UNIVERSITE DE TECHNOLOGIE DE COMPIEGNE Research Unit AVENUES EA 7284 Urban DC Microgrids Modeling, Optimization and Real-Time Control Prof. Manuela SECHILARIU manuela.sechilariu@utc.fr
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
UNIVERSITE DE TECHNOLOGIE DE COMPIEGNE Urban DC microgrids: Modeling, Optimization and Real-Time Control 3 Consortium/Alliance
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
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 2012-2018: 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
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
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
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
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
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
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 www.eaton.com
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
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
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
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
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
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 63202 electronic load Controller board dspace 1103 Power electronic converter 600V-100A SEMIKRON SKM100GB063D
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
2. URBAN MICROGRIDS Urban microgrids for advanced local energy management 19 Experimental platforms Platform STELLA: Smart Transport and Energy Living
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
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)
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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 0 0 0 0
4. MICROGRIDS OPTIMIZATION Urban DC microgrids: Modeling, Optimization and Real-Time Control 39 Multilayer microgrid supervisory and control principle
4. MICROGRIDS OPTIMIZATION 40 Urban microgrids for advanced local energy management K L_lim
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
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
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
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
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
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]
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 0 0 0 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
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
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
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
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
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)
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
5. MICROGRIDS REAL TIME CONTROL Urban DC microgrids: Modeling, Optimization and Real-Time Control 54 Demand side management (load shedding optimization) Power(W) 1000 900 800 700 600 500 400 300 200 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) 1000 900 800 700 600 500 400 300 200 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
5. MICROGRIDS REAL TIME CONTROL Urban DC microgrids: Modeling, Optimization and Real-Time Control 55 2500 2000 Raw data prediction Corrected prediction Measure Results p PV (W) 1500 1000 500 2500 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 Raw data prediction Corrected prediction Measure Case operation C total ( ) Load shedding ( ) PVA power limiting ( ) Optimization based predictions -0.777 0 0 Experiment 0.225 0.244 0 A postiori optimization based real conditions -0.247 0 0 2000 p PV (W) p PV (W) 1500 1000 500 2500 2000 1500 1000 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 20:00 500 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 -0.149 0 0 Experiment 0.929 0.266 0.052 A postiori optimization based real conditions 0.357 0 0 Case operation C total ( ) Load shedding ( ) PVA power limiting ( ) Optimization based predictions -0.368 0 0 Experiment 3.219 1.300 0 A postiori optimization based real conditions 2.165 0.257 0
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
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
UNIVERSITE DE TECHNOLOGIE DE COMPIEGNE Research Unit AVENUES EA 7284 Thanks for listening