Laboratoire des systèmes électriques et industriels

Slides:



Advertisements
Similar presentations
PhotoVoltaic System Sizing © ARJ This is not a How-To presentation. It is a What and Why presentation.
Advertisements

Supercapacitor Energy Storage System for PV Power Generation
Copyright © 2010 The Alpha Group. All Rights Reserved. 2) Headends and Hubs.
Rooftop Solar Systems Rooftop Solar System (Off-Grid) Reliance Solar Energy™, Ratnagiri.
May 11, The role of electric mobility in future Energy Systems Dr. ir. Zofia Lukszo With collaboration with dr. Remco Verzijlbergh Section Energy.
Master Plan of Electricity Supply for off-grid islands in Sundarbans Indradip Mitra November 2005.
I R H Simulink Modelling and Simulation of a Hydrogen Based Photovoltaic/Wind Energy System Mamadou Lamine Doumbia, Kodjo Agbossou, and Évelyne Granger.
Team Members: Justin Schlee Brendin Johnson Jeff Eggebraaten Anne Mousseau Preliminary Design Review.
RENEWABLE ENERGY INTEGRATION IN OFF-GRID APPLICATIONS:
Lesson 25: Solar Panels and Economics of Solar Power
Solar Home UPS 850VA & 1400VA India’s first Sine wave inverter with in built Solar Charge Controller and Controlled DC Load Output. Simultaneous Charging.
Electricity Compare AC and DC electrical current and understand their important differences Explain the relationship between volts, amps, amp-hour, watts,
Renewable Energy as Priority
Solar Powered Charging Station: Mid-Term Presentation Design Team: Ben Hemp Jahmai Turner Rob Wolf, PE Sponsors: Conn Center for Renewable Energy Dr. James.
21 st May 2015 SMARTGREENS 2015 Lisbon, Portugal Lane Department of Computer Science and Electrical Engineering West Virginia University Analyzing Multi-Microgrid.
Hybrid Wind & Solar Generation Project
Designing Solar PV Systems (Rooftops ). Module 1 : Solar Technology Basics Module 2: Solar Photo Voltaic Module Technologies Module 3: Designing Solar.
October 25, 2002ENO Presentation1 Frederick M. Ishengoma Dept. of Electrical Power Eng. NTNU Stand-alone PV power supply for developing countries.
Frankfurt (Germany), 6-9 June 2011 Presenter: Mahdi Kiaee Supervisors: Dr. Andrew Cruden and Professor David Infield The University of Strathclyde, Glasgow.
Prajwal K. Gautam, Dept. of Electrical and Computer Engineering Dr. Ganesh K. Venayagamoorthy, Dept of Electrical & Computer Engineering Dr. Keith A. Corzine,
WILLIAM KIEWICZ-SCHLANSKER LAFAYETTE COLLEGE LiFePO4 Battery Pack Per-Cell Management System.
Sizing & Data Capturing For A Vacation Home
Statistical Tools for Solar Resource Forecasting Vivek Vijay IIT Jodhpur Date: 16/12/2013.
Solar Powered Charging Station: Mid-Term Presentation Design Team: Ben Hemp Jahmai Turner Rob Wolf, PE Sponsors: Conn Center for Renewable Energy Dr. James.
August 30, 2012 Modeling Solar, Storage and Inverter based resources John Adams Resource Integration Planning Working Group.
Optimization of PHEV/EV Battery Charging Lawrence Wang CURENT YSP Presentations RM :00-11:25 1.
Fearghal Kineavy 4 th Energy Systems Engineering – Electrical Stream Department of Electrical and Electronic Engineering, NUIG Supervisor: Dr Maeve Duffy.
MIGRATING TOWARDS A SMART DISTRIBUTION GRID Authors: Prashanth DUVOOR Ulrike SACHS Satish NATTI Siemens PTI.
Factors influencing the conversion efficiency of a PV module Vaal University of Technology Augustine Ozemoya Vaal University of Technology Augustine Ozemoya.
Operation and Control Strategy of PV/WTG/EU Hybrid Electric Power System Using Neural Networks Faculty of Engineering, Elminia University, Elminia, Egypt.
1\ Backup system for solar on-grid installation 1 1pcs XTM inverter charger, nominal power 3.5KVA, 30min. Power 4KVA AC DC Load2 (secured power)
البحث الأول بحث مشترك منشور فى مجلة محكمة ذات معامل تأثير مرتفع نسبيا داخل التخصص ( معامل تأثير =2.932 ) International Journal of Innovative Computing,
To validate the proposed average models, our system was simulated with Matlab Simulink in near-real- time. The wireless communication architecture was.
SIZING OF ENERGY STORAGE FOR MICROGRID
Carnegie Mellon University Solar PV and Energy Storage for Commercial & Industrial Customers Shelly Hagerman, Paulina Jaramillo, Granger Morgan, Jay Whitacre.
Unidad 15 Introduction to solar PV energy - Dimensioning - Alberto Escudero-Pascual, IT+46 (cc) Creative Commons Share-Alike Non Commercial Attribution.
Assessment and Design of Rooftop Solar PV system
Estimating the resource adequacy value of demand response in the German electricity market Hamid Aghaie Research Scientist in Energy Economics, AIT Austrian.
Photovoltaic and Battery Primer
Photovoltaic and Battery Primer
Institute for Energy and Transport
Asst. Prof. Dr. Sameer Saadoon Algburi
DESIGN OF PV SYSTEM INTERCONNECTED WITH EU
A Microcontroller Based Power Management System for Standalone Micro grids With Hybrid Power Supply 1.
Optimization of multifluid microgrids, the REIDS project in Singapore
Standalone Photovoltaic System Sizing Based On Different Approaches
Photovoltaic Systems Engineering Session 15 Stand-Alone PV Systems
Photovoltaic Systems Engineering Session 22 Solar+Storage Systems
What is MPPT and why it is needed? (Maximum power point tracking)
System Control based Renewable Energy Resources in Smart Grid Consumer
DESIGN AND SIMULATION OF GRID CONNECTED
NATIONAL TECHNICAL UNIVERSITY OF ATHENS
Basics of interfacing PV to the Grid
Power Flow Interactive Sharing between Two DC Nanogrids Photovoltaic Local Branch Dynamic Systems at Island Operation Maged F. Bauomy1 , Haytham Gamal2,
NATIONAL TECHNICAL UNIVERSITY OF ATHENS
The Management of Renewable Energy
EE5900: Cyber-Physical Systems
Power Electronics Research at Seoul National University
Optimization of PHEV/EV Battery Charging
Photovoltaic (PV) Systems
OPERATION CONTROL STRATEGY AND SIMULATION OF PV SYSTEM
MIGRATING TOWARDS A SMART DISTRIBUTION GRID
2500 R Midtown Sacramento Municipal Utility District
“DESIGN OF GRID-CONNECTED PV SYSTEM”
Photovoltaic Systems Engineering Session 16 Solar+Storage Systems
Case Studies on Field Deployment of PV Battery Storage Systems
THE STUDY OF SOLAR-WIND HYBRID SYSTEM PH301 RENEWABLE ENERGY
ANALYSIS, DESIGN & ESTIMATION OF residential building with p.v installation under the guidance of Mr. S.Bhanu Prakash, M.Tech Assistant Professor Department.
Arslan Ahmad Bashir Student No
Components inverters Except where otherwise noted these materials are licensed Creative Commons Attribution 4.0 (CC BY)
Presentation transcript:

Laboratoire des systèmes électriques et industriels Institute of Energy System Technology Genetic Algorithms-based Battery Predictive Management in INES Smart Grid System Merci monsieur le président بسم الله الرحمان الرحيم Monsieur le président , madame, messieurs les membres de jury, honorable assistance, j’ai le plaisir de vous présenter mon travail dans le cadre d’une soutenance du mémoire de magistère intitulé par: Stockage et récupération d’énergie dans un système multi-sources, application au véhicule électrique Supervisors Dr. A A LADJICI Pr. E BOLLIN PhD student Mustapha HABIB

PhD main axis Forecasting tools for PV power and building power demand: ANN, ANFIS Short-term power management: frequency/voltage control Long-term power management: Daily power dispatching of grid connected hybrid system Experimental validation of battery predictive management: Developing a control algorithm for XTM 4000-48 Xtenders for batteries daily power management

Predictive controller Forecasting tools: ANN and fitting tools Control variables: grid energy and battery SOC System model: power flow equation Actuator variables: battery current and grid relay Cost function and solver: genetic algorithms

Forecasting tools

PV power forecasting with ANN Time Series tool Forecasting tools PV power forecasting with ANN Time Series tool Prediction horizon Prediction precision 6 past steps 1 step ahead

Forecasting tool PV power forecasting with ANN Time Series tool

ANN forecasting system Forecasting tools ANN-based building forecasting tool ANN forecasting system Hour of day Day of week Building power demand Temperature Holyday indicator

Forecasting tools ANN-based building forecasting tool

Forecasting tools Building power demand forecasting with fitting tool

Building power demand forecasting with fitting tool Forecasting tools Building power demand forecasting with fitting tool Polynomial Sum of sin Coefficient Value a1 1340 b1 0.1405 c1 -0.1949 a2 624.8 b2 0.2942 c2 2.055 a3 239 b3 0.5406 c3 2.274 a4 87.47 b4 0.9153 c4 -2.906 a5 55.9 b5 1.773 c5 -3.962 a6 48.87 b6 1.346 c6 0.1323 Coefficient Value a9 1.472e-06 a8 -0.0001152 a7 0.003223 a6 -0.03179 a5 -0.1718 a4 6.069 a3 -46.94 a2 163 a1 -290.3 a0 511.4 RMSE=74.96 R=0.9714 RMSE=51.91 R=0.9863

Control variables

3. Battery charging mode: the Xtender works as charger Control variables Grid energy 1. Power network energy 1. Grid feeding mode: the Xtender works as inverter SCI (current source inverter) 2. Island mode: the Xtender works as inverter VSI (voltage source inverter) 3. Battery charging mode: the Xtender works as charger

Control variables SOC (%) Battery SOC 90 2. Battery SOC 50 1. Maximum limit (90%): keep enough capacity to absorb any excess PV power in island mode 2. Minimum limit (50%): to save batteries life time by minimizing DOD level

System model

Genetic Algorithms-based Predictive Management System model Power flow equation in grid-connected mode Power flow equation in island mode Battery current calculation Battery SOC estimation

Actuator variables

Genetic Algorithms-based Predictive Management Power topology and the role of GA-PM

Cost function and solver

Genetic Algorithms-based Predictive Management Cost function Actuator variables : grid relay and battery current Battery and power converter efficiency Weighting coefficients Battery voltage Reduce the used grid energy/increase the energy injected into the grid Prediction of the evolution of battery current (SOC) in island mode Giving priority to the island mode compared to grid-connected mode

Genetic Algorithms-based Predictive Management System constraints

Global GA-PM Algorithm

Genetic Algorithms-based Predictive Management Global control algorithm MATLAB program

Experimental results

INES smart grid parameters Experimental results INES smart grid parameters Component Subcomponent Parameter Value PV system PV module Power at MPP 240 Wp Voltage at MPP 30.0 V Current at MPP 8.1 A Open circuit voltage 37.4 V Short circuit current 8.6 A Temperature coefficient -0.46 %/K Module model Bosch solar module c-Si M 60 PV power plant Number of modules 27 Inclination 9 x 35° 18 x 30° Alignment 180° south Power 6.3 KWp Batteries system Battery cell Voltage 4 V Nominal capacity 546 Ah Battery model Rolls Battery 4CS17P Batteries bank Number of cell in series 12 Number of cells in parallel 1 4.5 KW Programmable load - Nominal power 3.6 KW Load mode Constant power Control mode Remote Model Chroma 63803

Experimental results Reference method: rules-based power management

Experimental results Rules-based power management June 8th (sunny day)

June 14th (sunny day) Experimental results GA Predictive power management June 14th (sunny day)

21.36 % !! ΔE=580 Wh Economy of 6.23 % for just 7 hours Simulation results with MATLAB/Simulink Comparison Power management technique Produced PV energy (EPV) Consumed Energy (ELoad) Exchanged energy with grid (Eg) EPV/ELoad Estimated exchanged energy with grid when EPV/Eload = 1 Rules management 34.11 KWh 27.88 KWh -10.65 KWh 1.22 -8.72 Predictive management 31.9 KWh 27.88 -10.61 1.14 -9.30 ΔE=580 Wh Economy of 6.23 % for just 7 hours Estimated economy in the case of 24 hours 21.36 % !!

June 9th (cloudy day) Experimental results GA Predictive power management June 9th (cloudy day)

System model is not required Advantages and disadvantages Rules-based power management Simple algorithm System model is not required The control behavior is not on real time

On-line control behavior Advantages and disadvantages Predictive-based power management On-line control behavior Energy economy and battery SOC control performance The effectiveness depends highly on system model exactitude and on weather forecasting accuracy

Fuzzy-based power management

PLoad-PPV Grid relay SOC Battery current Electricity price Fuzzy-based power management Controller structure PLoad-PPV Grid relay SOC Battery current Electricity price MATLAB program

45 Rules ! Fuzzy-based power management Fuzzy rules PLoad-PPV Controller response dP++ dP+ dP0 dP- dP--   EP-L SOC-H P+ R1 R0 SOC-M P- SOC-L P-- EP-M P++ P0 EL-H Energy price SOC Rules Battery charging mode 18 Rules ! 45 Rules ! Grid feeding mode 18 Rules ! 09 Rules ! Island mode

Fuzzy-based power management Fuzzy rules 1. Inject more power into the grid when the energy price is high 2. Using the grid power to charge batteries when the energy price is low 3. When a low difference between building power demand and supplied PV power is detected an island mode is to be applied

Fuzzy-based power management Surfaces

Forecasted energy price in Germany for July 27th 2017 Fuzzy-based power management Energy price profile (€/MWh) Forecasted energy price in Germany for July 27th 2017 © http://www.nordpoolspot.com

Fuzzy-based power management Experimental results

Perspective works

Perspective works Repeat the same tests by introducing wind energy in the local network Improve the PV and building power forecasting tools: using on-line weather forecast with ANN Taking the batteries aging in consideration Taking the frequency/voltage control in consideration Using other solvers for the cost function: PSO Using INES offices as a load when doing new tests

Thank You for your Attention Any Question !