UNIVERSITE DE TECHNOLOGIE DE COMPIEGNE. Research Unit AVENUES EA Urban DC Microgrids. Modeling, Optimization and Real-Time Control
|
|
- Dominic Golden
- 5 years ago
- Views:
Transcription
1 UNIVERSITE DE TECHNOLOGIE DE COMPIEGNE Research Unit AVENUES EA 7284 Urban DC Microgrids Modeling, Optimization and Real-Time Control Prof. Manuela SECHILARIU
2 UNIVERSITE DE TECHNOLOGIE DE COMPIEGNE Urban DC microgrids: Modeling, Optimization and Real-Time Control 2 Compiègne Consortium School of Engineering IT Engineer Bio-mechanic Engineer Mechanic Engineer Urban Systems Engineer Industrial Process Engineer
3 UNIVERSITE DE TECHNOLOGIE DE COMPIEGNE Urban DC microgrids: Modeling, Optimization and Real-Time Control 3 Consortium/Alliance
4 Urban DC microgrids: Modeling, Optimization and Real-Time Control UNIVERSITE DE TECHNOLOGIE DE COMPIEGNE 4 Research unit AVENUES EA 7284 Interdisciplinary research on urban systems Multiscale urban systems modeling
5 Urban DC microgrids: Modeling, Optimization and Real-Time Control UNIVERSITE DE TECHNOLOGIE DE COMPIEGNE 5 Research unit AVENUES EA 7284 Interdisciplinary research on urban systems Energy management and microgrids Team: 2 permanent researchers, 1researcher (under project contract) PhD students, Master students PhD thesis in microgrids field : 7 PhD defended thesis 2018: 3 PhD thesis on going Two technological platforms Building integrated microgrid Electric vehicles charging station microgrid based Leader of French research network on Microgrids
6 Urban DC microgrids: Modeling, Optimization and Real-Time Control GDR SEEDS 2994 CNRS GROUP 6 SEEDS: Electrical Energy Systems in their Societal Dimensions SEEDS : national research group supported and funded by CNRS GT Microgrids: working group French research network: 20 laboratoires, 1 ITE, 55 researchers
7 Urban DC microgrids: Modeling, Optimization and Real-Time Control URBAN DC MICROGRIDS 7 Urban DC microgrids: Modeling, Optimization and Real-Time Control Outline 1. Context and motivation 2. Urban microgrids Smartgrid and urban microgrids Power management interface Urban energy management strategies 3. Microgrids modeling 4. Microgrids optimization Building-integrated DC microgrid Supervisory principle 5. Microgrids real time control Results 6. Conclusion
8 Urban DC microgrids: Modeling, Optimization and Real-Time Control URBAN DC MICROGRIDS 8 Urban DC microgrids: Modeling, Optimization and Real-Time Control Outline 1. Context and motivation 2. Urban microgrids Smartgrid and urban microgrids Power management interface Urban energy management strategies 3. Microgrids modeling 4. Microgrids optimization Building-integrated DC microgrid Supervisory principle 5. Microgrids real time control Results 6. Conclusion
9 Urban DC microgrids: Modeling, Optimization and Real-Time Control 1. CONTEXT AND MOTIVATION 9 Major preoccupations in urban areas Buildings energy performances Charging stations for plug-in electric vehicles Emerging projects Smart grid combined with microgrids Positive-energy buildings increasing Photovoltaic (PV) arrays most common used renewable sources in urban area Local microgrid based on PV sources Urban microgrids for advanced local energy management Smart grid communication Self-consumption
10 Urban DC microgrids: Modeling, Optimization and Real-Time Control 1. CONTEXT AND MOTIVATION 10 Distributed electricity production Power balancing in context of renewable energy integration Centralized regulation? or local regulation? or both? Smart grid microgrids (losses diminution, local regulation and optimization ) Production Nuclear, hydraulic, gas turbine plants Photovoltaic and wind turbine farms Control center for electricity network operators Photovoltaic small sites Photovoltaic and wind turbine farms High voltage END-USER DEMAND LOCAL INFORMATIONS Medium voltage Factory Rail network Optimization Production / Consumption Source : Commission de Régulation de l Energie Electricity transport and distribution Low voltage Remote area: houses or farms Consumption / Production Buildings Shopping centers Residential area Rail network Electricity injection Electricity supply Electricity power flow Data communication data transmission for the smart grid and for the end-user Grid interaction SMART GRID COMMUNICATION
11 Urban DC microgrids: Modeling, Optimization and Real-Time Control 1. CONTEXT AND MOTIVATION 11 Adopted microgrid definition (U.S. Energy department) Microgrid is defined as a group of interconnected loads and distributed energy resources renewable energies, storages, and traditional energies (gas, fuel ) with clearly defined electrical boundaries that acts as a single controllable entity with respect to the grid and the end-user and can connect and disconnect from the grid to enable it to operate in both grid connected or island mode Source
12 Urban DC microgrids: Modeling, Optimization and Real-Time Control URBAN DC MICROGRIDS 12 Urban DC microgrids: Modeling, Optimization and Real-Time Control Outline 1. Context and motivation 2. Urban microgrids Smartgrid and urban microgrids Power management interface Urban energy management strategies 3. Microgrids modeling 4. Microgrids optimization Building-integrated DC microgrid Supervisory principle 5. Microgrids real time control Results 6. Conclusion
13 Urban DC microgrids: Modeling, Optimization and Real-Time Control 2. URBAN MICROGRIDS 13 Smart grid and urban microgrids Public grid and communication network Control interfaces Communication bus Router Energy manager and public control Static switch Static switch Microgrid controller MICROGRID Static switch Microgrid controller MIDDLE SCALE PUBLIC GRID URBAN AREAS MICROGRID PCC Point of common coupling LARGE SCALE PUBLIC GRID
14 Urban DC microgrids: Modeling, Optimization and Real-Time Control 2. URBAN MICROGRIDS 14 Research interests in microgrid field Techno-economic optimization of microgrid Real-time power management at the local level SMART GRID DATA Control of microgrid Real time power management for sources and load END-USER DATA & WEATHER DATA Power system state Sources Control Load Control AC bus or DC Bus or AC-DC Buses Public grid AC or DC Load PV Sources Storage Super Capacitors Diesel Generator
15 2. URBAN MICROGRIDS Urban DC microgrids: Modeling, Optimization and Real-Time Control 15 Power management interface Active consumers Smart grid communication network Dynamic pricing Supervision Cost optimization Power peak shaving Microgrid interface and local energy management Weather forecasting Power demand forecasting Grid power supply prediction Smart metering Public grid Grid power injection prediction Monitoring and End-user management Power Public grid power supply Injection Photovoltaic power Building tertiary needed power Energy sources - Photovoltaic - Wind turbine - Storage - Fuel-cell - Micro-turbine - (Bio)Diesel generator Self-consumption 0h00 6h00 12h00 18h00
16 2. URBAN MICROGRIDS Urban DC microgrids: Modeling, Optimization and Real-Time Control 16 Urban energy management strategies V2H V2H: Vehicle to Home V2G V2G: Vehicle to Grid I2H: Infrastructure to Home I2H Applications Zero-energy or positive-energy buildings Prosumer (producer-consumer) building Self-consumption Charging stations and infrastructures for electric vehicles
17 2. URBAN MICROGRIDS Urban DC microgrids: Modeling, Optimization and Real-Time Control 17 Experimental platforms Platform PLER 16 PV Fabrik-Solar: 2kW STC Wind Turbine 1kVA Storage Li-ion, Lead-acide Power Grid Emulator Load Emulator Building Emulator Storage lead-acide baterries Drivers IGBT Current sensors card Voltage sensors card Grid emulator Real-time system Recorder Interface card Element Parameter Device Storage (serial 8 battery units) 96V/130Ah Sonnenschein Solar S12/130 A PV array (16 PV panel in series) I MPP =7.14A, STC V MPP =280V, STC PV panel: Solar-Fabrik SF- 130/2-125 Grid emulator 3kVA Bidirectional linear amplifier Programmable DC 2.6kW Chroma electronic load Controller board dspace 1103 Power electronic converter 600V-100A SEMIKRON SKM100GB063D
18 2. URBAN MICROGRIDS Urban DC microgrids: Modeling, Optimization and Real-Time Control 18 Experimental platforms Platform STELLA: Smart Transport and Energy Living Lab 9 parking spot at Innovation Center of UTC 84 PV Sunpower: 28,9kW STC Storage Li-ion, supercapacitors Public grid connection Building grid supply connection Charging terminals: AC and DC
19 2. URBAN MICROGRIDS Urban microgrids for advanced local energy management 19 Experimental platforms Platform STELLA: Smart Transport and Energy Living
20 Urban DC microgrids: Modeling, Optimization and Real-Time Control URBAN DC MICROGRIDS 20 Urban DC microgrids: Modeling, Optimization and Real-Time Control Outline 1. Context and motivation 2. Urban microgrids Smartgrid and urban microgrids Power management interface Urban energy management strategies 3. Microgrids modeling 4. Microgrids optimization Building-integrated DC microgrid Supervisory principle 5. Microgrids real time control Results 6. Conclusion
21 Urban DC microgrids: Modeling, Optimization and Real-Time Control 3. MICROGRIDS MODELING 21 Energetic Macroscopic Representation (EMR) Source of energy Electrical coupling (without energy accumulation) Electrical converter (without energy accumulation) Element with energy accumulation Systematic approach to design all the interactions between the different subsystems of a complex system Synthetic graphic tool using causal or functional representation Four basic elements interconnected following the action and reaction principle using exchange variables and respecting the integral causality integral causality defines accumulation element by a time-dependent relationship between its variables (output is an integral function of its inputs) other elements are described using relationships without time dependence Instantaneous power exchanged between two elements is the result of the product of action and reaction variables represented by arrows (inputs and outputs)
22 3. MICROGRIDS MODELING Urban DC microgrids: Modeling, Optimization and Real-Time Control 22 Building-integrated microgrid PV IPV& etimpedance adaptateur d ' impédance adaptor système Storage de stockage B i ' L PV i ' i ' 1 B2 L L B3 i ' L S charge DC Load i PV ilpv LPV C LS ils LL ill i L IPV CPV v PV v' PV v ' S vs v ' L vl CL i ' L R Storage System L R i L R v R v ' R IPV PV Installation Public Grid DC Load B4 Public Grid 5 réseau extérieur B
23 3. MICROGRIDS MODELING 23 Source of energy Electrical coupling (without energy accumulation) Electrical converter (without energy accumulation) Element with energy accumulation EMR of PV installation and the impedance adaptor IPV i PV v PV ipv vpv vpv il PV il PV mpv v ' PV 0;1 m i ' LPV PV v C PV IPV & impedance et adaptateur dadaptor ' impédance B i ' L PV i ' 1 B2 i ' L S sy EMR of the DC common bus i ' L L i ' LS i PV ilpv LPV C i ' L S IPV C PV v PV v' PV v ' S i ' LPV i ' i ' LL i ' L R i ' L R i ' LR Urban DC microgrids: Modeling, Optimization and Real-Time Control
24 3. MICROGRIDS MODELING Urban DC microgrids: Modeling, Optimization and Real-Time Control 24 Source of energy Electrical coupling (without energy accumulation) Electrical converter (without energy accumulation) Element with energy accumulation EMR of the DC load v C i ' LL ml 0;1 ml v' L i L L i LL L i PV EMR of the storage IPV IPV et adaptateur d ' impédance v C PV ilpv vl L i v PV L PV L v' PV système Storage de stockage B i ' L PV i ' i ' 1 B2 L L B3 C i ' L S v ' S LS ils vs v ' L LL charge DC Load vl ill CL i L vc i ' LS m 0;1 S ms v ' S i L S il S v S EMR of the public grid S i ' L R L R i L R v R v ' R i LR v ' R i ' LR mr i L R m 1;1 R v R R B4 Public Grid B5 réseau extérieur
25 3. MICROGRIDS MODELING Urban DC microgrids: Modeling, Optimization and Real-Time Control 25 Source of energy Electrical coupling (without energy accumulation) Electrical converter (without energy accumulation) Element with energy accumulation EMR of the microgrid i ' LS système Storage de stockage ms v ' S i L S i LS v S S IPV IPV et adaptateur d ' impédance PV & impedance adaptor ipv vpv vpv il PV il PV v ' PV i ' LPV v m C PV i ' i ' LL v' L i m LL L charge DC Load i L L vl v L i L L State variables: v PV ; i ; v L PV C ; v L ; i LL ; i LS ; i LR Control variables: m PV ; m L ; m S ; m R réseau extérieur Public Grid v v ' C R i LR R i ' LR m R i LR v R
26 3. MICROGRIDS MODELING Urban DC microgrids: Modeling, Optimization and Real-Time Control 26 Maximum Control Structure (MCS) without Control controller block without controller with controller with controller without controller with controller Block strategy Block strategy MCS deduced through specific inversion rules direct inversion (without controller) applied for items that are not time function (conversion elements) EMR formalism does not allow derivative causality (a direct inversion of time function item is not possible) indirect inversion (with controller) applied for items that are time function (accumulation elements are inverted using a close-loop control) Three basic elements
27 Urban DC microgrids: Modeling, Optimization and Real-Time Control 3. MICROGRIDS MODELING without controller without controller with controller 27 with controller without controller with controller Block strategy MCS of PV installation and the impedance adaptor Block strategy IPV S1 ipv vpv vpv il PV il PV v ' PV v * i * ' * PV L PV v PV m i ' LPV PV v C IPV PV IPV & impedance et adaptateur dadaptor ' impédance B i ' L PV i ' 1 B2 i PV C PV ilpv v PV LPV v' PV C i ' L S v ' S systè dv dt PV 1 C ipv ilpv i * * L 1 PV PV PV C v v ipv PV i ' L R di LPV dt 1 L PV v PV v ' PV v C i i v ' * * PV 2 LPV LPV PV i' LPV il PV m ' PV v PV vc m v ' * * PV PV vc B 4
28 Urban DC microgrids: Modeling, Optimization and Real-Time Control 3. MICROGRIDS MODELING without controller without controller with controller 28 with controller without controller with controller Block strategy MCS of PV installation and the impedance adaptor IPV S1 ipv vpv Block strategy vpv il PV il PV v ' PV v * i * ' * PV L PV v PV m i ' LPV PV v C i ' LS i C v v i * * LPV 1 PV PV PV v C i i v ' * * PV 2 LPV LPV PV v' * * PV PV vc m MCS of the DC load i C v v i * * LL 3 L L L v C i i v ' * * L 4 LL LL L m v ' * * L L vc i ' LPV i ' i ' LL ml v' L i L L v' * L il L vl * i LL v L i L * v L L i ' LR
29 Urban DC microgrids: Modeling, Optimization and Real-Time Control 3. MICROGRIDS MODELING without controller without controller with controller 29 with controller without controller with controller Block strategy Block strategy MCS of the storage system MCS of the public grid i ' LS ms v ' S i L S i LS v S S i ' LS v C i i v ' * * R 6 LR LR R i ' LPV v ' * i * S LS i ' LPV i ' i ' LL m v ' * * R R vc i ' i ' LL v C i i v ' * * S 5 LS LS S v' * * S S vc m i ' LR mr v ' R i L R i LR v R R i ' LR v' * R * i LR
30 3. MICROGRIDS MODELING 30 without controller without controller with controller with controller without controller with controller Block strategy MCS of DC common bus and the system Block strategy i C v v i '* * ' 7 C C LPV i ' LS ms v ' S i L S i LS v S S control of 7 state variables with 4 control variables 2 strategies IPV i PV vpv vpv il PV il PV v ' PV i ' LPV v m C PV i ' i ' LL v' * S v ' L i m LL L * i LS il L vl v L i L L S1: MPPT S2: power balancing S1 v * i * ' * PV L PV v PV v' * L v ' R * i LL i LR * v L p L p v i * '* i' C p p p * * i' L * * ps kr p p 1k p * * R r 0 1 k r i i * LS * LR p v p v * S S * R R * i '* i ' LR * p i ' p PV i LR m R S2 v' * R p * v R * i LR k r R * p R * p S v SMIN v SMAX Urban DC microgrids: Modeling, Optimization and Real-Time Control v S
31 Urban DC microgrids: Modeling, Optimization and Real-Time Control 3. MICROGRIDS MODELING 31 EV charging station based on microgrid Direct DC power use DC bus voltage 1000V Public grid 230/400V, 50Hz PVA PEVs f PEVs i ' L PEVs i PEVs i LPEVs L PEVs Grid connection DC load PEVs v PEVs C PEVs v' PEVs i Load i LLoad L Load f Load i ' L Load v PVA i PVA i i ' C f A f B f C u ' AC u ' BC ia ib i C L L L u AC u BC Public Grid Load C Load v Load v' Load PVA v ' A v ' B v ' C
32 3. MICROGRIDS MODELING Urban DC microgrids: Modeling, Optimization and Real-Time Control 32 Source of energy Electrical coupling (without energy accumulation) Electrical converter (without energy accumulation) Element with energy accumulation EMR of the system i PVA PVA PEVs i PEVs v PEVs v PEVs i LPEVs i LPEVs v' PEVs m PEVs mpevs i ' L PEVs v PVA 0;1 v PVA i v PVA vpva i ' m m m m A B A B 1;1 u ' u i i ' AC BC A B i i u u A B AC BC PG Load i Load v Load v Load i LLoad i LLoad v' Load i ' L Load v PVA State variables: m Load mload 0;1 v PEVs ; i L PEVs ; v Load ; i L Load ; v PVA ; i A ; i B Control variables: m PEVs ; m Load ; m A ; m B
33 Urban DC microgrids: Modeling, Optimization and Real-Time Control 3. MICROGRIDS MODELING 33 without controller without controller with controller with controller without controller with controller Block strategy MCS of the system Block strategy S v PVA * q* 0 i '* p * i * i * i PVA PVA PEVs i PEVs v PEVs v PEVs i LPEVs i LPEVs v' PEVs m PEVs i ' L PEVs v PVA v PVA i v PVA vpva i ' m m A B u ' u i i ' AC BC A B v v i i u u A B AC BC PG v PEVs * i Load Load v Load v Load * i * v' PEVs* L PEVs v Load i LLoad i LLoad v' Load i * v' Load * L Load m Load i ' L Load v PVA i i m PEVs, m Load impose constant DC voltage (v PEVs ; v Load ) v PEVs * ; v Load * m A, m B impose variable DC voltage (v PVA )v PVA * imposed by P&O MPPT power balance: p* v i * v i * v i'* PVA
34 Urban DC microgrids: Modeling, Optimization and Real-Time Control 3. MICROGRIDS MODELING 34 EMR modeling for DC microgrid operation analysis unified and comprehensible graphical representation physical modeling inversion rules applied to EMR system's control structure is easily deduced using the MCS representation Local DC microgrid based on PV sources Building integrated microgrid Charging station integrated microgrid DC microgrid EMR model based on the interaction principle graphical description DC microgrid MCS inversion-based control structure graphical description
35 Urban DC microgrids: Modeling, Optimization and Real-Time Control URBAN DC MICROGRIDS 35 Urban DC microgrids: Modeling, Optimization and Real-Time Control Outline 1. Context and motivation 2. Urban microgrids Smartgrid and urban microgrids Power management interface Urban energy management strategies 3. Microgrids modeling 4. Microgrids optimization Building-integrated DC microgrid Supervisory principle 5. Microgrids real time control Results 6. Conclusion
36 Urban DC microgrids: Modeling, Optimization and Real-Time Control 4. MICROGRIDS OPTIMIZATION 36 Generic system overview Local DC Microgrid, DC bus distribution, AC bus distribution, appliances
37 4. MICROGRIDS OPTIMIZATION Urban DC microgrids: Modeling, Optimization and Real-Time Control 37 Building integrated DC microgrid DC MICROGRID SYSTEM SUPERVISORY SUBSYSTEM MULTI-SOURCE POWER SUBSYSTEM USER DEMAND METADATA SMART GRID MESSAGES Power subsystem states DC micro-grid: - efficiently integration of other renewable sources and storage - absence of phase synchronization - only the voltage must be stabilized - a single inverter is required to connect an AC load PVA Control v * PV PVA PVA: PV array DC DC v i * G Public Grid DC AC i* S DC DC K L Storage DC bus and DC load: - improving overall performance by removing multiple energy conversions - use of existing infrastructure cables with the same power transfer as in AC distribution network - positive-energy building - electric vehicle connection
38 4. MICROGRIDS OPTIMIZATION Urban DC microgrids: Modeling, Optimization and Real-Time Control 38 From hybrid dynamic system to supervisory and control principle x( t) F( x( t), q( t), u( t)) A( q) x( t) B u( t) y( t) C x( t) x( t ), q( t ) G( x( t), q( t), v( t)) if v( t) c occurs x( t ) x, q( t ) q
39 4. MICROGRIDS OPTIMIZATION Urban DC microgrids: Modeling, Optimization and Real-Time Control 39 Multilayer microgrid supervisory and control principle
40 4. MICROGRIDS OPTIMIZATION 40 Urban microgrids for advanced local energy management K L_lim
41 Urban DC microgrids: Modeling, Optimization and Real-Time Control 4. MICROGRIDS OPTIMIZATION 41 Human-machine interface To define operating criteria: total load shedding amount, period Load power parameters Appliances shedding parameters
42 Urban DC microgrids: Modeling, Optimization and Real-Time Control 4. MICROGRIDS OPTIMIZATION 42 Prediction layer K L_lim Load prediction p L_PRED by statistic data, BMS information PV prediction p PV_PRED by weather forecast data, sun position, PV model
43 Urban DC microgrids: Modeling, Optimization and Real-Time Control 4. MICROGRIDS OPTIMIZATIONROGRID 43 Prediction layer K L_lim Load prediction p L_PRED by Statistic data, Building Manag. System Other source information p L * p L_PRED
44 4. MICROGRIDS OPTIMIZATION Urban DC microgrids: Modeling, Optimization and Real-Time Control 44 Prediction layer K L_lim PV prediction p PV_PRED by weather forecast data, sun position, PV model
45 Urban DC microgrids: Modeling, Optimization and Real-Time Control 4. MICROGRIDS OPTIMIZATION 45 Energy management layer Objective: minimized energy cost Grid connected mode: reduce grid power peak demand Off-grid mode: minimize diesel generator fuel consumption Both modes: avoid load shedding and PV power limiting Optimization result : K D
46 4. MICROGRIDS OPTIMIZATION Urban DC microgrids: Modeling, Optimization and Real-Time Control 46 Energy management layer Problem formulation for grid-connected operating mode p p p G G _ I G _ S p PV p p PV _ MPPT PV _ LIM p p p S S _C S _ D p L P p L _ MAX L _ LIM p ( t) p ( t) p ( t) p ( t) G S L PV * * S * G * S D * p p p p K p KD [0,1]
47 4. MICROGRIDS OPTIMIZATION Urban DC microgrids: Modeling, Optimization and Real-Time Control 47 p PV p p Energy management layer Problem formulation for grid-connected operating mode PV _ MPPT PV _ LIM p p p S S _C S _ D p p p G G _ I G _ S p L P p L _ MAX L _ LIM Minimize C C C C C total G S PV _ S L _ S for t { t, t t, t 2 t,..., t } and with respect to: i pl ( ti ) pg _ I ( ti ) ps _ C ( ti ) pg _ S ( ti ) ps _ D ( ti ) ppv ( ti ) ps ( ti ) ps _ C ( ti ) ps _ D ( ti ) ppv ( ti ) ppv _ MPPT ( ti ) ppv _ S ( ti ) pl ( ti ) p LD ( ti ) pl _ S ( ti ) if p ( t ) p ( t ) then p ( t ) 0 PV _ MPPT i LD i L _ S i pl _ S ( ti ) 0 if ppv _ MPPT ( ti ) pld ( ti ) then ppv _ S ( ti ) 0 if ppv _ MPPT ( ti ) pld ( ti ) then ppv _ S ( ti ) 0 SOCmin soc( ti ) SOCmax 1 soc( t ) SOC ( p ( t ) p ( t )) t p p p PV L t i 0 F 3600vS CREF ti t0 S _ C ( t ) 0 i ( t ) 0 i PV _ S i ( t ) 0 pl _ S ( ti ) 0 P p () t P S _ max S S _ max 0 pg I ( ti ) PG I G S L PV 0 pg _ S ( ti ) PG _ S _lim * * * Limit pg ( ti ) p pg ( ti1 ) p Limit S p G pg ( ti ) 0, ps ( ti ) 0 * if ppv ( ti ) p * LD ( ti ) 0 ( ) 0, ( ) 0 ps if K( D) pg ti ps ti ppv ti pld ( ti ) 0 ppv _ S ( t ) 0 if soc( t) SOCmax KD [0,1] p_ ( t) _ p_lim ( t) p ( t) p ( t) F i S _ D i
48 4. MICROGRIDS OPTIMIZATION Urban DC microgrids: Modeling, Optimization and Real-Time Control 48 Energy management layer Energy cost optimization min ( C C C C C ) t G S PV _ S L _ S Tariff T T T T : S G PV _ S L _ S Optimization solved by Mixed Integer Linear Programming SUPERVISION SYSTEM Human-machine interface Prediction layer Energy management layer User demand Metadata DATA : p INPUT FILES PV _ PRED L _ PRED P, P, P CONSTRAINTS : lim lim _ G I G S L MAX SOC, SOC, P MIN MAX S _ MAX energy tariff,..., p Operation layer Power system states Problem modeling according to CPLEX MULTI-SOURCE POWER SYSTEM IBM ILOG CPLEX OUTPUT OPTIMAL POWER EVOLUTION p, p, p, p, p G S _ C S _ D PV _ S L _ S
49 4. MICROGRIDS OPTIMIZATION Urban DC microgrids: Modeling, Optimization and Real-Time Control 49 Energy management layer Energy cost optimization min ( C C C C C ) t G S PV _ S L _ S Tariff T T T T : S G PV _ S L _ S Optimization solved by Mixed Integer Linear Programming SUPERVISION SYSTEM Human-machine interface Prediction layer Energy management layer User demand Metadata DATA : p INPUT FILES PV _ PRED L _ PRED P, P, P CONSTRAINTS : lim lim _ G I G S L MAX SOC, SOC, P MIN MAX S _ MAX energy tariff,..., p Operation layer K D Power system states Problem modeling according to CPLEX MULTI-SOURCE POWER SYSTEM IBM ILOG CPLEX K D p p S _ C S _ D p ( p p ) G S _ C S _ D OUTPUT OPTIMAL POWER EVOLUTION p, p, p, p, p G S _ C S _ D PV _ S L _ S
50 4. MICROGRIDS OPTIMIZATION Urban DC microgrids: Modeling, Optimization and Real-Time Control 50 Operational layer Interface: optimization by K D Robust: power balancing with any K D value Self-correcting: load shedding PV power limiting
51 Urban DC microgrids: Modeling, Optimization and Real-Time Control URBAN DC MICROGRIDS 51 Urban DC microgrids: Modeling, Optimization and Real-Time Control Outline 1. Context and motivation 2. Urban microgrids Smartgrid and urban microgrids Power management interface Urban energy management strategies 3. Microgrids modeling 4. Microgrids optimization Building-integrated DC microgrid Supervisory principle 5. Microgrids real time control Results 6. Conclusion
52 Urban DC microgrids: Modeling, Optimization and Real-Time Control Local load power prediction System parameters SOC, SOC, P, k min max S_ max L_crit p L _ pre soc p PV _ pre Forecast Subsystem PV power prediction calculation g, AIR Local Weather Forecast 52 Economic Dispatch Layer Optimization algorithm P P, P G _ I _ pre G _ S _ pre, P G _ I _max G _ S _max Energy Tariff Smart Grid soc Estimation Measurement p PV PV Control Operational Subsystem p S p _, p, p L DEM PV S p PV _ S k D Operational Algorithm p L _ DEM p, p PV S p L _ S Human-Machine Interface CoP, T, T orig min Load shedding/restoration optimization algorithm max p L _ S Load Control Communication Subsystem Demand Side Management Subsystem f ( PV ) f( G ) f( S) f( L)
53 5. MICROGRIDS REAL TIME CONTROL Urban DC microgrids: Modeling, Optimization and Real-Time Control 53 Demand side management (load shedding optimization) th 0 if i appliance is off xi th 1 if appliance is on n i max f ( x) CoPi xi : 1 i n i1 CoPorig 0 Wi Wrated ku kc 0 with respect to: n P W x P D i i AVL i1 CoPorig Tcount off CoPi CoPorig Tcount on T count _ off 50 if _ T T min if _ T max max
54 5. MICROGRIDS REAL TIME CONTROL Urban DC microgrids: Modeling, Optimization and Real-Time Control 54 Demand side management (load shedding optimization) Power(W) P AVL P D P S 100 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 Power(W) P AVL P D P S 100 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00
55 5. MICROGRIDS REAL TIME CONTROL Urban DC microgrids: Modeling, Optimization and Real-Time Control Raw data prediction Corrected prediction Measure Results p PV (W) :00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:00 Raw data prediction Corrected prediction Measure Case operation C total ( ) Load shedding ( ) PVA power limiting ( ) Optimization based predictions Experiment A postiori optimization based real conditions p PV (W) p PV (W) :00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20: Raw data prediction Corrected prediction Measure 0 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:00 Case operation C total ( ) Load shedding ( ) PVA power limiting ( ) Optimization based predictions Experiment A postiori optimization based real conditions Case operation C total ( ) Load shedding ( ) PVA power limiting ( ) Optimization based predictions Experiment A postiori optimization based real conditions
56 Urban DC microgrids: Modeling, Optimization and Real-Time Control URBAN DC MICROGRIDS 56 Urban DC microgrids: Modeling, Optimization and Real-Time Control Outline 1. Context and motivation 2. Urban microgrids Smartgrid and urban microgrids Power management interface Urban energy management strategies 3. Microgrids modeling 4. Microgrids optimization Building-integrated DC microgrid Supervisory principle 5. Microgrids real time control Results 6. Conclusion
57 Urban DC microgrids: Modeling, Optimization and Real-Time Control 6. CONCLUSION 57 Energy cost optimization and predictive control Flexible and reconfigurable algorithm Power balancing following K D parameter as predictive control parameter Limits Near-optimal cost due to the forecast uncertainties Real time optimization Microgrid for urban areas offers interface with the future smart grid Multilayer supervisory hierarchical design allow smart communication Experimental validation technical feasibility Work in progress Dynamic converter efficiencies nonlinear optimization Electromobility: V2G, V2H, I2H
58 UNIVERSITE DE TECHNOLOGIE DE COMPIEGNE Research Unit AVENUES EA 7284 Thanks for listening
INTELLIGENT DC MICROGRID WITH SMART GRID COMMUNICATIONS: CONTROL STRATEGY CONSIDERATION AND DESIGN
INTELLIGENT DC MICROGRID WITH SMART GRID COMMUNICATIONS: CONTROL STRATEGY CONSIDERATION AND DESIGN Presented by: Amit Kumar Tamang, PhD Student Smart Grid Research Group-BBCR aktamang@uwaterloo.ca 1 Supervisor
More informationIEEE Workshop Microgrids
From Knowledge Generation To Science-based Innovation IEEE Workshop Microgrids A Test Bed in a Laboratory Environment to Validate Islanding and Black Start Solutions for Microgrids Clara Gouveia (cstg@inescporto.pt)
More informationMicrogrids Optimal Power Flow through centralized and distributed algorithms
DEIM Dipartimento di Energia, Ingegneria della Informazione e Modelli Matematici Flow through centralized and, N.Q. Nguyen, M. L. Di Silvestre, R. Badalamenti and G. Zizzo Clean energy in vietnam after
More informationRobust Battery Scheduling in a Micro-Grid with PV Generation Xing Wang, Ph.D. GE Grid Software 2016 March 30, 2016
1 Robust Battery Scheduling in a Micro-Grid with PV Generation Xing Wang, Ph.D. GE Grid Software Solution @i-pcgrid 2016 March 30, 2016 Imagination at work 2 Outline Introduction Problem description Case
More information«electricity & Vehicles» PLATFORM
EMR 17 International Summer School 20 th June 2017 «electricity & Vehicles» PLATFORM electricity & Vehicles 2 electricity & Vehicles National Position MEGEVH NETWORK 3 (Energetic Modelling and Energy Management
More informationSPIRO SOLUTIONS PVT LTD POWER ELECTRONICS 1. RENEWABLE ENERGY PROJECT TITLES I. SOLAR ENERGY
POWER ELECTRONICS 1. RENEWABLE ENERGY S.NO PROJECT CODE PROJECT TITLES I. SOLAR ENERGY YEAR 1 ITPW01 Photovoltaic Module Integrated Standalone Single Stage Switched Capacitor Inverter with Maximum Power
More informationOptimal and Modular Configuration of Wind Integrated Hybrid Power Plants for Off-Grid Systems
Optimal and Modular Configuration of Wind Integrated Hybrid Power Plants for Off-Grid Systems Lennart Petersen, Industrial Ph.D. Fellow Hybrid Solutions Co-Authors: F. Iov (Aalborg University), G. C. Tarnowski,
More informationCommunications requirements in lowvoltage. Environmental concerns
Communications requirements in lowvoltage smart grids Fernando Kuipers Network Architectures and Services Delft University of Technology March 6, 2013 http://www.nas.ewi.tudelft.nl/people/fernando/ 1 Environmental
More informationPPT EN. Industrial Solutions
PPT2015.04.07.00EN Solving complexity of renewable energy production Reliability of supply Wind and photovoltaic are non-dispatchable generators. Production is dictated by weather conditions, not users
More informationSmart Grids and the Change of the Electric System Paradigm
2010 February 9 Lisbon Campus da FEUP Rua Dr. Roberto Frias, 378 4200-465 Porto Portugal T +351 222 094 000 F +351 222 094 050 jpl@fe.up.pt Smart Grids and the Change of the Electric System Paradigm João
More informationUsing Opal-RT Real-Time Simulation and HIL System in Power and Energy Systems Research
Using Opal-RT Real-Time Simulation and HIL System in Power and Energy Systems Research Shuhui Li Department of Electrical & Computer Engineering The University of Alabama Presented on February 15, 2017
More informationEnergy Security Electrical Islanding Approach and Assessment Tools. Dr. Bill Kramer Senior Research Engineer Distributed Energy Systems Integration
Energy Security Electrical Islanding Approach and Assessment Tools Dr. Bill Kramer Senior Research Engineer Distributed Energy Systems Integration Dr. Bill Kramer - 2 Electricity, Resources, & Building
More informationUNC-Charlotte's Power Engineering Teaching lab
1 UNC-Charlotte's Power Engineering Teaching lab B. Chowdhury Panel Session Title: Existing and Proposed Power Systems Laboratories for the Undergraduate Curriculum PES GM 2015 2 Outline Background - Energy
More informationLaboratory Scale Microgrid Test-Bed Hardware Implementation
Laboratory Scale Microgrid Test-Bed Hardware Implementation Joyer Benedict Lobo Ameya Chandrayan Peter Idowu, Ph.D. In Partnership with: Outline Features of a Microgrid Microgrid Test Bed at Penn State
More informationLaboratory Infrastructure
www.smartrue.gr Laboratory Infrastructure Laboratory Infrastructure Single-phase Microgrid Solar o 11x110Wp monocrystaline PV panels o Inverter SMA Sunny Boy 1100E 1.1kW Wind o WHISPER Wind Generator o
More informationUse of Microgrids and DERs for black start and islanding operation
Use of Microgrids and DERs for black start and islanding operation João A. Peças Lopes, FIEEE May 14 17, 17 Wiesloch The MicroGrid Concept A Low Voltage distribution system with small modular generation
More informationTechnical and Economic Assessment of Solar Photovoltaic and Energy Storage Options for Zero Energy Residential Buildings
Technical and Economic Assessment of Solar Photovoltaic and Energy Storage Options Pedro Moura, Diogo Monteiro, André Assunção, Filomeno Vieira, Aníbal de Almeida Presented by Pedro Moura pmoura@isr.uc.pt
More informationOPTIMIZING THE ACQUISITION AND OPERATION OF DISTRIBUTED GENERATION SYSTEMS
OPTIMIZING THE ACQUISITION AND OPERATION OF DISTRIBUTED GENERATION SYSTEMS Kris Pruitt, PhD Candidate, USAF Dr. Alexandra Newman, Division of Economics and Business Dr. Robert Braun, Division of Engineering
More informationDynamic Control of Grid Assets
Dynamic Control of Grid Assets ISGT Panel on Power Electronics in the Smart Grid Prof Deepak Divan Associate Director, Strategic Energy Institute Director, Intelligent Power Infrastructure Consortium School
More informationThe Prince Lab microgrid test bed
BUCHAREST 2018 SYMPOSIUM ON MICROGRIDS University Politehnica of Bucharest, Romania 2-6 Sept. 2018 The Prince Lab microgrid test bed Enrico De Tuglie enricoelio.detuglie@poliba.it DEPARTMENT OF ELECTRICAL
More informationtechnologies Balanced geographies FY 2013 revenue Balanced end markets FY 2013 revenue of revenue in new economies people in 100+ countries
1 Challenges, technologies, project feedback Sylvain Lechat Sanjuan, Schneider Electric 2014.07.29 - IEEE Power & Energy Society General Meeting Washington, DC, USA Number: 14PESGM2723 2 Schneider Electric
More informationArmands Senfelds, Leonids Ribickis, Ansis Avotins, Peteris Apse-Apsitis
Development of 600V Industrial DC Microgrid for Highly Automated Manufacturing Applications: Factory and Laboratory Infrastructure Experience Armands Senfelds, Leonids Ribickis, Ansis Avotins, Peteris
More informationEnergy storages in flexible energy systems. Kari Mäki VTT
Energy storages in flexible energy systems Kari Mäki VTT Contents Short status overview Needs for storage units Storage integration in energy systems Ancillary services Aggregator business logics Case
More informationRenewables induce a paradigm shift in power systems, is energy storage the holy grail?
THE VALUE OF STORAGE FOR THE ENERGY TRANSITION, EURELECTRIC CONFERENCE, DECEMBER 2017 storage for future power systems Adrian Timbus, Head of Technology and Solutions for Smart Grids and Renewables, ABB
More informationMicrogrid Storage Integration Battery modeling and advanced control
Alexandre Oudalov, ABB Switzerland Ltd., 1th Microgrid Symposium, Beijing, November 13-14, 214 Microgrid Storage Integration Battery modeling and advanced control Microgrid Storage Integration Outline
More informationSEVILLA, APRIL Microgeneration and Microgrids (modeling, islanding operation, black start, multi-microgrids) J. Peças Lopes Power Systems Unit
SEVILLA, APRIL 2010 Campus da FEUP Rua Dr. Roberto Frias, 378 4200-465 Porto Portugal T +351 222 094 000 F +351 222 094 050 cmoreira@inescporto.pt www.inescporto.pt Microgeneration and Microgrids (modeling,
More informationMicrogrids Outback Power Technologies
Microgrids Outback Power Technologies Microgrids - Definition EPRI defines microgrids as a power system with distributed resources serving one or more customers that can operate as an independent electrical
More informationDC Nanogrids Igor Cvetkovic
Center for Power Electronics Systems The Bradley Department of Electrical and Computer Engineering College of Engineering Virginia Tech, Blacksburg, Virginia, USA DC Nanogrids Igor Cvetkovic Presentation
More informationCPES Initiative on Sustainable Buildings and Nanogrids
Center for Power Electronics Systems Bradley Department of Electrical and Computer Engineerung College of Engineering Virginia Tech, Blacksburg, Virginia, USA CPES Initiative on Sustainable Buildings and
More informationMEDSolar Training Course Module 1 Microgrids with PV support
MEDSolar Training Course Module 1 Microgrids with PV support Concept of microgrid and smart microgrid. Profiles in generation/consumption sides. Hardware blocks of the microgrid. Connection to the mains
More informationSmart Grids and Mobility
International Conference on Technology Policy and Innovation 2009 July 14th Smart Grids and Mobility Campus da FEUP Rua Dr. Roberto Frias, 378 4200-465 Porto Portugal T +351 222 094 000 F +351 222 094
More informationControl System for a Diesel Generator and UPS
Control System for a Diesel Generator and UPS I. INTRODUCTION In recent years demand in the continuity of power supply in the local distributed areas is steadily increasing. Nowadays, more and more consumers
More informationSIRFN Capability Summary RSE- Ricerca sul Sistema Energetico (Italy)
SIRFN Capability Summary RSE- Ricerca sul Sistema Energetico (Italy) Introduction RSE has laboratories and facilities to perform applied research on DER and Smart Grids. Main research activities areas
More informationImproving the Storage Capability of a Microgrid with a Vehicle-to-Grid Interface
Improving the Storage Capability of a Microgrid with a Vehicle-to-Grid Interface Vicente Leite, Ângela Ferreira and José Batista Polytechnic Institute of Bragança, Portugal Outline Motivation The IPB microgrid
More information2009 Wind-Diesel Workshop. Microgrid Control System Technology GE Digital Energy, Markham Ontario
2009 Wind-Diesel Workshop Microgrid Control System Technology GE Digital Energy, Markham Ontario June 2 nd, 2009 Protection & Control Multilin Communications MDS, Lentronics Power Quality Zenith Controls
More informationImpact of Distributed Generation and Storage on Zero Net Energy (ZNE)
Impact of Distributed Generation and Storage on Zero Net Energy (ZNE) Omar Siddiqui Senior Technical Executive Emerging Technologies Summit San Francisco, CA October 21, 2014 Together Shaping the Future
More informationNREL Microgrid Controller Innovation Challenge Event
Power Systems Engineering Center NREL Microgrid Controller Innovation Challenge Event Brian Miller, PE Strategic Team Lead, Microgrids Brian.Miller@NREL.gov 303-275-4917 Overview Background: NREL capabilities
More informationThe integration of second life EV battery and vehicle-to-grid charging into a micro-grid environment
The integration of second life EV battery and vehicle-to-grid charging into a micro-grid environment by Xander Theron, Nelson Mandela University The global push to reduce the effects of global warming
More informationFacilitated Discussion on the Future of the Power Grid
Facilitated Discussion on the Future of the Power Grid EPRI Seminar: Integrated Grid Concept and Technology Development Tokyo Japan, August 20, 2015 Matt Wakefield, Director Information, Communication
More informationThe virtual battery: energy management in buildings and neighbourhoods siemens.com
The virtual battery: energy management in buildings and neighbourhoods siemens.com 18 May, 2016 Siemens focuses on electrification, automation and digitalization and is actively supporting Smart City/Neighbourhood
More informationReal-Time Simulation of A Modular Multilevel Converter Based Hybrid Energy Storage System
Real-Time Simulation of A Modular Multilevel Converter Based Hybrid Energy Storage System Feng Guo, PhD NEC Laboratories America, Inc. Cupertino, CA 5/13/2015 Outline Introduction Proposed MMC for Hybrid
More informationDesign of Active and Reactive Power Control of Grid Tied Photovoltaics
IJCTA, 9(39), 2016, pp. 187-195 International Science Press Closed Loop Control of Soft Switched Forward Converter Using Intelligent Controller 187 Design of Active and Reactive Power Control of Grid Tied
More informationSIZING AND TECHNO-ECONOMIC ANALYSIS OF A GRID CONNECTED PHOTOVOLTAIC SYSTEM WITH HYBRID STORAGE
UPEC 2016, Coimbra,Portugal 6 th Sept -9 th Sept 2016 SIZING AND TECHNO-ECONOMIC ANALYSIS OF A GRID CONNECTED PHOTOVOLTAIC SYSTEM WITH HYBRID STORAGE Faycal BENSMAINE Dhaker ABBES Dhaker.abbes@hei.fr Antoine
More informationDynamic Modelling of Hybrid System for Efficient Power Transfer under Different Condition
RESEARCH ARTICLE OPEN ACCESS Dynamic Modelling of Hybrid System for Efficient Power Transfer under Different Condition Kiran Kumar Nagda, Prof. R. R. Joshi (Electrical Engineering department, Collage of
More informationGetting Smart Evolution to the Smart Grid April 2008
Getting Smart Evolution to the Smart Grid April 2008 Thomas F Garrity Vice President, Sales and Business Development Siemens Power T&D, Inc. Electrical energy is the backbone of our society Page 2 Mar-07
More information«OPTIMAL ENERGY MANAGEMENT BY EMR AND META-HEURISTIC APPROACH FOR MULTI-SOURCE ELECTRIC VEHICLES»
EMR 13 Lille Sept. 213 Summer School EMR 13 Energetic Macroscopic Representation «OPTIMAL ENERGY MANAGEMENT BY EMR AND META-HEURISTIC APPROACH FOR MULTI-SOURCE ELECTRIC VEHICLES» Dr. João Pedro TROVÃO,
More information2016 UC Solar Research Symposium
2016 UC Solar Research Symposium Beyond UCR s Sustainable Integrated Grid Initiative: Energy Management Projects in Southern California October 7, 2016 Presented by: Alfredo A. Martinez-Morales, Ph.D.
More informationRESEARCH CHALLENGES IN MICROGRID TECHNOLOGIES. MicroGrid Research Programme leader and co-leader
RESEARCH CHALLENGES IN MICROGRID TECHNOLOGIES MicroGrid Research Programme leader and co-leader Prof. Dr. Josep M. Guerrero joz@et.aau.dk Assist. Prof. Dr. Juan C. Vasquez joz@et.aau.dk Presenter Dr. Tomislav
More informationSTORAGE SYSTEM PV APPLICATIONS
Technical University of Graz, April 2012 «Modelling and control using Energetic Macroscopic Representation» «EMR AND INVERSION-BASED CONTROL OF A HYBRID STORAGE SYSTEM PV APPLICATIONS» FOR PV A I. M. Garcia-Herreros,
More information(GEMS) with Etherium Base Energy Trading platform
Experience of implementing ASEAN first 1 Green Community Energy Management System (GEMS) with Etherium Base Energy Trading platform Dr. Wuthipong Suponthana Leonics Co., Ltd. wuthipong@leonics.com innovates
More informationSolutions for Smarter Power Markets
Solutions for Smarter Power Markets Eric GOUTARD Alstom Grid 6-8 March 2011 GRID 1 ALSTOM APEx- APAC Regional Meet 2011, 6th -8th March 2011, New Delhi Key Drivers for Smart Grids 1. Maximize CO2 free
More informationSIRFN Capability Summary European Distributed Energy Resources Laboratories (DERlab) e. V.
SIRFN Capability Summary European Distributed Energy Resources Laboratories (DERlab) e. V. Introduction For more information, contact: DERlab is the association of 20 leading laboratories and research
More informationInverter with MPPT and Suppressed Leakage Current
POWER ELECTRONICS IEEE Projects Titles -2018 LeMeniz Infotech 36, 100 feet Road, Natesan Nagar(Near Indira Gandhi Statue and Next to Fish-O-Fish), Pondicherry-605 005 Web : www.ieeemaster.com / www.lemenizinfotech.com
More informationModeling and Simulation of DC Microgrids for Electric Vehicle Charging Stations
Energies 215, 8, 4335-4356; doi:1.339/en854335 Article OPEN ACCESS energies ISSN 1996-173 www.mdpi.com/journal/energies Modeling and Simulation of DC Microgrids for Electric Vehicle Charging Stations Fabrice
More informationTechnology from the New Product SANUPS K for a Smart Grid Society
Features: Technology Contributing to Effective Use of Power Technology from the New Product SANUPS K for a Smart Grid Society Yoshiaki Okui 1. Introduction After the Tohoku Earthquake, there is a movement
More informationStand-alone PV power supply for developing countries
Stand-alone PV power supply for developing countries Frederick M. Ishengoma Dept. of Electrical Power Eng. NTNU October 25, 2002 ENO Presentation 1 Access to Grid electricity Estimated 2 billion people
More informationNew Safety Rules for Large Scale Photovoltaic Systems, Energy Storage Systems, and Microgrids
October 10, 2016 New Safety Rules for Large Scale Photovoltaic Systems, Energy Storage Systems, and Microgrids New Safety Rules for Large Scale Photovoltaic Systems, Energy Storage Systems, and Microgrids
More informationDER Portfolio Optimization and Dispatch, Tertiary Control/Monitoring Strategies
DER Portfolio Optimization and Dispatch, Tertiary Control/Monitoring Strategies Maggie Clout Siemens Energy Management Digital Grid Siemens AG 2016 Three Pillars of a Microgrid System Mixed Generation
More informationMicrogrid solutions Delivering resilient power anywhere at any time
Microgrid solutions Delivering resilient power anywhere at any time 2 3 Innovative and flexible solutions for today s energy challenges The global energy and grid transformation is creating multiple challenges
More informationTesting Energy Storage Systems: From EVs to Utility Grid
Testing Energy Storage Systems: From EVs to Utility Grid Jonathan P. Murray Business Development Manager 2008 Bloomy Controls. All Rights Reserved Agenda Energy storage system landscape Electric vehicle
More informationMicrogrid with Solar Power and Fuel Cell Technology
Environment, Energy Security, and Sustainability (E2S2) Symposium and Exhibition Microgrid with Solar Power and Fuel Cell Technology 16 June 2010 Dan Markiewicz Senior Director, Electrical Design 1 OVERVIEW
More informationPOWERTRAIN SOLUTIONS FOR ELECTRIFIED TRUCKS AND BUSES
POWERTRAIN SOLUTIONS FOR ELECTRIFIED TRUCKS AND BUSES PDiM 2017 (Heimo Schreier) Burak Aliefendioglu Fredrik Haag AVL H. Schreier, B Aliefendioglu, F. Haag PDIM 2017 30 November 2017 1 TRUCK & BUS ELECTRIFICATION
More informationMicrogrid Technology. Paul Newman Microgrid Sales Manager North America - West
Microgrid Technology Paul Newman Microgrid Sales Manager North America - West Agenda Microgrid Thin Film Advantage Energy Storage Microgrid Master Controller Case Study Cat Microgrid Solutions See official
More informationDesign and Analysis of Hybrid Renewable Microgrid Systems for United Nations WFP Humanitarian Locations in Developing Countries
Design and Analysis of Hybrid Renewable Microgrid Systems for United Nations WFP Humanitarian Locations in Developing Countries Denim D Dcosta MSPE Master s Thesis Exposé Supervisor: Prof. Dr.-Ing. Ulrich
More informationTHE YOUNICOS SOFTWARE PLATFORM
THE YOUNICOS SOFTWARE PLATFORM BENEFITS AT A GLANCE UNIQUE EXPERIENCE SYSTEM-WIDE INTEROPERABILITY Y.Q combines over a decade of energy storage project experience and operational field data and has been
More information1.Power System Infrastructure
1.Power System Infrastructure Central Generating Station Step-Up Transformer 2. Communications and Information Infrastructure Operators, Planners & Engineers Control Center Distribution Substation Gas
More informationRenewables from a TSO Perspective. M.BENA, SmartGrids Director, RTE, French TSO Vienna, 18 May 2015
Renewables from a TSO Perspective M.BENA, SmartGrids Director, RTE, French TSO Vienna, 18 May 2015 RTE in Europe 8500 employees Owner and Operator of the Assets 100 000 km UHV and HV lines (400 kv -> 63
More informationMeasuring the Smartness of the Electricity Grid
Measuring the Smartness of the Electricity Grid Leen Vandezande Benjamin Dupont Leonardo Meeus Ronnie Belmans Overview Introduction Key Performance Indicators (KPIs): what & why? Benchmarking the Smart
More informationResearch Challenges in Microgrid Technologies
Research Challenges in Microgrid Technologies Microgrid Research Programme Leader Josep M. Guerrero joz@et.aau.dk Microgrid Research Programme and Laboratories Microgrid Research Activities Microgrid Projects
More informationControl Strategies for Supply Reliability of Microgrid
Control Strategies for Supply Reliability of Microgrid K. M. Sathya Priya, Dept. of EEE Gvpcoe (A), Visakhapatnam. K. Durga Malleswara Rao Dept. of EEE GVPCOE (A), Visakhapatnam. Abstract-- Maintaining
More informationSmall Electrical Systems (Microgrids)
ELG4126: Microgrids Small Electrical Systems (Microgrids) A microgrid is a localized, scalable, and sustainable power grid consisting of an aggregation of electrical and thermal loads and corresponding
More informationAlexis Kwasinski. Power Electronic Systems Research. The University of Texas at Austin
Alexis Kwasinski Power Electronic Systems Research at The University of Texas at Austin Overview Introduction Microgrids Planning: Lifelines, renewable energy sources and energy storage availability modeling
More informationFuture of the Power System? Presented by : Yazhou (Joel) Liu, Ph.D., PE Schneider Electric Engineering Services electric.
Microgrids Future of the Power System? Presented by : Yazhou (Joel) Liu, Ph.D., PE Schneider Electric Engineering Services Yazhou.liu@us.schneider electric.com Outline What is Microgrids? Why Microgrids?
More informationPower Systems for GRID Simulation. Mahesh Thaker, Director of Engineering AMETEK Programmable Power / VTI Instruments
Power Systems for GRID Simulation Mahesh Thaker, Director of Engineering AMETEK Programmable Power / VTI Instruments Agenda AMETEK Programable Power introduction Evolution of Grid Power Simulation Growth
More informationE-Highway2050 WP3 workshop April 15 th, 2014 Brussels. Battery Storage Technology Assessment Lukas Sigrist, Comillas, Eric Peirano, TECHNOFI
E-Highway2050 WP3 workshop April 15 th, 2014 Brussels Battery Storage Technology Assessment Lukas Sigrist, Comillas, Eric Peirano, TECHNOFI Content Introduction Methodology Results Concluding remarks WP3
More informationAn approach for estimation of optimal energy flows in battery storage devices for electric vehicles in the smart grid
An approach for estimation of optimal energy flows in battery storage devices for electric vehicles in the smart grid Gergana Vacheva 1,*, Hristiyan Kanchev 1, Nikolay Hinov 1 and Rad Stanev 2 1 Technical
More information"Creating a Resilient Energy Network (Enernet) of Distributed Renewable Energy Powered Buildings
"Creating a Resilient Energy Network (Enernet) of Distributed Renewable Energy Powered Buildings Brian T. Patterson IEEE, IEC, USGBC President, EMerge Alliance Designing & Implementing Distributed Energy,
More informationPower Conditioning of Microgrids and Co-Generation Systems
Power Conditioning of Microgrids and Co-Generation Systems Nothing protects quite like Piller piller.com Content 1 Introduction 3 2 Basic requirements of a stable isolated network 3 3 Requirements for
More informationSmart Grid and Renewable Energy Workforce Development and Training Programs at Penn State University
GRIDSTAR: Grid-Smart Technology Application and Resource Center Smart Grid and Renewable Energy Workforce Development and Training Programs at Penn State University Principal Investigator: Dr. David Riley
More informationA Battery Equivalent Model for DER Services
GridWise Architecture Council A Battery Equivalent Model for DER Services June 13-15, Portland, Oregon Rob Pratt Mgr., Distribution and Demand Response Sector Pacific Northwest National Laboratory Presentation
More informationIMEON 9.12 USER MANUAL
IMEON 9.12 USER MANUAL USER MANUAL IMEON Modifications Index Indiex Date Modified pages Modification description Author A 30/09/2015 - Initial drafting F.M. Reference IMEON 9.12 Indiex A IMEON 9.12 Smart
More informationPowering the most advanced energy storage systems
Powering the most advanced energy storage systems Greensmith grid-edge intelligence Building blocks for a smarter, safer, more reliable grid Wärtsilä Energy Solutions is a leading global energy system
More informationIntroduction. TBSI Opening Ceremony. Scott Moura
Introduction TBSI Opening Ceremony Scott Moura Assistant Professor ecal Director Tsinghua Berkeley Shenzhen Institute Civil & Environmental Engineering University of California, Berkeley TBSI Opening Ceremony
More informationStorage + X: Hybrid Energy Storage Systems. Overview Dan Wishnick
Storage + X: Hybrid Energy Storage Systems Overview Dan Wishnick Medium Voltage Systems Energy Transition What does it mean? $/MW h EPA Renewables Demand Reduction CCPP Coal Plants Retirement & Reduction
More informationPower Electronics & Drives [Simulink, Hardware-Open & Closed Loop]
Power Electronics & [Simulink, Hardware-Open & Closed Loop] Project code Project theme Application ISTPOW801 Estimation of Stator Resistance in Direct Torque Control Synchronous Motor ISTPOW802 Open-Loop
More informationConclusions. Fall 2010
Conclusions ECEN 2060 Fall 2010 ECEN 2060 Topics Introduction to electric power system Photovoltaic (PV) power systems Energy efficient lighting Wind power systems Hybrid and electric vehicles 2 Electric
More informationDistribution grid congestion management Remco Verzijlbergh, section Energy and Industry, faculty of Technology, Policy and Management
Distribution grid congestion management Remco Verzijlbergh, section Energy and Industry, faculty of Technology, Policy and Management 07-01-15 Delft University of Technology Challenge the future Demand
More informationA Novel GUI Modeled Fuzzy Logic Controller for a Solar Powered Energy Utilization Scheme
1 A Novel GUI Modeled Fuzzy Logic Controller for a Solar Powered Energy Utilization Scheme I. H. Altas 1, * and A.M. Sharaf 2 ihaltas@altas.org and sharaf@unb.ca 1 : Dept. of Electrical and Electronics
More informationIntegrated System Models Graph Trace Analysis Distributed Engineering Workstation
Integrated System Models Graph Trace Analysis Distributed Engineering Workstation Robert Broadwater dew@edd-us.com 1 Model Based Intelligence 2 Integrated System Models Merge many existing, models together,
More informationEnabling Smart Grid Interoperability: A System of Systems Approach via IEEE P2030 TM and IEEE 1547 TM
Enabling Smart Grid Interoperability: A System of Systems Approach via IEEE P2030 TM and IEEE 1547 TM Standards presented May 2010 by Tom Basso Electricity, Resources and Building Systems Integration Center
More informationSOLAR POWER INTERNATIONAL Presents. The Enernet
SOLAR POWER INTERNATIONAL Presents The Enernet SOLAR POWER INTERNATIONAL Presents Doing for Electricity what the Internet did for Information Today s Presenter Brian T. Patterson President EMerge Alliance
More informationHigh-Speed High-Performance Model Predictive Control of Power Electronics Systems
High-Speed High-Performance Model Predictive Control of Power Electronics Systems S. MARIÉTHOZ, S. ALMÉR, A. DOMAHIDI, C. FISCHER, M. HERCEG, S. RICHTER, O. SCHULTES, M. MORARI Automatic Control Laboratory,
More informationOPTIMIZING ENERGY STORAGE WITH INTEGRATED RENEWABLES. Mari McGowan Director of Project Management Energy Storage Solutions
OPTIMIZING ENERGY STORAGE WITH INTEGRATED RENEWABLES Mari McGowan Director of Project Management Energy Storage Solutions Battery and Systems Expertise- Battery & Energy Products Communications Systems
More informationEnergy Management and Control for Grid Connected Hybrid Energy Storage System under Different Operating Modes
Energy Management and Control for Grid Connected Hybrid Energy Storage System under Different Operating Modes SATHIYAMURTHI.K 1 * 1 Assistant professor Department of EEE, Arignaranna institute of science
More information"The Role of DC Microgrids in Power Producing Buildings for the 21st Century Energy Network."
"The Role of DC Microgrids in Power Producing Buildings for the 21st Century Energy Network." Brian T. Patterson IEEE, IEC, USGBC President, EMerge Alliance Roundtable and Policy Committee Meeting Thursday,
More informationEnergetic Macroscopic Representation and Energy Management Strategy of a Hybrid Electric Locomotive
Energetic Macroscopic Representation and Energy Management Strategy of a Hybrid Electric Locomotive J. Baert *, S. Jemei *, D. Chamagne *, D. Hissel *, D. Hegy ** and S. Hibon ** * University of Franche-Comte,
More informationIntergrid: A Future Electronic Energy Network?
Center for Power Electronics Systems The Bradley Department of Electrical and Computer Engineering College of Engineering Virginia Tech, Blacksburg, Virginia, USA A part of Grid Technologies Collaborative
More informationBalancing act. Microgrid optimization control stabilizes production in solar and hybrid microgrids
Balancing act Microgrid optimization control stabilizes production in solar and hybrid microgrids CELINE MAHIEUX, ALEXANDRE OUDALOV Traditionally, remote, off-grid microgrids have relied on diesel generators
More informationIndia Smart Grid Week, 2017
India Smart Grid Week, 2017 N. Venu President and Head, Power Grids Division, South Asia, Middle East and Africa ABB 1 Big Shift in Power: Shaping the System of the Future Several global challenges Population
More informationSolar Microgrid Integrates Solar PV, Energy Storage, Smart Grid Functionality and Advanced Vehicle-to-Grid Capabilities
Case Study Solar Microgrid Integrates Solar PV, Energy Storage, Smart Grid Functionality and Advanced Vehicle-to-Grid Capabilities Project awarded Maryland Energy Administration s prestigious Game Changer
More information