate renewable energy sources into the existing energy system network. In Japan, some demonstration projects of smart city are under contemplation [1]. In China, Tianjin City is being rebuilt as an ecological city by project in collaboration with a Singapore company [2]. Such type of trend will continue and in near future many cities will be rebuilt as smart city.

In a smart city planning, it is very important to consider the future growth of building integrated environment friendly energy systems. Energy output from intermittent renewable energy sources in the built environment depends on the availability of natural resources (e.g. wind speed, solar radiation, etc.) in the urban area. In the built environment, it will be

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challenging to integrate intelligently renewable energy sources and distributed generators as the existing building infrastructure are not designed to integrate them into the power system infrastructure. In future smart city planning, it is very important to consider proper intelligent integration of renewable energy sources into the built environment. A smart city development and deployment of building integrated renewable energy system has to harmonize with expansion of the combined heat and power system infrastructure, the information and communication infrastructure, the transport infrastructure, and with integration of new secure monitoring and control applications [3].

This chapter intends to propose a smart city for utilizing renewable energy sources as much as possible. For example, the city consists of many buildings and the buildings are thought to be an obstacle to natural wind flow. If the wind movements through building layouts are controlled, then there is possibility that the wind can be utilized for power generation through wind turbine. In addition, solar power can be utilized by installing solar panels on the roof and/or side wall of the buildings. The proper building design can help to utilize the available solar radiation for generating power and heat through solar energy systems. For designing a building in smart city, building dimensions and layouts are very important for effective utilization of wind speed and solar radiation. It has been observed that, there is very limited research and project works, which are investigating these issues. This chapter is providing study on large scale power generation by wind turbine and PV systems integrated with building and in the city. In this study, the horizontal axis wind turbine is considered for integrating with city infrastructure and the output of commercial horizontal axis wind turbine is much larger compared to that of the commercial vertical axis wind turbine. Additionally, the building integrated with PV systems is considered. The proposed schematic of smart buildings integrated with wind turbine and PV system is given in Figure 1.

In a smart city, it is very important to analyze the wind speed and their directions flow. The analysis of wind flows around the buildings is done through numerical simulations and most of them are using turbulent model such as standard *k*-ε, LES (Large Eddy Simulation), and DNS (Direct Numerical Simulation) [4-10]. Also, wind tunnel experiments on wind flows around a building have been discussed in references [11-14]. Although these works have investigated wind velocity profile around various building models under different conditions [4-14], but there has not been any report/work in which building sizes and layouts are considered in order to utilize wind blowing around the building for power generation from wind turbine in built environment. To realize wind energy utilization in the built environment, it is important to conduct feasibility study on power generation from wind turbine under the actual wind speed conditions. Although the power generation performance of a wind turbine has been predicted using frequency distribution of wind speed and wind direction, in most of the studies, the proper planning of wind turbine by utilizing the wind movements through building sizes and layouts and PV system in the built environment in a smart city are not considered [15-24]. Additionally, it is observed that there are very few studies that investigate the power output of building integrated PV system with wind turbine under actual meteoro‐ logical conditions considering city layouts. Moreover, it is important to examine/analyze the A Study on Assessment of Power Output by Integrating Wind Turbine… http://dx.doi.org/10.5772/58880 141

challenging to integrate intelligently renewable energy sources and distributed generators as the existing building infrastructure are not designed to integrate them into the power system infrastructure. In future smart city planning, it is very important to consider proper intelligent integration of renewable energy sources into the built environment. A smart city development and deployment of building integrated renewable energy system has to harmonize with expansion of the combined heat and power system infrastructure, the information and communication infrastructure, the transport infrastructure, and with integration of new secure

This chapter intends to propose a smart city for utilizing renewable energy sources as much as possible. For example, the city consists of many buildings and the buildings are thought to be an obstacle to natural wind flow. If the wind movements through building layouts are controlled, then there is possibility that the wind can be utilized for power generation through wind turbine. In addition, solar power can be utilized by installing solar panels on the roof and/or side wall of the buildings. The proper building design can help to utilize the available solar radiation for generating power and heat through solar energy systems. For designing a building in smart city, building dimensions and layouts are very important for effective utilization of wind speed and solar radiation. It has been observed that, there is very limited research and project works, which are investigating these issues. This chapter is providing study on large scale power generation by wind turbine and PV systems integrated with building and in the city. In this study, the horizontal axis wind turbine is considered for integrating with city infrastructure and the output of commercial horizontal axis wind turbine is much larger compared to that of the commercial vertical axis wind turbine. Additionally, the building integrated with PV systems is considered. The proposed schematic of smart

buildings integrated with wind turbine and PV system is given in Figure 1.

In a smart city, it is very important to analyze the wind speed and their directions flow. The analysis of wind flows around the buildings is done through numerical simulations and most of them are using turbulent model such as standard *k*-ε, LES (Large Eddy Simulation), and DNS (Direct Numerical Simulation) [4-10]. Also, wind tunnel experiments on wind flows around a building have been discussed in references [11-14]. Although these works have investigated wind velocity profile around various building models under different conditions [4-14], but there has not been any report/work in which building sizes and layouts are considered in order to utilize wind blowing around the building for power generation from wind turbine in built environment. To realize wind energy utilization in the built environment, it is important to conduct feasibility study on power generation from wind turbine under the actual wind speed conditions. Although the power generation performance of a wind turbine has been predicted using frequency distribution of wind speed and wind direction, in most of the studies, the proper planning of wind turbine by utilizing the wind movements through building sizes and layouts and PV system in the built environment in a smart city are not considered [15-24]. Additionally, it is observed that there are very few studies that investigate the power output of building integrated PV system with wind turbine under actual meteoro‐ logical conditions considering city layouts. Moreover, it is important to examine/analyze the

monitoring and control applications [3].

140 Global Warming - Causes, Impacts and Remedies

**Figure 1.** Image of smart buildings integrated with wind turbine and PV system proposed by this study

feasibility of installing wind turbines in planed building models and solar PV electricity generation and their role on meeting electrical energy demand of the city.

In this study, building layouts are considered for producing higher wind turbine power output in built environment [25, 26]. The configuration of building layouts like nozzle, as shown in Figure 2, is proposed and investigated to obtain the tapered wind flow through the buildings. Two buildings are configured as a nozzle (Figure 1) and the building size is taken 10 m width, 40 m length, and 40 m height. The representative length of this model *L*, which is a hydraulic diameter of horizontal cross area, is 16 m. Other dimensions (e.g. angle between two buildings, i.e. 90 degree, distance between two buildings 40 m, etc.) of the building layouts are given in Figure 2. In a city planning, there will be several buildings, but this study considers only two buildings. In future work, multi building layouts will be considered for wind speed distribu‐ tion in the downstream. The results of numerical simulation on wind flows around buildings have been carried out by the turbulent model such as *k*-ε model. The wind speed distributions across the buildings according to the proposed building layouts are investigated. Moreover, the wind speed distribution under the various wind velocities and directions at inlet of the building model is investigated in order to simulate the actual meteorological conditions. The wind speed data base of the Japan Meteorological Agency [27] and the power curve charac‐ teristics of commercial wind turbine are utilized for evaluating wind speed profile across the buildings and for finding the electrical energy output from a wind turbine. In addition, this study presents the investigation results on the optimum installing procedure of solar PV panel on the roof of a building under the actual meteorological conditions in order to obtain higher

**Figure 2.** Building layouts for wind speed profile and wind power generation

power output from PV system. This study also evaluates the power generation characteristics of combined system including wind turbine and PV under the actual meteorological conditions as well as energy supply adaptability to the energy demand of a building. The change in power energy of wind turbine and PV system with time is investigated comparing with real time energy demand. It is very important to consider these aspects while designing a smart city and its infrastructure. The real time power generation form the energy sources located in the built environment and the demand characteristics are going to be very useful for designing and planning distributed smart power system network infrastructure. The significant point is to fulfill the built environment energy demand from the renewable energies through daily and seasonal variations and these analyses are presented in this chapter. It is assumed that proposed building models will be located in the actual city in Japan and local energy supply and demands are also discussed. The study, which is presented in this chapter, may be very useful for city planner for finding proper locations/layouts of the buildings for effective utilization of wind and solar energy resources in the built environment. It can suggest new concepts in order to construct/develop a smart city, which can help/contribute in reducing green house gas emissions.

## **2. Building model analysis in built environment**

#### **2.1. Simulation of wind speed distribution due to building sizes and layouts in built environment**

In this section, a commercial CFD software CFD-ACE+(WAVE FRONT) is adopted for numerical simulation of wind speed distribution in the built environment. This CFD software has many simulation code/tools for solving the multi-dimensional fluid dynamics. The validation of the simulation procedure of this CFD software has been well established [28-33]. The standard *k*-ε model is adopted in this study. In the CFD software, the continuity equation is given by [34, 35]:

$$\frac{\partial \rho}{\partial t} + \nabla \left(\rho \vec{V}\right) = 0 \tag{1}$$

where *ρ* is density [in kg/m3 ], *t* is time [in s] and *V* <sup>→</sup> is velocity vector [in m/s].

The momentum equation is given by [34, 35]:

power output from PV system. This study also evaluates the power generation characteristics of combined system including wind turbine and PV under the actual meteorological conditions as well as energy supply adaptability to the energy demand of a building. The change in power energy of wind turbine and PV system with time is investigated comparing with real time energy demand. It is very important to consider these aspects while designing a smart city and its infrastructure. The real time power generation form the energy sources located in the built environment and the demand characteristics are going to be very useful for designing and planning distributed smart power system network infrastructure. The significant point is to fulfill the built environment energy demand from the renewable energies through daily and seasonal variations and these analyses are presented in this chapter. It is assumed that proposed building models will be located in the actual city in Japan and local energy supply and demands are also discussed. The study, which is presented in this chapter, may be very useful for city planner for finding proper locations/layouts of the buildings for effective utilization of wind and solar energy resources in the built environment. It can suggest new

640 m

**340m**

340 m

284.7 m

**Figure 2.** Building layouts for wind speed profile and wind power generation

90°

40 m

81.7m 40 m

*x*

*y*

*y*

142 Global Warming - Causes, Impacts and Remedies

10 m

*x/L***=2.50** *x/L***=1.875** *x/L***=1.25**

*z* 10 m

40 m

520 m

680 m

530 m

*x*/*L* = 1.25 *x*/*L* = 1.875 *x*/*L* = 2.50

40 m

680 m

80 m

10 m

$$\frac{\partial \boldsymbol{u}\_{j}}{\partial t} + \nabla \left(\boldsymbol{u}\_{j}\vec{V}\right) = -\frac{1}{\rho} \frac{\partial p}{\partial \mathbf{x}\_{j}} + \nabla \left(\nu\_{\text{eff}} \nabla \boldsymbol{u}\_{j}\right) \tag{2}$$

$$\nu\_{\rm eff} = \nu + \nu\_t \tag{3}$$

where *uj* is velocity [in m/s] at *j* component of coordinate system, *p* is pressure [in Pa], *νeff* is effective viscosity coefficient [in m2 /s], *ν* is kinematic viscosity coefficient [in m2 /s] and *ν<sup>t</sup>* is eddy viscosity coefficient [in m2 /s].

In the CFD software, the standard *k*-ε model is given by [34, 35]:

$$\nu\_t = \frac{\mathbb{C}\_{\mu}k^2}{\varepsilon} \tag{4}$$

$$\frac{\partial \mathbf{k}}{\partial t} + \frac{\partial}{\partial \mathbf{x}\_j} \left( \boldsymbol{\mu}\_j \mathbf{k} \right) = \mathbf{S} - \boldsymbol{\varepsilon} + \frac{\partial}{\partial \mathbf{x}\_j} \left[ \left( \boldsymbol{\nu} + \frac{\boldsymbol{\nu}\_t}{\sigma\_k} \right) \frac{\partial \mathbf{k}}{\partial \mathbf{x}\_j} \right] \tag{5}$$

Photovoltaic Energy Sources with Futuristic Smart Buildings

Author(s) Name(s): Akira Nishimura

5th row, 2nd column:

U0 = 3.00 12.00 m/s

Table caption:

Time h]

Title of horizontal axis:

Title of right vertical axis:

Title of right vertical axis:

Self-sufficient ratio [ ]

Self-sufficient ratio [ ]

Table 2. Specification of wind turbine

Page No.

6 Table 1.

9 Table 2.

19 Figur

22 Figur

22 Figur

e 22.

e 21.

e 18.

Line

$$\frac{\partial \boldsymbol{\varepsilon}}{\partial t} + \frac{\partial}{\partial \mathbf{x}\_j} \left( \boldsymbol{u}\_j \boldsymbol{\varepsilon} \right) = \mathbf{C}\_{x1} \frac{\mathbf{S} \boldsymbol{\varepsilon}}{k} - \mathbf{C}\_{x2} \frac{\boldsymbol{\varepsilon}^2}{k} + \frac{\partial}{\partial \mathbf{x}\_j} \left[ \left( \boldsymbol{\nu} + \frac{\mathbf{v}\_t}{\sigma\_\varepsilon} \right) \frac{\partial \boldsymbol{\varepsilon}}{\partial \mathbf{x}\_j} \right] \tag{6}$$

$$S = \nu\_i \left( \frac{\partial u\_i}{\partial x\_j} + \frac{\partial u\_i}{\partial x\_i} - \frac{2}{3} \frac{\partial u\_m}{\partial x\_m} \delta\_{ij} \right) \frac{\partial u\_i}{\partial x\_j} - \frac{2}{3} k \frac{\partial u\_m}{\partial x\_m} \tag{7}$$

where *k* is turbulent energy [in m2 /s2 ], ε is dissipation rate [in m<sup>2</sup> /s2 ], *δij* is Kronecker delta, *Cμ* is 0.09, *C*ε*1* is 1.44, *C*ε*2* is 1.92, *σk* is 1.0, *σ*ε is 1.3. Regarding *xi* , *xj* , and *xm* which represent components of coordinate system [in m], *x1*=*x*, *x2*=*y*, *x3*=*z*. Regarding *ui* , *uj* , and *um* which represent velocities [in m/s], *u1*=*U*, *u2*=*V*, *u3*=*W*. *U*, *V*, *W* are the velocity components of coordinate system, *x*, *y*, *z*, respectively. PROOF CORRECTIONS FORM No. Delete Replace with


Table 2. Specifications of wind turbine **Table 1.** Simulation condition of wind speed distribution around buildings

0 0.02 0.04 0.06 0.08

Power energy, Electric consumption [kWh]

Self-sufficient ratio [%]

Wind turbine Photovoltaic

Self-sufficient ratio [%]

Electric consumption

Combined power generation system

U0 = 3.00 - 12.00 m/s

Time [h] 0.1 0.12 Power energy[kWh/m²] Total amount of power energy in a day: 0.71kWh/m<sup>2</sup> Table 1 lists the simulation parameters of wind speed distribution around buildings. The simulation model is already shown in Figure 2. Numerical simulation has been carried out under steady state by standard *k*-ε model and the calculation number is set 10000. This calculation number should be appropriate because the residue of each parameter under each numerical simulation condition keeps a stable low value after 500 times calculation. Wind speed at inlet of the model is set by the following equation:

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Time [h]

1 2 3 4 5 6 7 8 9 101112131415161718192021222324

Self-sufficient ratio of combined power generation system

Time [h]

1

Self-sufficient ratio [%]

A Study on Assessment of Power Output by Integrating Wind Turbine… http://dx.doi.org/10.5772/58880 145

$$\mathcal{U} = \mathcal{U}\_0 \left(\frac{z}{30}\right)^{0.25} \tag{8}$$

where *U* is the wind speed [in m/s] in *x* direction, *U*0 is the initial wind speed [in m/s] at z=30 m which is changed from 3.0 m/s to 12.0 m/s, *z* is height [in m]. *U*0=10.0 m/s is the rated wind speed of AEOLOS (AEOLOS: wind turbine manufacture) wind turbine of 50 kW class [36]. In this equation, it is assumed that *U* equals to *U*0 at *z*=30 m which is the hub height of wind turbine when the wind reaches to the building.

( ) <sup>2</sup> 1 2

e

*t ij*

/s2

components of coordinate system [in m], *x1*=*x*, *x2*=*y*, *x3*=*z*. Regarding *ui*

is 0.09, *C*ε*1* is 1.44, *C*ε*2* is 1.92, *σk* is 1.0, *σ*ε is 1.3. Regarding *xi*

Kinematic viscosity coefficient

U0 = 3.00 - 12.00 m/s

*S k*

*<sup>S</sup> uC C*

e

*j j j*

2 2 3 3 *j i mi m*

], ε is dissipation rate [in m<sup>2</sup>

*ji m j m u uu u u*

*xx x x x*

æ ö ¶ ¶¶ ¶ ¶ = +- - ç ÷

¶¶ ¶ ¶ ¶ è ø

 d

represent velocities [in m/s], *u1*=*U*, *u2*=*V*, *u3*=*W*. *U*, *V*, *W* are the velocity components of

Density of wind at inlet 1.166 kg/m<sup>3</sup>

of wind 1.56×10-5

Wind speed at inlet <sup>U</sup> = U0×(z/30)0.25 m/s

Turbulent flow model Standard k -ε model Turbulent energy 0.025 m<sup>2</sup>

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Total amount of power energy in a day: 0.71kWh/m<sup>2</sup>

Time [h]

1 2 3 4 5 6 7 8 9 101112131415161718192021222324

Self-sufficient ratio of combined power generation system

Time [h]

Temperature of wind at inlet 293 K Pressure of wind at inlet 0.1 MPa

Slip on side wall of building V = (0.41×|l|)

Dissipation rate (1.58×10-3

Calculation number 10000 Residue of each parameter <1.0×10-5 Calculation state Steady state

Table 1 lists the simulation parameters of wind speed distribution around buildings. The simulation model is already shown in Figure 2. Numerical simulation has been carried out under steady state by standard *k*-ε model and the calculation number is set 10000. This calculation number should be appropriate because the residue of each parameter under each numerical simulation condition keeps a stable low value after 500 times calculation. Wind

Table 2. Specifications of wind turbine

**Table 1.** Simulation condition of wind speed distribution around buildings

speed at inlet of the model is set by the following equation:

 e

*tx k k x x*

 e

¶ ¶ ¶ ¶ é ù æ ö + =-+ + ê ú ç ÷ ¶ ¶ ¶ ¶ ê ú

*j*

n

e

e

144 Global Warming - Causes, Impacts and Remedies

Proof Corrections Form

PROOF CORRECTIONS FORM

Chapter Title: A Study on Assessment of Power Output by Integrating Wind Turbine and Photovoltaic Energy Sources with Futuristic Smart Buildings

No. Delete Replace with

Author(s) Name(s): Akira Nishimura

5th row, 2nd column:

U0 = 3.00 12.00 m/s

Table caption:

Time h]

Title of horizontal axis:

Title of right vertical axis:

Title of right vertical axis:

Self-sufficient ratio [ ]

Self-sufficient ratio [ ]

Table 2. Specification of wind turbine

Page No.

6 Table 1.

9 Table 2.

19 Figur

22 Figur

22 Figur

e 22.

e 21.

e 18.

Line

where *k* is turbulent energy [in m2

coordinate system, *x*, *y*, *z*, respectively.

Time [h]

0 0.02 0.04 0.06 0.08 0.1 0.12

Power energy, Electric consumption [kWh]

Self-sufficient ratio [%]

Wind turbine Photovoltaic

Self-sufficient ratio [%]

Electric consumption

Combined power generation system

Power energy[kWh/m²]

*t*

e

/s2

, *xj*

 m2 /s

/s2

)/z m<sup>2</sup> /s2

0.25U

(U <sup>0</sup> = 3.00 - 12.00 m/s)

ë û è ø (6)

], *δij* is Kronecker delta, *Cμ*

, and *xm* which represent

1

Self-sufficient ratio [%]

, and *um* which

, *uj*

(7)

n

s

 n

e

In this study, the wind speed data for Tsu city in Japan are used from the Japan Meteorological Agency [37] for five years (from 2007 to 2011). The buildings locations are considered as a nozzle, therefore the wind inflow direction is important for obtaining the wind blowing through the buildings. The layouts of the buildings are decided based on the wind speed direction. The wind speed directions and building layouts are given in Figure 3. If the main wind direction is North (N), the wind from North-West (NW), North-North-West (NNW), North-North-East (NNE) and North-East (NE) including North can be utilized for blowing the wind among the buildings through proposed nozzle. Assuming the symmetry to the main wind direction, the wind speed distributions around the buildings for the in-flow angles *β* of 22.5 degree and 45 degree are simulated to evaluate the effect of four angular inflows on the wind speed distribution. Wind speed at the inlet of the model is set by Eqs. (9) and (10), when the effect of inflow angle is considered.

$$\mathcal{U}\mathcal{U} = \cos\beta \times \mathcal{U}\_0 \left(\frac{z}{30}\right)^{0.25} \tag{9}$$

$$V = \sin \beta \times \mathcal{U}\_0 \left(\frac{z}{30}\right)^{0.25} \tag{10}$$

where *V* is the wind speed [in m/s] in *y* direction, *β* is in-flow angle.

The top layer of the model has been considered free (without any disturbance). The slip on side wall of building is set by the following equation:

$$V = \left(0.41 \times \left| l \right| \right)^{0.25} \mathcal{U} \tag{11}$$

where 0.41× |*l* | is the mixing length, 0.41 is Karman coefficient, *l* is distance from wall of building.

**Figure 3.** Wind speed directions and layouts of buildings

#### **2.2. Concept for building size setting in built environment**

It is assumed that buildings are multi storied apartments. According to the statistical data collected by ministry of internal affairs and communications in Japan [38], the average floor area of dwelling in Japan is about 100 m2 per household. The height of one floor is assumed as 4 m. Assuming that four households stay per floor, the floor space is 400 m2 . In this study, for simulating a nozzle by buildings orientation, the width and depth of building are set at 10 m and 40 m, respectively. The height of building should be set over the height of wind turbine if we request an accelerated wind by blowing through buildings. In this study, the real commercial wind turbine is considered for estimating the power generated by wind speed distribution. AEOLOS wind turbine of 50 kW class [36] is used. Table 2 provides the specifi‐ cation of the wind turbine. The hub height and rotor radius of this turbine are 30 m and 9 m, respectively, resulting that the height of building of 40 m is almost same as axis height of wind turbine.


**Table 2.** Specifications of wind turbine

**Figure 3.** Wind speed directions and layouts of buildings

146 Global Warming - Causes, Impacts and Remedies

area of dwelling in Japan is about 100 m2

turbine.

**2.2. Concept for building size setting in built environment**

It is assumed that buildings are multi storied apartments. According to the statistical data collected by ministry of internal affairs and communications in Japan [38], the average floor

for simulating a nozzle by buildings orientation, the width and depth of building are set at 10 m and 40 m, respectively. The height of building should be set over the height of wind turbine if we request an accelerated wind by blowing through buildings. In this study, the real commercial wind turbine is considered for estimating the power generated by wind speed distribution. AEOLOS wind turbine of 50 kW class [36] is used. Table 2 provides the specifi‐ cation of the wind turbine. The hub height and rotor radius of this turbine are 30 m and 9 m, respectively, resulting that the height of building of 40 m is almost same as axis height of wind

as 4 m. Assuming that four households stay per floor, the floor space is 400 m2

per household. The height of one floor is assumed

. In this study,

#### **2.3. Power generation estimation from built environment located wind turbine**

The wind in back area of the building is considered to be available for power generation from wind turbine, as the wind would be accelerated by blowing through nozzle created by buildings layouts/orientations. Three points at the back of buildings, which are apart from the buildings by 20, 30, 40 m (*x*/*L*=1.25, 1.875, 2.50), are assumed as installation points of the wind turbine. The wind speed for calculating the power generated by wind turbine is obtained on 1049 points located in the area where the rotor of wind turbine rotates, i.e., the swept rotor area. The wind speed at each point on the swept rotor area is considered average wind speed in a local area of 0.5 m × 0.5 m. By considering the wind speed distribution of this local wind speed, the wind energy can be calculated. Average wind speed to *x* axis direction is estimated by using the following equation:

$$
\Delta U\_{\text{ave}} = \left(\frac{2Q\_x}{N\rho A}\right)^{\frac{1}{3}}\tag{12}
$$

where *Uave* is the average wind speed [in m/s] to *x* axis direction, *Qx* is the summation of wind energy [in W] to *x* axis direction on each point for calculating wind speed distribution in the swept rotor area *A* (=1049 points), *ρ* is the density [in kg/m3 ] of wind, *A* is the swept rotor area [in m2 ]. Wind energy at each point on the swept rotor area is calculated by the following equation:

$$Q\_x = \sum\_{i=1}^{1049} Q\_{x,i} = \sum\_{i=1}^{1049} \left(\frac{1}{2} \rho A\_i U\_i^3\right) \tag{13}$$

where *Qx,i* is the wind energy [in W] to *x* axis direction at each point, *Ai* is the area of each point [in m2 ] which is equal to 0.5 m × 0.5 m, *Ui* is the wind speed [in m/s] to *x* axis direction at each point for calculating wind energy. *Vave* which is the average wind speed [in m/s] to *y* axis direction is estimated by the same calculation way of *Uave*. The average wind speed [in m/s] to horizontal surface of the swept rotor area *Uh,ave* is calculated by the following equation:

$$\text{CL}\_{h,\text{ave}} = \sqrt{\text{LI}\_{\text{ave}}^2 + {V}\_{\text{ave}}^2} \tag{14}$$

Here, the wind speed and wind energy to *z* axis direction are ignored because the rotor of wind turbine cannot move toward *z* axis direction and wind energy to *z* axis direction cannot be utilized. In estimation of power generation, the wind energy at the point whose *Uh,ave* is below 3 m/s is omitted, because the cut-in wind speed of AEOLOS wind turbine of 50 kW class is 3 m/s.

The power curve of AEOLOS wind turbine of 50 kW class is shown in Figure 4. The authors derive the empirical equation from the data of power curve, which is provided by AEOLOS. Figure 4 indicates the relationship between wind and power, resulting that the power gener‐ ated by this wind turbine can be estimated by using the power curve. The power curve which is adopted in this study is as follows:

$$P\_w = 59.075 \text{LI}\_{h, \text{ave}}^3 - 62.619 \text{LI}\_{h, \text{ave}}^2 - 33.433 \text{LI}\_{h, \text{ave}} \quad \text{(3 } m/s \le \text{LI}\_{h, \text{ave}} \le 10 \text{ m/s)} \tag{15}$$

$$P\_w = -793.94 \text{L} \mathbf{I}\_{h, \text{ave}} + 61.012 \text{ (10 } m/\text{ s} < \text{U}\_{h, \text{ave}} \le 19 \text{ m/s)} \tag{16}$$

where *Pw* is the power [in W] of wind turbine.

**Figure 4.** Power curve of wind turbine

#### **2.4. Estimation of power generated from PV system**

2 2 *U UV h ave ave ave* , = + (14)

Here, the wind speed and wind energy to *z* axis direction are ignored because the rotor of wind turbine cannot move toward *z* axis direction and wind energy to *z* axis direction cannot be utilized. In estimation of power generation, the wind energy at the point whose *Uh,ave* is below 3 m/s is omitted, because the cut-in wind speed of AEOLOS wind turbine of 50 kW class is 3

The power curve of AEOLOS wind turbine of 50 kW class is shown in Figure 4. The authors derive the empirical equation from the data of power curve, which is provided by AEOLOS. Figure 4 indicates the relationship between wind and power, resulting that the power gener‐ ated by this wind turbine can be estimated by using the power curve. The power curve which

,,, , 59.075 62.619 33.433 (3 / 10 ) / *<sup>w</sup> h ave h ave h ave h ave P* =-- *U U U m* £ £ *m sUs* (15)

0 2 4 6 8 10 12 14 16 18 20

Wind speed *Uh,ave* [m/s]

, , 793.94 61.012 10 / 1 ( 9 / ) *<sup>w</sup> h ave m h ave* =- + < £ *U s U m sP* (16)

m/s.

is adopted in this study is as follows:

148 Global Warming - Causes, Impacts and Remedies

3 2

where *Pw* is the power [in W] of wind turbine.

0

10000

**Figure 4.** Power curve of wind turbine

20000

30000

Power of wind turbine

*Pw* [W]

40000

50000

60000

The power generated by PV system is calculated by using the following equation [39]:

$$E\_p = H \times K \times P\_p \div 1\tag{17}$$

where *Ep* is the annual electric energy of PV [in kWh], *H* is the amount of solar radiation [in kWh/(m2 )], *K* is the power generation loss factor, *Pp* is the system capacity of PV [in kW], 1 is the solar radiation intensity [in kW/m2 ] under standard state (AM1.5, solar radiation intensity: 1 kW/m2 , module temperature: 25 degree Celsius). In this study, the high performance PV HIT-B205J01 produced by Panasonic whose module conversion efficiency and maximum power per module are 17.4 % and 205 W, respectively is adopted for PV system [40]. The size of PV module is 1319 mm × 894 mm × 35 mm. *Pp* is calculated by installing this PV module on a roof of the building model, which is 67.7 kWp. *K* is calculated by using the following equation:

$$K = K\_p \times K\_m \times K\_i \tag{18}$$

where *Kp* is the power conversion efficiency of power conditioner, *Km* is the correction factor decided by module temperature, and *Ki* is the power generation loss by interconnection and dirty of module surface. In this study, *Kp* and *Ki* are set at 0.945 and 0.95, respectively. *Kp* is assumed by referring to the performance of commercial power conditioning device SSI-TL55A2 manufactured by Panasonic [41]. *Km* is calculated by the following equation:

$$K\_m = 1 - \frac{\left(T\_m - T\_s\right)}{100} \text{C} \tag{19}$$

where *Tm* is the PV module temperature [in degree Celsius], *Ts* is the temperature [in degree Celsius] under standard test condition (=25 degree Celsius), and *C*is the temperature correction factor [in %/degree Celsius] which is 0.35 [42]. *Tm* is calculated by using the following equation [43]:

$$T\_m = T\_a + \left(\frac{46}{0.41\mathcal{U}\_m^{0.8} + 1} + 2\right)I - 2\tag{20}$$

where *Ta* is the ambient air temperature [in degree Celsius], *Um* is the wind velocity [in m/s] over module of PV, *I* is solar radiation intensity [in kW/m2 ]. In this study, the meteorological data, such as solar radiation intensity, the ambient air temperature, and wind velocity of Tsu city in Japan are used from the data base METPV-11 provided by the New Energy and Industrial Technology Development Organization in Japan [44].

## **3. Results and discussion**

## **3.1. Wind speed distribution around buildings in built environment**

The contours of wind speed distribution in *x* direction (*U*) around the buildings for *U0*=10 m/ s at *z*=30 m are given in Figure 5 and they are on *x* – *y* cross section of the building. It shows the distribution of *U* in case of *β*=0 degree, (i.e. the model faces the main wind direction). In this model, *x*=0 m and *y*=0 m is located at the center of distance between the nearest edge of adjacent two buildings. In Figure 5, the black lines mean the separation lines, which distinguish the different calculation domain in the model used for numerical simulation. It has been observed that the wind is accelerated within the intervening space between the buildings because some wind is over the *U0* of 10 m/s. The contracted flow occurs by passing through two buildings located like nozzle.

To investigate the location point of wind turbine, the contours of wind speed *U* distribution for *U0*=10 m/s in the swept rotor area at the back of buildings for *x*/*L*=1.25, 1.875, and 2.50 on *y* – *z* cross section are analyzed and it is presented in Figure 6. In the Figure 6, the black cross line shows the blades of wind turbine, if the wind turbine is located there. The black block lines show the building wake position. Although the wind speed decreases in the building wake, the wind is accelerated within the intervening space between the buildings at the area of the back of buildings for *x*/*L*=1.25, 1.875, and 2.50.

**Figure 5.** Contour of wind speed *U* distribution around buildings at *z*=30 m on *x* – *y* cross section (*U0*=10 m/s)

The wind speed distribution in the swept rotor area of wind turbine is important for estimating the wind turbine power output. The frequency distribution of *U* in the swept rotor area at *x*/ *L*=1.25, 1.875, and 2.50 is given in Figure 7. It has been observed that the *U* > *U0* of 10 m/s is A Study on Assessment of Power Output by Integrating Wind Turbine… http://dx.doi.org/10.5772/58880 151

**3. Results and discussion**

150 Global Warming - Causes, Impacts and Remedies

two buildings located like nozzle.

*x*

*y*

back of buildings for *x*/*L*=1.25, 1.875, and 2.50.

**3.1. Wind speed distribution around buildings in built environment**

The contours of wind speed distribution in *x* direction (*U*) around the buildings for *U0*=10 m/ s at *z*=30 m are given in Figure 5 and they are on *x* – *y* cross section of the building. It shows the distribution of *U* in case of *β*=0 degree, (i.e. the model faces the main wind direction). In this model, *x*=0 m and *y*=0 m is located at the center of distance between the nearest edge of adjacent two buildings. In Figure 5, the black lines mean the separation lines, which distinguish the different calculation domain in the model used for numerical simulation. It has been observed that the wind is accelerated within the intervening space between the buildings because some wind is over the *U0* of 10 m/s. The contracted flow occurs by passing through

To investigate the location point of wind turbine, the contours of wind speed *U* distribution for *U0*=10 m/s in the swept rotor area at the back of buildings for *x*/*L*=1.25, 1.875, and 2.50 on *y* – *z* cross section are analyzed and it is presented in Figure 6. In the Figure 6, the black cross line shows the blades of wind turbine, if the wind turbine is located there. The black block lines show the building wake position. Although the wind speed decreases in the building wake, the wind is accelerated within the intervening space between the buildings at the area of the

**Figure 5.** Contour of wind speed *U* distribution around buildings at *z*=30 m on *x* – *y* cross section (*U0*=10 m/s)

The wind speed distribution in the swept rotor area of wind turbine is important for estimating the wind turbine power output. The frequency distribution of *U* in the swept rotor area at *x*/ *L*=1.25, 1.875, and 2.50 is given in Figure 7. It has been observed that the *U* > *U0* of 10 m/s is

**Figure 6.** Contour of wind speed *U* distribution at the area at the back of buildings for *x*/*L*=1.25, 1.875, 2.50 on *y* – *z* cross section (*U0*=10 m/s)

confirmed at the area at the back of buildings of *x*/*L*=1.25, 1.875, and 2.50. Additionally, it is known that the higher *U* is obtained near the buildings.

**Figure 7.** Frequency distribution of wind speed *U* in the swept rotor area at *x*/*L*=1.25, 1.875, and 2.50 (*U0*=10 m/s)

This study carries out the 3D simulation and investigates the wind speed distribution toward *y* direction as well as *x* direction in order to calculate *Uh,ave*. The frequency distribution of wind speed towards *y* direction (*V*) in the swept rotor area for *U0*=10 m/s at *x*/*L*=1.25, 1.875, and 2.50 are given in Figure 8. It is observed from Figure 8 that *V* is small compared to *U* shown in Figure 6. Therefore, it can be said that *Uh,ave* is decided by *Uave* mainly. The variations of *Uh,ave* in the swept rotor area at the back of buildings for *x*/*L*=1.25, 1.875, and 2.50 in case of *β*=0 degree with the different *U0* condition are given in Table 3. *Uh,ave* is estimated from the simulation results. It is seen that *Uh,ave* is greater than *U0* for each *U0* condition. Hence, it can be concluded that the proposed building model can provide the wind acceleration irrespective of *U0*. Considering the location point of wind turbine, the highest *Uh,ave* is obtained at *x*/*L*=1.25 under these investigation conditions. Therefore, this study has examined the wind turbine power generation performance for turbine location at *x*/*L*=1.25.

**Figure 8.** Frequency distribution of wind speed *V* in the swept rotor area at *x*/*L*=1.25, 1.875, and 2.50 (*U0*=10 m/s)


**Table 3.** *Uh,ave* in case of β=0 degree under different *U0* conditions

#### **3.2. Power generation performance of wind turbine located at proposed building layouts in actual area of Tsu city (Japan)**

In order to decide the location of the wind turbine in proposed building layouts, the wind directions are considered (Figures 2 and 3) in the actual area of Tsu city. Annual wind direction distribution in Tsu city is given in Figure 9 and it is based on the wind speed data provided by the Japan Meteorological Agency [37]. The hourly measurement data from 2007 to 2011 are used for estimation of annual wind direction distribution.

**Figure 9.** Annual wind direction distribution in Tsu city

It is observed from Figure 9 that the main wind direction throughout the year is North-West (NW). In this study, it is assumed that the open tip of building model is located as facing with the main wind direction (refer to Figure 3). West-North-West (WNW), North-North-West (NNW) and North (N) in addition to North-West (NW) are the wind directions, which can blow the wind among the buildings through nozzle. In this study, it is assumed that the wind blowing from the directions except for the above described five wind directions cannot be utilized for power generation of wind turbine. The different inflow angle conditions are considered in the simulation for finding the direction of wind in the proposed building layouts. As an example of the simulation results, the contours of wind speed *U* distribution around buildings for *U0*=10 m/s at *z*=30 m on *x*-*y* cross section for angular inflow conditions are given in Figure 10. Although the wind blows through the intervening space between the buildings, the wind acceleration is not high. Hence, the wind power generation through wind speeds coming from the main wind direction is important.

*U* 0 [m/s] 3.00 4.00 5.00 6.00 7.00 8.00 9.00 10.00 11.00 12.00 *U h, ave* at *x* /*L* = 1.25 [m/s] 3.23 4.32 5.41 6.50 7.58 8.67 9.76 10.84 11.93 13.02 *U h, ave* at *x* /*L* = 1.875 [m/s] 3.15 4.22 5.28 6.35 7.41 8.47 9.54 10.60 11.67 12.73 *U h, ave* at *x* /*L* = 2.50 [m/s] 3.04 4.07 5.10 6.13 7.16 8.19 9.22 10.25 11.28 12.31

**3.2. Power generation performance of wind turbine located at proposed building layouts in**

In order to decide the location of the wind turbine in proposed building layouts, the wind directions are considered (Figures 2 and 3) in the actual area of Tsu city. Annual wind direction distribution in Tsu city is given in Figure 9 and it is based on the wind speed data provided by the Japan Meteorological Agency [37]. The hourly measurement data from 2007 to 2011 are

N NNE NE ENE E ESE SE SSE S SSW SW WSW W WNW NW NNW

It is observed from Figure 9 that the main wind direction throughout the year is North-West (NW). In this study, it is assumed that the open tip of building model is located as facing with the main wind direction (refer to Figure 3). West-North-West (WNW), North-North-West (NNW) and North (N) in addition to North-West (NW) are the wind directions, which can blow the wind among the buildings through nozzle. In this study, it is assumed that the wind blowing from the directions except for the above described five wind directions cannot be utilized for power generation of wind turbine. The different inflow angle conditions are considered in the simulation for finding the direction of wind in the proposed building layouts. As an example of the simulation results, the contours of wind speed *U* distribution around buildings for *U0*=10 m/s at *z*=30 m on *x*-*y* cross section for angular inflow conditions are given in Figure 10. Although the wind blows through the intervening space between the buildings,

**Table 3.** *Uh,ave* in case of β=0 degree under different *U0* conditions

used for estimation of annual wind direction distribution.

**actual area of Tsu city (Japan)**

152 Global Warming - Causes, Impacts and Remedies

0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2

**Figure 9.** Annual wind direction distribution in Tsu city

**Frequency [-]**

**Figure 10.** Contour of wind speed *U* distribution around buildings at *z*=30 m on *x* – *y* cross section for angular inflow conditions (*U0*=10 m/s)

The hourly data on wind speed and direction are used as inputs in the simulation for finding the wind turbine power output at a location of wind turbine in the proposed building layouts. The daily power energy outputs of wind turbine, which is installed at the location of the proposed building layout, for months January, April, July, and October are given in Figures 11, 12, 13, and 14, respectively. These four months are considered as representative of four seasons.

**Figure 11.** Variation of wind energy output in January in case of installing proposed building layouts in Tsu city (*x*/ *L*=1.25)

**Figure 12.** Variation of wind energy output in April in case of installing proposed building layouts in Tsu city (*x*/ *L*=1.25)

**Figure 13.** Variation of wind energy output in July in case of installing proposed building layouts in Tsu city (*x*/*L*=1.25)

0

0

5

10

15

**Power energy [kWh]**

20

25

30

*L*=1.25)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

**Time [h]**

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

**Time [h]**

**Figure 13.** Variation of wind energy output in July in case of installing proposed building layouts in Tsu city (*x*/*L*=1.25)

**Figure 12.** Variation of wind energy output in April in case of installing proposed building layouts in Tsu city (*x*/

5

10

15

**Power energy [kWh]**

20

25

30

154 Global Warming - Causes, Impacts and Remedies

**Figure 14.** Variation of wind energy output in October in case of installing proposed building layouts in Tsu city (*x*/ *L*=1.25)

From these figures, it is observed that higher power energy of wind turbine can be obtained in the daytime irrespective of month/season. In addition, the higher amount of total wind energy through a day is obtained in January, while the lower amount of total wind energy through a day is obtained in July (in comparison with these four representative months). The main wind direction in July is East-South-East (ESE) and it is shown in Figure 15, while the main wind direction throughout the year is North-West (NW). In this study, it is assumed that the winds blowing from the restricted wind directions can be utilized for power generation from wind turbine. The restricted wind directions mean five directions whose center is the main wind direction, and the other four directions are located symmetry to the main wind direction. The amount of wind energy production is estimated to be zero for the wind blowing from the other directions. The monthly main wind direction is East-South-East (ESE) in July as shown in Figure 15, which is different from the annual main wind direction. The frequency of wind blowing from the restricted five directions is small in July, resulting that the wind energy production is lower compared to the other months. Therefore, while selecting the building orientations, it is very important to consider the wind speed directions in order to maximize annual wind energy production.

**Figure 15.** Frequency distribution of wind direction in July in Tsu city

#### **3.3. Power generation performance of PV system located at proposed building layouts in actual area of Tsu city (Japan)**

To maximize the power output of PV system, it is important to set the tilt angle of solar panel normal to the solar orbit. Hence, this study investigates the optimum tilt angle for installing PV system on roof of the proposed building model in Tsu city. By examining the data of solar radiation intensity in Tsu city for five year from 1999 to 2009 [44], the best tilt angle is 35 degree (Figure 16). Therefore, this study estimates the power output of PV system installed on roof of a building whose tilt angle is 35 degree. In addition, this study assumes that the PV arrays face to the south.

**Figure 16.** Effect of tilt angle of PV array assumed to be installed in Tus city on amount of annual solar radiation intensity

The daily power outputs from PV system, for months January, April, July, and October, are given in Figures 17, 18, 19, and 20, respectively. The hourly meteorological measurement data of solar radiation intensity from 1990 to 2009 [44] and that of temperature and wind velocity from 2010 to 2011 [37] which are averaged among all days in each month are used for estimation of daily power outputs of PV system. U0 = 3.00 - 12.00 m/s Density of wind at inlet 1.166 kg/m<sup>3</sup> Temperature of wind at inlet 293 K Pressure of wind at inlet 0.1 MPa

Proof Corrections Form

Chapter Title: A Study on Assessment of Power Output by Integrating Wind Turbine and

Photovoltaic Energy Sources with Futuristic Smart Buildings

PROOF CORRECTIONS FORM

Figures 17, 18, 19, and 20 provide the output of PV system with respect to the time. Though the solar radiation intensity in July is high, the power generation from PV system is reduced due to high temperature (as explained by Eq. (19)). Because the solar radiation is relatively high and temperature is not high in April compared to the other months/seasons, therefore the PV system has good performance in month April. Kinematic viscosity coefficient of wind 1.56×10-5 m 2 /s Wind speed at inlet <sup>U</sup> = U0×(z/30)0.25 m/s (U <sup>0</sup> = 3.00 - 12.00 m/s) Slip on side wall of building <sup>V</sup> = (0.41×|l|)0.25<sup>U</sup>

**Figure 17.** Variation of PV energy output in January in case of installing in Tsu city

Time [h]

Power energy, Electric consumption [kWh]

0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2

**actual area of Tsu city (Japan)**

3000

3500

4000

4500

Amount of annual solar radiation intensity

[MJ/m2]

5000

5500

6000

face to the south.

**Figure 15.** Frequency distribution of wind direction in July in Tsu city

**Frequency [-]**

156 Global Warming - Causes, Impacts and Remedies

N NNE NE ENE E ESE SE SSE S SSW SW WSW W WNW NW NNW

Author(s) Name(s): Akira Nishimura

5th row, 2nd column:

U0 = 3.00 12.00 m/s

Table caption:

Time h]

Title of horizontal axis:

Title of right vertical axis:

Title of right vertical axis:

Self-sufficient ratio [ ]

Self-sufficient ratio [ ]

Table 2. Specification of wind turbine

**3.3. Power generation performance of PV system located at proposed building layouts in**

Page No.

6 Table

1.

Line

To maximize the power output of PV system, it is important to set the tilt angle of solar panel normal to the solar orbit. Hence, this study investigates the optimum tilt angle for installing PV system on roof of the proposed building model in Tsu city. By examining the data of solar radiation intensity in Tsu city for five year from 1999 to 2009 [44], the best tilt angle is 35 degree (Figure 16). Therefore, this study estimates the power output of PV system installed on roof of a building whose tilt angle is 35 degree. In addition, this study assumes that the PV arrays

9 Table

19 Figur

2.

e 18.

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90

Tilt angle [degree]

e 21.

**Figure 16.** Effect of tilt angle of PV array assumed to be installed in Tus city on amount of annual solar radiation intensity

22 Figur

22 Figur

e 22.

1 2 3 4 5 6 7 8 9 101112131415161718192021222324

Self-sufficient ratio of combined power generation system

Time [h]

1

Self-sufficient ratio [%]

**Figure 18.** Variation of PV energy output in April in case of installing in Tsu city

Self-sufficient ratio [%]

Wind turbine Photovoltaic

Self-sufficient ratio [%]

Electric consumption

Combined power generation system

**Figure 19.** Variation of PV energy output in July in case of installing in Tsu city

**Figure 20.** Variation of PV energy output in October in case of installing in Tsu city

#### **3.4. Investigation on performance of renewable energy supply system in built environment**

This study investigates the power generation performance of a wind turbine and PV system integrated with two buildings and the power supply characteristics from this combined power generation system with electric demand are presented. In the building model of this study, one wind turbine and two PV arrays of 67.7 kWp per two buildings are assumed to be installed. As described above, by using the meteorological data [37, 44], the daily power outputs of wind turbine and PV system for each month throughout a year and annual power outputs from them are estimated. As a result, the annual electrical energy production of combined power generation system is 153 MWh. A typical monthly electric consumption for a household in Japan (for year 2012) is 276 kWh [45], resulting that the annual electric consumption for the proposed building model, which has 80 households per two buildings, is 265 MWh. Therefore, this combined power generation system can cover the 57.7% of the annual electric consumption of households assumed to be living in the building model. In the future study, the change of self-sufficient ratio with time will be investigated by collecting the statistical data of electric consumption.

To compare the power supply characteristics of this combined power generation system with the electric demand characteristics by the other consumer, the energy data base of Mie university [46] which is located in Tsu city is adopted. The data of daily electric consumption in weekday from 2010 to 2011 is used from this data base. Then, the daily data of a represen‐ tative one day in each month is estimated by averaging all daily data through week days in each month. In this estimation, the electric consumption of Mie university per floor space is calculated from the data base and applied to the floor space of the building model, resulting that the amount of daily electric consumption for this building model is derived.

0

0

0.02 0.04 0.06 0.08

Power energy [kWh/m²]

0.1

0.12

**Figure 19.** Variation of PV energy output in July in case of installing in Tsu city

**Figure 20.** Variation of PV energy output in October in case of installing in Tsu city

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Total amount of power energy in a day: 0.64kWh/m2

Time [h]

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Total amount of power energy in a day: 0.54kWh/m2

Time [h]

**3.4. Investigation on performance of renewable energy supply system in built environment**

This study investigates the power generation performance of a wind turbine and PV system integrated with two buildings and the power supply characteristics from this combined power generation system with electric demand are presented. In the building model of this study, one wind turbine and two PV arrays of 67.7 kWp per two buildings are assumed to be installed.

0.02 0.04 0.06 0.08

Power energy [kWh/m²]

0.1

0.12

158 Global Warming - Causes, Impacts and Remedies

Figures 21, 22, 23, and 24 show the daily power outputs of wind turbine, PV system, and combined them for months January, April, July, and October. Additionally, the daily selfsufficient ratio of combined power generation system, which is a ratio of electrical energy generation to electric consumption, is also shown. From these figures, it is clear that the electrical energy of combined power generation system as well as that of wind turbine or PV system in daytime is larger than nighttime. As the electric consumption of university in daytime is also larger than nighttime, the characteristics of power supply by combined power generation system matches the characteristics of electric consumption of university. The selfsufficient ratio of combined power generation system in daytime is approximately 20 – 60 %, while that in nighttime is below 20 %. The average self-sufficient ratios of combined power generation system in all the day for January, April, July, and October are 23.3 %, 30.0 %, 15.7 %, and 19.9 %, respectively. The highest self-sufficient ratio of combined power generation system is obtained in April. Because April is the moderate season in Japan, the electric consumption is relatively small compared to summer and winter. In addition, the amount of power energy of PV system in April is higher compared to the other months/seasons as described above. Consequently, the self-sufficient ratio of combined power generation system in April is the best. On the other hand, the self-sufficient ratio of combined power generation system in July is lower compared to the other months/seasons. In July, the electric consumption is high due to hot season in Japan. In addition, the power energy of combined power generation system is low because the meteorological condition is not good for wind turbine and PV system as described above. Therefore, the self-sufficient ratio of combined power generation system in July shows the small value relatively.

0.02 0.04 0.06 0.08 0.1 0.12

Power energy[kWh/m²]

Time [h]

Proof Corrections Form

PROOF CORRECTIONS FORM

Chapter Title: A Study on Assessment of Power Output by Integrating Wind Turbine and Photovoltaic Energy Sources with Futuristic Smart Buildings

No. Delete Replace with

Author(s) Name(s): Akira Nishimura

5th row, 2nd column:

U0 = 3.00 12.00 m/s

Table caption:

Time h]

Title of horizontal axis:

Title of right vertical axis:

Title of right vertical axis:

Title of right vertical axis:

Title of right vertical axis:

Self-sufficient ratio [ ]

Self-sufficient ratio [ ]

Self-sufficient ratio [ ]

Self-sufficient ratio [ ]

Table 2. Specification of wind turbine

Page No.

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9 Table 2.

19 Figur

22 Figur

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23 Figur

23 Figur

e 24.

e 23.

e 22.

e 21.

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Line

## Self-sufficient ratio [%]

U0 = 3.00 - 12.00 m/s

Kinematic viscosity coefficient

Density of wind at inlet 1.166 kg/m<sup>3</sup> Temperature of wind at inlet 293 K Pressure of wind at inlet 0.1 MPa

of wind 1.56×10-5

Slip on side wall of building <sup>V</sup> = (0.41×|l|)0.25<sup>U</sup> Turbulent flow model Standard k -ε model Turbulent energy 0.025 m<sup>2</sup>

Dissipation rate (1.58×10-3)/z m<sup>2</sup>

Calculation number 10000 Residue of each parameter <1.0×10-5 Calculation state Steady state

Table 2. Specifications of wind turbine

Wind speed at inlet <sup>U</sup> = U0×(z/30)0.25 m/s

 m2 /s

/s2

/s2

(U <sup>0</sup> = 3.00 - 12.00 m/s)

Time [h]

Total amount of power energy in a day: 0.71kWh/m<sup>2</sup>

Self-sufficient ratio [%] **Figure 21.** Variation of energy output of each power generation system and self-sufficient ratio of power generation to electric consumption in January

1

Self-sufficient ratio [%]

Self-sufficient ratio [%]

Self-sufficient ratio [%]

2

Self-sufficient ratio [%] 40 60 80 100 120 140 160 180 200 220 Power energy, Electric consumption [kWh] Wind turbine Photovoltaic Combined power generation system Electric consumption Self-sufficient ratio of combined power generation system

0 20

> Wind turbine Photovoltaic

Self-sufficient ratio [%]

Wind turbine Photovoltaic

Electric consumption

Combined power generation system

Electric consumption

Power energy, Electric consumption [kWh]

Power energy, Electric consumption [kWh]

Self-sufficient ratio of combined power generation system

Combined power generation system

1 2 3 4 5 6 7 8 9 101112131415161718192021222324

Time [h]

Self-sufficient ratio of combined power generation system

1 2 3 4 5 6 7 8 9 101112131415161718192021222324

Time [h]

1 2 3 4 5 6 7 8 9 101112131415161718192021222324

Time [h]

0 20

20

Self-sufficient ratio [%]
