Energy Management Systems Annabelle Pratt Power Research Engineer Energy Systems Research, Intel Labs annabelle.pratt@intel.com
Contents • Project goal • Device-level management, e.g. plug-in electric vehicle • Building-level management, e.g. home • Collective level management, e.g. EV charging aggregator • Collaboration opportunities
Proj roject ect go goal l Our research aims to develop Energy Management Systems which shape the power demand of devices, buildings and collections of buildings in order to benefit individual consumers by minimizing their energy cost, and society at large by enabling efficient, reliable Smart Grids with significant renewable generation.
Multi-level management approach Utility control center • Device level (Device controller) Collective level – Only in smart loads/DR, e.g. EV Aggregator Aggregator – Single device optimization (quadratic) Aggregator Building automation level • Building level (HEMS/BEMS) – Optimization of several devices DR Agent BEMS HEMS HEMS – Multi-objective (search) Device level • Collective level (Aggregator) – Optimization across collection of Device Ctrl Device Ctrl Device Ctrl buildings & shared resources * DR = Distributed Resource – Linear opt/multi-agent system?
Multi-level modeling tools • Simulation platform in Matlab and Simulink being developed in collaboration with the University of Colorado – Short (sec/min) and long (hr/day) time scales – Microgrid can operate off-grid (islanded) and grid-tied – ensure seamless disconnection & re-connection Microgrid Microgrid EMS
Multi-level examples and results • Device-level : plug-in electric vehicles (PEVs) – significant and potentially intelligent loads • Building-level : Home Energy Management System – targeting next generation HEMS products • Collective level : EV charging aggregator – at early stages, with preliminary results Demo at http://www.intel.com/embedded/energy/homeenergy/demo/index.html
Collective Five residences charging EVs Building Device Consumers billed based on time of use electricity pricing Simple timer to delay start time of charging P txf Distribution transformer EV#3 EV#4 EV#5 EV#2 Utility Control Center AMI Advanced Metering EV#1 Infrastructure Smart EVSE Meter Collaboration with University Electric Vehicle of Colorado, Boulder Supply Equipment (EVSE) 7
Collective Building Device Total power through transformer (Ptxf) Contribution of PEVs to Ptxf Note time scale : time plotted from noon on Day 1 through noon on Day 2 Simple time delay Charges during minimum load period 8
Collective Smart charging of individual EVs Building Device Electricity cost profile provided to vehicle Charging App in vehicle determines charging profile P txf Distribution transformer EV#3 EV#4 EV#5 EV#2 Utility IP Control Center Internet Protocol Home Energy Mgmt System AMI (HEMS) EV#1 Smart EVSE Meter Collaboration with University of Colorado, Boulder 9
Collective Building Device Simple time delay Intelligent vehicle optimizer Charges during minimum load Minimizes cost and charging rate of PEV period 10
Collective Home Energy Management System Building Device Electricity cost profile provided to the HEMS HEMS determines optimal setpoints for all controllable appliances P txf Power & ambient sensors Earliest and Latest Distribution start times; stove to transformer start before dryer HEMS Utility Min and max temps IP Control Center Internet Protocol AMI EV#1 Fixed Load Smart EVSE Meter Max power, charge/discharge; P=0 when not plugged in; SOC=100% by target time 11
Collective Building Preliminary results : Single optimization result Device • 8.5% cost savings; balanced with user comfort – EV charging not lower cost, but grid-friendly Tstpt Tstpt0 Optimized results Initial guess Troom Troom 30 30 Tout Tout Temp [degC] Temp [degC] 20 20 10 10 0 PEV 0 PEV0 0 5 10 15 20 25 0 5 10 15 20 25 PEV0-opt Pdryer Pdryer Pstove Pstove Pavg-th 10 10 Pavg-th Pfixed Pfixed 5 5 0 0 -5 -5 0 5 10 15 20 25 0 5 10 15 20 25 price [c/kWh] price [c/kWh] cum energy cost [$] cum energy cost [$] 20 20 10 10 0 0 0 5 10 15 20 25 0 5 10 15 20 25 Time [h] Time [h]
Aggregator Building Preliminary results : V2G enabled / home storage Device • 27% energy cost savings with V2G enabled higher load at night • not direct comparison with previous Optimized results Initial guess 30 30 Temp [degC] Temp [degC] 20 20 10 10 0 0 PEV PEV0 0 5 10 15 20 25 0 5 10 15 20 25 Pdryer Pdryer Pstove Pstove Pavg-th Pavg-th 5 5 Pfixed Pfixed 0 0 -5 -5 0 5 10 15 20 25 0 5 10 15 20 25 price [c/kWh] price [c/kWh] cum energy cost [$] cum energy cost [$] 20 $4.95/day 20 27% 10 10 0 0 0 5 10 15 20 25 0 5 10 15 20 25 Time [h] Time [h]
Collective Building Proposed Aggregator Device • Coordinates with all the HEMS/BEMS. • May be implemented on a local device or as a cloud service • Example functions : – Determining the optimal solution for a collection of buildings. Most applicable to a campus with a single building owner – Interacting with the utility-issued demand response requests – Maximizing run-time when operating off-grid, e.g. for a microgrid. – Protecting local infrastructure (distribution transformer) through adjustment of local electricity price
Collective EV charging aggregator Building Device Aggregator sets local electricity price HEMS not yet included P txf Distribution transformer EV#3 EV#4 EV#5 EV#2 Utility PEV Control Aggregator Center AMI EV#1 Smart EVSE Meter Collaboration with University of Colorado, Boulder 15
Collective Building Device Intelligent vehicle optimizer only With PEV Aggregator Minimizes cost and charging rate of PEV Limits total PEV power by adjusting local electricity price 16
Collaboration opportunities • Energy Management algorithms – Optimization engines, load forecasting, thermal models for buildings, user behavior modeling and influencing, etc. • Prototyping – Device level: smart charging on PEV emulator and then on PEV – Building level : test HEMS optimization algorithms in a home – as allowed by controllable appliances available, and EV capabilities – Collective level : HEMS interaction with utility through aggregator
Device level : PEV • Demonstrates operation of Charging App in conjunction with Battery Management System to implement optimal charging • Implementation on vehicle to follow Vehicle emulator Load (emulates traction) AC 120/240 EMI EVSE AC/DC DC/DC Vac filter Gate drive signals Charger Charging Battery Utility Control Electricity App Management Center model price System IVI System Laptop
Building level : HEMS • Demonstrate HEMS algorithms in real (occupied) homes – to the extent possible, determined by controllable appliance availability and PEV capabilities – First in ESR homes Power & – Then Intel homes ambient sensors – Then external Artificial price HEMS signal & service requests EV#1 Fixed Load Smart EVSE Meter
Collective level : neighborhood • Detailed simulations • Field trial with external partner P txf Distribution transformer EV#3 EV#4 EV#5 EV#2 Utility Aggregator Control Center AMI EV#1 Smart EVSE Meter 20
Thank You Please visit the Intelligent management of Electric Vehicles demo Contact me at : annabelle.pratt@intel.com
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