Introduc)on Chris Develder

Size: px

Start display at page:

Download "Introduc)on Chris Develder"

Kory Chambers
5 years ago
Views:

Introduc)on Chris Develder PhD, Ghent University, 2003 Design and analysis of op<cal packet switching networks Professor at Ghent University since Oct.

1 Introduc)on Chris Develder PhD, Ghent University, 2003 Design and analysis of op<cal packet switching networks Professor at Ghent University since Oct Research Interests: dimensioning, modeling and op<mizing op)cal (grid/ cloud) networks; smart grids; mul<media and home networks; informa)on retrieval Visi<ng researcher at UC Davis, CA, USA, Jul- Oct (op<cal grids) Visi<ng researcher at Columbia Univ., NY, USA, (IR/IE) Industry Experience: network planning/design tools OPNET Technologies (now part of Riverbed), More info: h^p://users.atlan<s.ugent.be/cdvelder

2 Distributed smart charging of electrical vehicles Kevin Mets, Tom Verschueren, Ma^hias Strobbe, Wouter Haerick, Chris Develder Ghent University iminds Dept. of Informa<on Technology IBCN

3 Outline 1. Smart Grids? 2. Simula<on tool 3. EV charging: Peak shaving 4. EV charging: Wind balancing 5. Related research projects

4 Smart Grids Fault detec)on? Restora)on? Data processing? Privacy, security? Pricing schemes? Distributed genera)on (large scale) Green energy sources (fluctua)ng) New services & business models Distributed genera)on (small scale) ICT infrastructure Local energy storage Demand side management PHEV charging (car parks) PHEV charging (residen)al)

5 Power grid structure Transmission network (operated by TSO) Distribu)on network (operated by DSO)

6 Outline 1. Smart Grids? 2. Simula<on tool 3. EV charging: Peak shaving 4. EV charging: Wind balancing 5. Related research projects K. Mets, T. Verschueren, C. Develder, T. Vandoorn and L. Vandevelde, "Integrated SimulaAon of Power and CommunicaAon Networks for Smart Grid ApplicaAons", in Proc. 16th IEEE Int. Workshop Computer Aided Modeling, Analysis and Design of Commun. Links and Netw. (CAMAD 2011), Kyoto, Japan, Jun. 2011, pp doi: /camad

simula)on approach, power & communica<on are loosely coupled Requires careful

7 Problem Statement Simulators are already used in the two domains: Communica)on network engineering ns-2 / ns-3 Power engineering OpenDSS OMNeT++ Matlab tools In a co- simula)on approach, power & communica<on are loosely coupled Requires careful synchronisa<on Drawback: no integra<on of tools Power Grid Simulation Communication Network Simulation

8 Requirements Provide a tool to Develop and analyze control strategies Develop and analyze sorware architectures Analyze communica<on network requirements Analyze the impact on the power grid The simula<on tool must be Extensible Flexible Scalable Usable

9 Our solu)on Integrated (combined) power grid and communica<on network simula<on à Large scale smart grid simula<ons Application Layer DSM/DR AMI PMU Middleware Layer Power Grid Simulation Communication Network Simulation

representations Data logging, presentation, processing, etc. Open source.

10 OMNeT++ Discrete Event Simulator: Modular, Scalable, Cluster support Models for communication networks Integrated in Eclipse Random Data Generation Graphical representations Data logging, presentation, processing, etc. Open source. Custom Components: Electric components: loads, generators, etc. ICT components: smart devices, coordination services,

11 Power Flow Simulator Support for radial distribution grid topologies. Model based on Fast Harmonic Simulation Method [1]. Model implemented in MATLAB and integrated in simulator. Uses an Iterative forward/backward sweep method: INPUT: - Power demand (Watt) at each node at time t. - Phase to which each node is connected LOOP: Backward sweep Determines currents in every node, based on known voltages in each node. Currents in all network branches are determined. Forward sweep Determines voltage at every node Compare voltages with the voltages in the previous iteration. If difference below a certain threshold: Stop iterations. Else Continue iterations. [1] L. Degroote, L. Vandevelde, and B. Renders, Fast harmonic simulation model for the analysis of network losses with converterconnecter distributed generation, Electric Power System Research, vol. 80, pp , 2010.

12 Communica)on Network Models INET Framework: Open source communica<on network simula<on package for OMNeT++ Layer Transport Network Link Protocol TCP UDP IPv4 IPv6 Ethernet (WiFi) PPP Implemented as OMNeT++ modules. à Simulate different technologies or basic OMNet++ message framework: No specific protocol or physical layers are simulated à Reduced overhead compared to INET

Simulator Configura)on Nodes Implementa<on of ICT and/or power components The nodes and interconnec<ons between nodes form the power grid and communica<on network that form the simulated smart grid.

13 Simulator Configura)on Nodes Implementa<on of ICT and/or power components The nodes and interconnec<ons between nodes form the power grid and communica<on network that form the simulated smart grid. 1 Network Descrip<on File List of used modules Electrical network model Communica<on network Smart devices Topology Electrical Communica<on 2 5 INI File Node parameters Node type Generator capacity Ba^ery charge rate... Simula0on parameters # houses, # devices, Communica<on technologies Device proper<es Control algorithms 3 4 Defining input files E.g., load profiles

14 Outline 1. Smart Grids? 2. Simula<on tool 3. EV charging: Peak shaving 4. EV charging: Wind balancing 5. Related research projects K. Mets, R. D'hulst and C. Develder, "Comparison of intelligent charging algorithms for electric vehicles to reduce peak load and demand variability in a distribuaon grid", J. Commun. Netw., Vol. 14, No. 6, Dec. 2012, pp doi: /jcn

What is minimal impact on load peaks we could theore<cally

15 Example case study: EV charging Research ques<ons: 1. What is impact of EV charging in residen<al environment? 2. What is minimal impact on load peaks we could theore<cally achieve? 3. How can we minimize the impact of EV in prac<ce?

16 Impact of EV charging Sample analysis for 150 homes, x% of them own a PHEV BAU = maximally charge upon arrival at home Total load (kwh) No PHEVs BAU- 10% BAU- 30% BAU- 60% 100% 80% 60% 40% 20% Addi<onal Power Consump<on Addi<onal Peak Load 0 12:00 13:30 15:00 16:30 18:00 19:30 21:00 22:30 00:00 01:30 03:00 04:30 06:00 07:30 09:00 10:30 12:00 Time (h) 0% 10% 30% 60% PHEV Penetra)on (%)

17 Controlling EV charging? Objec<ves: Reduce peak load Fla^en (total) load profile (= reduce <me- variability) Avoid voltage viola<ons Total load (kwh) :00 14:15 16:30 18:45 21:00 23:15 01:30 03:45 06:00 08:15 10:30 Time (h) Charging schedule Total load (kwh) Charging Rate (W) Time Slot :00 14:15 16:30 18:45 21:00 23:15 01:30 03:45 06:00 08:15 10:30 Time (h)

18 Smart charging algorithms Quadra)c Programming (QP) Offline algorithm Planning window Benchmark Three approaches: Local Itera<ve Global Mul)- Agent System (MAS) Online algorithm No planning window à current <me slot info only (but EV bidding changes when charging deadline approaches) Realis<c Single approach Reference scenario: uncontrolled charging

19 Smart charging: QP BAU (uncontrolled) Local control (QP) Global control (QP) Market MAS

20 Market- based MAS Market agent Household agent Household agent Transformer agent EV agent UL agent UL agent

21 Case study 63 Households Randomly distributed over 3 phases Spread over 3 feeders Electrical vehicles PHEV: 15 kwh ba^ery Full EV: 25 kwh ba^ery Randomized arrivals (~5pm) and departures (~6am) Scenario PHEV 3.6 kw PHEV 7.4 kw EV 3.6 kw EV 7.4 kw Light Medium Heavy

Results (1) Load profiles Power consum)on (kw) Power consum)on (kw) 120 100 80 60 40 20 0 300 250 200 150 100 50 0 Light Uncontrolled

22 Results (1) Load profiles Power consum)on (kw) Power consum)on (kw) Light Uncontrolled Local Global MAS 12:00 15:00 18:00 21:00 0:00 3:00 6:00 9:00 12:00 Heavy 12:00 15:00 18:00 21:00 0:00 3:00 6:00 9:00 12:00 Time (hh:mm)

23 Results (2) Load peaks & variability QP1 = local QP2 = iterative QP3 = global

24 Results (3) Voltage devia)ons Not solved en<rely! (No explicit part of objecave funcaon!) Note: 10 slots ~ 3.4% of the time

25 Outline 1. Smart Grids? 2. Simula<on tool 3. EV charging: Peak shaving 4. EV charging: Wind balancing 5. Related research projects

26 Distributed genera)on (DG)

27 Distributed genera)on (DG) Mo<va<on for DG Use renewable energy sources (RES) reduc<on of CO 2 Energy efficiency, e.g., Combined Heat and Power (CHP) Genera<on close to loads Deregula<on: open access to distribu<on network Subsidies for RES Technologies Wind turbines Photovoltaic systems CHP (based on fossil fuels or RES) Hydropower Biomass

28 Technical impact of DG?

29 Technical impact of DG? Voltage varia<ons Islanding? Feeder disconnected from grid DG may be unsafe for people & equipment Power quality Transient voltage varia<ons (during connec<on/disconnec<on) Cyclic varia<ons of generator output Protec<on Increase of fault currents

30 Wind turbines Horizontal axis Upwind vs downwind Needs to be pointed into the wind High rota<onal speed (10-22 rpm) Needs a lot of space (cf m high; blades 20-40m) Ver<cal axis Omnidirec<onal No need to point to wind Lower rota<onal speed Can be closer together E.g., h`p:// fp7.eu/ Darrieus Savonius

31 A typical wind profile

32 Worldwide wind power installed capacity

33 Worldwide wind power capacity & genera)on Canada 2% Portugal 2% United Kingdom 3% Italy 3% France 3% India 7% Installed Capacity 2011 Spain 9% Rest of world 13% Germany 12% China 26% United States 20% Portugal 3% Canada United 2% Kingdom Italy 3% 2% India 6% France 3% Produc)on 2010 Rest of world 14% Spain 13% China 16% Germany 11% United States 27%

34 Case Study K. Mets, F. De Turck and C. Develder, "Distributed smart charging of electric vehicles for balancing wind energy", in Proc. 3rd IEEE Int. Conf. Smart Grid CommunicaAons (SmartGridComm 2012), Tainan City, Taiwan, 5-8 Nov. 2012, pp doi: / SmartGridComm

35 Wind balancing Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7 Power (W) :00 6:00 12:0018:000:00 6:00 12:0018:000:00 6:00 12:0018:000:00 6:00 12:0018:000:00 6:00 12:0018:000:00 6:00 12:0018:000:00 6:00 12:0018:000:00 Uncontrolled Time Wind energy Imbalance between supply and demand Inefficient use of renewable energy sources Imbalance costs High peak loads Undesirable!

36 Architecture Coordinator Exchange of control messages to itera<vely nego<ate charging plans for a specific period of <me. Balance Responsible Party EV EV

37 Electric vehicle model Minimize disu<lity: Charging schedule variables: x t k = charging rate for user k at <me t Spread demand over <me, preferably at the preferred charging rate (p k ), which is the maximum supported charging rate in our case. Model behavior/preferences of the subscriber (β k ) Charging schedule for a window of T <me slots: minimize disu<lity (1) (2) Respect energy Requirement: (3) Vehicle can only be charged between arrival <me S k and departure <me T k

38 Balance Responsible Party Model Imbalance Costs Minimize imbalance costs: cost penalty if supply demand Supply: wind energy (w t ) Demand: total of all electric vehicles (d t ) Tuning parameter: α Cost func<on: For a planning window of T <me slots, minimize:

39 Centralized Op)miza)on Model Based on social welfare maximiza<on Minimize imbalance costs Minimize user disu<lity Objec<ve: Drawbacks: 1) Privacy: sharing of cost & disu<lity func<ons, arrival/ departure info, 2) Scalability Global constraints: Local constraints: BRP: supply < limit EV: energy & <me constraints

40 Distributed op)miza)on model Move demand- supply constraint into objec<ve, w/ Lagrange mul<plier λ t No<ce: Objec<ve func<on is separable into K+1 problems that can be solved in parallel (assuming λ t are given) 1 BRP problem K subscriber problems Itera<vely update pricing vector

41 Distributed op)miza)on model scheme: 1. Coordinator distributes virtual prices 2. BRP solves local problem in parallel 3. Subscribers solve local problem 4. Coordinator collects schedules: BRP: EVs: 5. Coordinator updates virtual prices: 6. Repeat un<l demand = supply

42 Case study: Assump)ons Wind energy supply EV energy consump<on Energy supply = 6.8 MWh 100 Electric vehicles Ba^ery capacity: 10 kwh ba^ery Maximum charge power: 3.68 kw Arrivals & departures: sta<s<cal model Charging at home scenario Time Simulate 4 weeks Time slots of 15 minutes Planning window of 24 hours

43 Case study: Algorithms Uncontrolled business as usual (BAU) EV starts charging upon arrival EV stops charging when state- of- charge is 100% No control or coordina<on Distributed algorithm Executed at the start of each <me slot Ideal world benchmark Offline all- knowing algorithm determines schedules for ALL sessions No EV disu<lity func<on à maximum flexibility Objec<ve: min

44 Results: Uncontrolled BAU vs. Distributed Power (W) Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7 0 0:00 6:00 12:0018:00 0:00 6:00 12:0018:00 0:00 6:00 12:0018:00 0:00 6:00 12:0018:00 0:00 6:00 12:0018:00 0:00 6:00 12:0018:00 0:00 6:00 12:0018:00 0:00 Time Uncontrolled Wind energy Power (W) Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7 0 0:00 6:00 12:0018:00 0:00 6:00 12:0018:00 0:00 6:00 12:0018:00 0:00 6:00 12:0018:00 0:00 6:00 12:0018:00 0:00 6:00 12:0018:00 0:00 6:00 12:0018:00 0:00 Time Distributed Wind energy

45 Results: Distributed vs. Benchmark Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7 Power (W) :00 6:00 12:0018:00 0:00 6:00 12:0018:00 0:00 6:00 12:0018:00 0:00 6:00 12:0018:00 0:00 6:00 12:0018:00 0:00 6:00 12:0018:00 0:00 6:00 12:0018:00 0:00 Time Benchmark Distributed Wind energy

Results: Energy Mix 8.00 40% 68% 73% 1600 1450 7.00 1400 Energy supply (MWh) 6.00 5.

99 CO2 emissions (kg) 1200 1000 800 600 400 200 45% 798 56% 633 0.

Benchmark Charging strategy Renewables Non renewables Renewables: 7.

46 Results: Energy Mix % 68% 73% Energy supply (MWh) CO2 emissions (kg) % % Uncontrolled Distributed Benchmark Charging strategy 0 Uncontrolled Distributed Benchmark Charging strategy Renewables Non renewables Renewables: 7.4 CO2 g/kwh Non Renewables: CO2 g/kwh Total energy consump<on 6.8 MWh Substan<al increase in the use of renewable energy Reduced CO 2 emissions

47 Conclusions Objec)ve: balance wind energy supply with electric vehicle charging demand Method: Distributed coordina<on algorithm in which par<cipants exchange virtual prices and energy schedules Performance: Distributed coordina<on significantly be^er than BAU, close to ideal world benchmark Increased usage of renewable energy sources Reduc<on of CO 2 emissions

48 Outline 1. Smart Grids? 2. Simula<on tool 3. EV charging: Peak shaving 4. EV charging: Wind balancing 5. Wrap- up

49 Future & ongoing work Communica<on network architecture C- DAX concept: generic smart grid middleware Hierarchical architecture, e.g., using data aggregators to reduce communica<on overhead Communica<on network requirements, impact of communica<on problems, etc. Algorithm development Stochas<c behavior Mul<ple balancing zones Vehicle- to- grid support

50 Thank you any ques)ons?

51 Thank you any ques)ons? Prof. Chris Develder Ghent University iminds

52 References (1/2) K. Mets, R. D hulst, and C. Develder, Comparison of intelligent charging algorithms for electric vehicles to reduce peak load and demand variability in a distribu<on grid, J. Commun. Netw., vol. 14, no. 6, pp , Dec K. Mets, F. De Turck, and C. Develder, Distributed smart charging of electric vehicles for balancing wind energy, in Proc. 3rd IEEE Int. Conf. Smart Grid CommunicaAons (SmartGridComm 2012), Tainan City, Taiwan, 2012, pp K. Mets, W. Haerick, and C. Develder, A simulator for the control network of smart grid architectures, in Proc. 2nd Int. Conf. InnovaAon for Sustainable ProducAon (i- SUP 2010), Bruges, Belgium, 2010, vol. 3, pp K. Mets, M. Strobbe, T. Verschueren, T. Roelens, C. Develder, and F. De Turck, Distributed Mul<- Agent Algorithm for Residen<al Energy Management in Smart Grids, in Proc. IEEE/IFIP Netw. OperaAons and Management Symp. (NOMS 2012), Maui, Hawaii, USA, K. Mets, T. Verschueren, F. De Turck, and C. Develder, Evalua<on of Mul<ple Design Op<ons for Smart Charging Algorithms, in Proc. 2nd IEEE ICC Int. Workshop on Smart Grid Commun., Kyoto, Japan, K. Mets, T. Verschueren, F. De Turck, and C. Develder, Exploi<ng V2G to Op<mize Residen<al Energy Consump<on with Electrical Vehicle (Dis)Charging, in Proc. 1st Int. Workshop Smart Grid Modeling and SimulaAon (SGMS 2011) at IEEE SmartGridComm 2011, Brussels, Belgium, 2011, pp K. Mets, T. Verschueren, C. Develder, T. Vandoorn, and L. Vandevelde, Integrated Simula<on of Power and Communica<on Networks for Smart Grid Applica<ons, in Proc. 16th IEEE Int. Workshop Computer Aided Modeling, Analysis and Design of Commun. Links and Netw. (CAMAD 2011), Kyoto, Japan, 2011, pp K. Mets, T. Verschueren, W. Haerick, C. Develder, and F. De Turck, Op<mizing Smart Energy Control Strategies for Plug- In Hybrid Electric Vehicle Charging, in Proc. 1st IFIP/IEEE Int. Workshop on Management of Smart Grids, at 2010 IEEE/IFIP Netw. OperaAons and Management Symp. (NOMS 2010), Osaka, Japan, 2010, pp

53 References (2/2) M. Strobbe, K. Mets, M. Tahon, M. Tilman, F. Spiessens, J. Gheerardyn, K. De Craemer, S. Vandael, K. Geebelen, B. Lagaisse, B. Claessens, and C. Develder, Smart and Secure Charging of Electric Vehicles in Public Parking Spaces, in Proc. 4th Int. Conf. InnovaAon for Sustainable ProducAon (i- SUP 2012), Bruges, Belgium, M. Strobbe, T. Verschueren, K. Mets, S. Melis, C. Develder, F. De Turck, T. Pollet, and S. Van de Veire, Design and Evalua<on of an Architecture for Future Smart Grid Service Provisioning, in Proc. 4th IEEE/IFIP Int. Workshop on Management of the Future Internet (ManFI 2012), Maui, Hawaii, USA, 2012, pp T. Verschueren, K. Mets, W. Haerick, C. Develder, F. De Turck, and T. Pollet, Architectures for smart end- user services in the power grid, in Proc. 1st IFIP/IEEE Int. Workshop on Management of Smart Grids, at 2010 IEEE/IFIP Netw. OperaAons and Management Symp. (NOMS 2010), Osaka, Japan, 2010, pp T. Verschueren, K. Mets, B. Meersman, M. Strobbe, C. Develder, and L. Vandevelde, Assessment and mi<ga<on of voltage viola<ons by solar panels in a residen<al distribu<on grid, in Proc. 2nd IEEE Int. Conf. Smart Grid CommunicaAons (SmartGridComm 2011), Brussels, Belgium, 2011, pp C. Develder, W. Haerick, K. Mets, and F. De Turck, Smart Grids and the role of ICT, in Proc. IEEE Smart Grid Comms Workshop, at IEEE Int. Conf. on Commun. (ICC 2010), Cape Town, South Africa, W. Labeeuw, S. Claessens, K. Mets, C. Develder, and G. Deconinck, Infrastructure for Collabora<ng Data- Researchers in a Smart Grid Pilot, in Proc. 3rd IEEE PES InnovaAve Smart Grid Technologies Europe (ISGTEU 2012), Berlin, Germany, 2012, pp. 1 8.

Introduc)on Chris Develder

Introduc)on Chris Develder Professor at Ghent University since Oct. 2007 Research Interests: smart grids; dimensioning, modeling and op@mizing op)cal (grid/cloud) networks; informa)on retrieval/extrac)on