Presentation 3, Session 3 "Cost Effective BIPV Design Optimization Methodology"

1. REFER: Cost Effective BIPV Design
Optimization Methodology
Jordi Macià
Eurecat, Technologic Centre
Manresa, Catalonia

2. Introduction
A novel methodology to design and optimize BIPV installations.
This methodology is based on intelligent optimization algorithms that determine the
optimal technologies and sizing of photovoltaic panels, presented as an easy to use tool for
non-advanced users.
It integrates different modular technologies developed by Eurecat.
Project COMRDI15-1-0036
Funded by:
• Generalitat de Catalunya (Catalan government,
via research agency ACCIÓ)
• European Union (EU), FEDER.
RIS3CAT - ENERGIA
Project: REFER. Energy Reduction and Flexibility in Building Retroftting. 2015 - 2019

3. Introduction
Methodology Scheme:
1. Building Energy
Demand Forecast
• Few input parameters
• Mathematical modelling of
demand
• Output: Heating, Cooling,
HTW and Electricity. Hourly
annual demand profiles.
Feeds
demand
into
optimizer
4. Optimization Algorithms
• Optimization criteria: meet demand, maximum
production, minimum cost.
• Type of installation: Fixed, roof integrated, etc.
Feeds
specs
3. PV DDBB
• Technologic
database
• Electric
parameters
• Cost
• Efficiency2. PV Panel Simulation
• Incident solar radiation
• Electric simulation (MPPT)
• Thermal simulation (Tcell)
ITERATIVE
Orientation,
slope, electric
parameters,
cost, etc.
OUTCOME
Design of PV installation
PV technology model, #of panels, slope, azimuth, etc.

4. Introduction
Market target:
This methodology is oriented to ESCOs, engineering services, installers, etc. as a quick,
easy to use and accurate design tool for PV installations, as a cost effective inversion
selective method.
Some screenshots of the testing software tool
In order to demonstrate the
methodology, a testing
software tool has been
developed for testing and
demonstration purposes only,
based on Matlab.

5. Real data used to
adjust models to
reality
Mathematical
model creation
Energy Demand Calculation Engine (EDCE)
Energy Simulation Data Base
Based on Energy Plus
Quick forecast of energy demand:
• Heating
• Cooling
• Hot Tap Water (HTW)
• Electricity.
Φ 𝐻 = 𝑓(𝑇, 𝑋1, 𝑋2,… )
1. Building Energy demand forecast
Development of calculation tool based on mathematical models able to predict the energy
demand of a building with few input parameters.
These models can be used to minimize the energy demand of a building by means of an
accurate design of the PV and HVAC systems.
With monitoring and data collection from existing buildings, models can be improved and
adjusted to the demand forecasting.
 Location (weather)
12 climatic zones (CTE) for
all the iberic peninsula: A3,
A4, C1, etc.
 Dedication of the building
Tertiary, Residential, etc.
 Shape and dimensions
Concavity, shape factor
 Occupancy and users
Schedules, setpoints, activity
 Thermal insulation
Facades, openings, ground,
roof, etc.
 Solar protection
Blinds, shadings, fixed,
mobile, etc.
 Glazing (%)
Glass to wall ratio
Real building monitoring data

6. Energy Demand Calculation Engine (EDCE)
1. Building Energy demand forecast
Output example from EDCE: annual hourly profile of thermal
demand for a given building

7. 2. Photovoltaic Panel Simulation
Simulation of the electrical and thermal behaviour of a PV panel.
For BIPV purposes, it is important to simulate:
• The panel incident radiation (HDKR model);
• The cell temperature, which depending on BIPV application it can change significantly;
• PV electrics. Maximum power point tracking (MPPT) V, I, for each incident radiation and cell Temperature.
Thermal Model
𝑇𝑐𝑒𝑙𝑙
º
𝑆 𝑛𝑜𝑟𝑚
𝑇𝑎𝑚𝑏
º
Incident Rad.
𝐿𝑜𝑐
𝑇𝑖𝑚𝑒 𝑆𝑖𝑛𝑐𝑖𝑑𝑒𝑛𝑡
[𝛽 𝛾]
PV model
𝑆𝐼𝑛𝑐𝑖𝑑𝑒𝑛𝑡
𝑇º 𝑐𝑒𝑙𝑙 𝐼 𝑚𝑜𝑑
𝑉 𝑚𝑜𝑑
MPPT
𝑃 𝑀𝑃𝑃𝑇𝐼 𝑚𝑜𝑑
𝑉 𝑚𝑜𝑑
𝑃 𝑀𝑃𝑃𝑇
Inverter
𝑃𝐼𝑁𝑉𝑃 𝑀𝑃𝑃𝑇
Panel iterative simulation diagram

8. 2. Photovoltaic Panel Simulation
Simulation of the electrical behaviour of a PV panel
• Mathematical models for PV panels according using equivalent circuit:
• Manufacturer data + electric model (Vo, Io, Rs, Tc, etc.)
• Models parametrized for:
• Orientation, space consumption
• Thermal efficiency
• Investment and operation costs
𝐼 = 𝐼 𝑝ℎ − 𝐼0 𝑒
𝑞 𝑉+𝑅 𝑠 𝐼
𝐴 𝐾 𝑇 − 1 −
𝑉 + 𝑅 𝑠 𝐼
𝑅 𝑠ℎ
PV Equivalent Circuit Model
Behaviour Eq.
PV model
𝑆𝑖𝑛𝑐
𝑇º 𝑐𝑒𝑙𝑙 𝐼 𝑚𝑜𝑑
𝑉 𝑚𝑜𝑑
Analytical Model
Boundary
conditions
Panel specs
Theoretical
response
Panel datasheet
Response: I/V curves
PV
BBDD

9. 2. Photovoltaic Panel Simulation
Calculation of the incident solar radiation on a panel.
Model used: HDKR
It includes:
• Beam radiation
• Isotropic diffuse radiation
• Horizon radiation
• Ground reflected radiation
HDKR model
Eq.: Total incident solar radiation (HDKR model)

10. 2. Photovoltaic Panel Simulation
Calculation of the Cell Temperature (Tcell)
Model used: Duffie and Beckman (1991)
It takes into account:
• Radiation absorbed
• Radiation transformed to electricity
• Thermal characteristics of the panel
• Thermal environment of the panel
Eq.: Cell Temperature for free standing panels
(Duffie & Beckman model)
Thermal model for BIPV
PV panel
Thermal
insulation
Flow of the
ventilated
chanel

11. 3. PV DDBB
Data base of the PV technologies. The optimization algorithm selects the optimal PV panel
model for each case.
For each model is included:
• Electrical parameters: V, I for NOCT, STC.
• Peak power (kWp)
• Efficiency
• Dimensions
• Cost
Screenshot of the PV technologies database, in excel before being loaded into the software

12. 4. Optimization algorithms
Optimization algorithms try to find the optimal:
• PV panel technology
• Model: electrical specs.
• Mono/polycristal
• Slope and azimuth of the panel (if possible)
• PV array set
Optimization criteria
a) Meet the building energy demand + min (investment €)
b) max( PV production) + min (investment €)
c) max( PV production) + limit(investment €)

13. 4. Optimization algorithms
Types of installation considered to be optimized:
• Fixed rooftop
• Tile roof integrated
• BIPV
• Façade (opaque wall, curtain wall)
• Openings (windows)
• Solar protections (blinds, cover,etc.)
Opaque façade
integrated
Fixed rooftop
Tile roof integrated
Curtain wall integrated
Sun cover integrated
Blind integrated

14. 4. Optimization algorithms
Algorithm’s structure
 MINL Optimization algorithm based on Genetic Algorithms:
Evaluation of the
termination criteria
Generation of the
initial population
Return of the best
individual
Application of the
genetic operators
Fitness calculation
for each individual
Met
Not met
}1,0{,, 

PPPpopulation bitspop NN
• Discrete values
• Continuous values

15. 4. Optimization algorithms
The output of the optimization algorithm, is:
Optimization process
Progress of optimization of
orientation and slope
Evaluation of PV productivity as a function
of slope and azimuth
• The selection of PV model technology
• Number of panels to install
• Slope and azimut of the panels (not for BIPV)
• Electric production
• Solar radiation collected
• Investment (€)

16. References
Gairaa, Kacem & Khellaf, Abdallah & Chellali, Farouk & Benkaciali, Said & Bakelli, Yahia &
Bezari, Salah. (2015). Maximisation and Optimisation of the Total Solar Radiation Reaching
the Solar Collector Surfaces. 10.1007/978-3-319-17031-2_57.
Duffie, J. and Beckman, W. (2006). Solar engineering of thermal processes. Hoboken, N.J.:
John Wiley & Sons.
Kun Ding, XinGao Bian, HaiHao Liu, and Tao Peng.”A MATLAB-Simulink-Based PV Module
Model and Its Application Under Conditions of Nonuniform Irradiance” in IEEE
TRANSACTIONS ON ENERGY CONVERSION, VOL. 27, NO. 4, DECEMBER 2012
E. I. Batzelis, "Simple PV Performance Equations Theoretically Well Founded on the Single-
Diode Model," in IEEE Journal of Photovoltaics, vol. 7, no. 5, pp. 1400-1409, Sept. 2017.
M. Moeini-Aghtaie, P. Dehghanian, M. Fotuhi-Firuzabad and A. Abbaspour, "Multiagent
Genetic Algorithm: An Online Probabilistic View on Economic Dispatch of Energy Hubs
Constrained by Wind Availability", in IEEE Transactions on Sustainable Energy, vol. 5, no. 2,
pp. 699-708, 2014. doi: 10.1109/TSTE.2013.2271517

17. https://timepac2019.blogspot.com
If you would like to have more information
about this presentation, please contact
jordi.macia@eurecat.org