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Jordi Macià, EURECAT Technology Centre of Catalonia, Barcelona, Spain

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

Publié dans : Technologie
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Jordi Macià, EURECAT Technology Centre of Catalonia, Barcelona, Spain

  1. 1. REFER: Cost Effective BIPV Design Optimization Methodology Jordi Macià Eurecat, Technologic Centre Manresa, Catalonia
  2. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 17. https://timepac2019.blogspot.com If you would like to have more information about this presentation, please contact jordi.macia@eurecat.org

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