Intelligent Energy Management System Simulator for PHEVs at a Municipal Parking Deck in a Smart Grid Environment
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1 Intelligent Energy Management System Simulator for PHEVs at a Municipal Parking Deck in a Smart Grid Environment Preetika Kulshrestha, Student Member, IEEE, Lei Wang, Student Member, IEEE, Mo-Yuen Chow, Fellow, IEEE and Srdjan Lukic, Member, IEEE North Carolina State University Raleigh, NC
2 Outline Introduction System architecture Component description System simulator Sample system simulation Future work Intelligent Energy Management System Simulator for PHEVs at a Municipal Parking Deck in a Smart Grid Environment 2
3 Emergence of the Smart Grid Optimization of power delivery - Capacity to deliver efficiently, reliably and intelligently Features Decentralization of control Services customized to user s needs Use of energy efficient systems Rapid reconfiguration Intelligent Energy Management System Simulator for PHEVs at a Municipal Parking Deck in a Smart Grid Environment 3
4 Introduction to PHEVs HEV with larger battery pack Can be charged from standard wall outlet 40 mile all-electric range (Chevy Volt) Benefits Reduction in GHG emissions Reduction on oil dependence MW Load Off peak charging Load leveling Source: Prometheus Institute Lower cost Load leveling during extreme load events Time of Day Potential Synergistic Relation A cluster of vehicles is a controllable load for the grid Intelligent Energy Management System Simulator for PHEVs at a Municipal Parking Deck in a Smart Grid Environment 4
5 Opportunities and Challenges Opportunities US fleet s 176 million light vehicles = power capacity of 19.5TW= 24 x power capacity of the electric generation system. PHEV penetration by % of the US fleet (EPRI prediction) Challenges Potential load of 1000 cars => 4 MW load Potential dangers => Voltage instability and blackouts Infrastructure Need of an underlying framework to enable PHEV integration A Solution Intelligent Energy Management at a Municipal Parking Deck Intelligent Energy Management System Simulator for PHEVs at a Municipal Parking Deck in a Smart Grid Environment 5
6 Related Work J. Tomic and W. Kempton, Using fleets of electric-drive vehicles for grid support, J. Power Sources, vol. 168, issue 2, 2007 M. Duvall and E. Knipping, Environmental assessment of Plug-in Hybrid Electric vehicles, EPRI, July 2007 Hutson, G. K. Venayagamoorthy, K. A. Corzine, Intelligent Scheduling of Hybrid and Electric Vehicle Storage Capacity in a Parking Lot for Profit Maximization in Grid Power Transactions, in proc. IEEE Energy2030, Atlanta, GA, 2008 S. B. Pollack et al, patent title User interface and user control in a power aggregation system for distributed electric resources, IPC8 Class: AG01R2106FI, USPC Class: GridPoint: Intelligent Energy Management System Simulator for PHEVs at a Municipal Parking Deck in a Smart Grid Environment 6
7 System Architecture Intelligent Energy Management System Simulator for PHEVs at a Municipal Parking Deck in a Smart Grid Environment 7
8 Information Flow Utility User Profile Entry Pricing Available power iems Optimization Data Acquisition (DSP, FPGA etc.) Power Allocated Rate of charge Other control messages Vehicle 1 Vehicle 2 Time of availability Type of charge Pricing preferences Current state of charge Power consumed.. Communication Medium: Wi-Fi, Bluetooth, Satellite ZigBee etc. Vehicle Vehicle n Intelligent Energy Management System Simulator for PHEVs at a Municipal Parking Deck in a Smart Grid Environment 8
9 System - Component Description States Idle: There is no activity, the load may be waiting for a control action or it may have completed charging the battery. Data Acquisition: The data is acquired from the battery and the user. Communication: Data is communicated to controller. Charging: Battery charging is in progress. Error: There is an error in the system and system operation is halted until error is resolved. State transitions A - Idle B - Data Acquisition C - Communication D - Charging E - Stop Intelligent Energy Management System Simulator for PHEVs at a Municipal Parking Deck in a Smart Grid Environment 9
10 System - Component Description Initialize T=T sample or request received for update or new load has plugged in? Data from Loads Time of Availability User Preferences Initial State of Charge Data Acquisition Yes Sample instantaneous power and SOC of load Optimize on power subject to constraints Information from Utility Total Power Available Pricing A Plug- in/ Update Request, Tsample A - Communicate B- Optimize Allocate Power to loads B States iems flow chart State transitions Communication: Inform the loads of power allocated/listen for signals Optimize: Calculation of power allocation when: There is a change in utility power. A load has plugged-in/out. Periodically, after sampling the instantaneous power consumed by loads. Intelligent Energy Management System Simulator for PHEVs at a Municipal Parking Deck in a Smart Grid Environment 10
11 System - Component Description Functions Periodically inform the iems about the power available and pricing information Intelligent Energy Management System Simulator for PHEVs at a Municipal Parking Deck in a Smart Grid Environment 11
12 iems - System Simulator Notify Plug in Vehicle Information Discrete, Ts = 5 s. powergui Clock time Simulation Time Input Rate and Allocated Power Completed VA UpdateI Station I Notify Plug in Vehicle Information Input Rate and Allocated Power Completed VB UpdateII Station II Input from car I Car I Notify Plug in Input from car II Car II Vehicle Information Utility Input from car III Input from car IV Input from car V Car III Car IV Input Rate and Allocated Power Completed VC Power Power Car V UpdateIII iems Station III Notify Plug in Vehicle Information Input Rate and Allocated Power Completed VD UpdateIV Station IV Hybrid System Notify Plug in Vehicle Information Input Rate and Allocated Power Completed VE UpdateV Station V Discrete Continuous Dynamics Intelligent Energy Management System Simulator for PHEVs at a Municipal Parking Deck in a Smart Grid Environment 12
13 Sample System Simulation A SoC in = 70% D SoC in = 40% T plug out = 10:40 am T plug out = 11:36 am B C SoC in = 50% 8:00 am 9:00 am 10:00 am 11:00 am 12:00 am 1:00 pm E SoC T plug out = 11:22 am in = 0% T T plug out = 1:04 pm plug out = 10:17 am SoC in = 10% Objective function: ( ) max J( k) = w ( k) SoC k + j p w i (k): the priority assigned for to vehicle i at time step k Priorities are assigned based on capacity required and time remaining j i i i Power (W) Plot of power consumption of five cars PowerA PowerB PowerC PowerD PowerE Total Power Available Power Time (s) x 10 4 Intelligent Energy Management System Simulator for PHEVs at a Municipal Parking Deck in a Smart Grid Environment 13
14 Monte Carlo Simulation Simulation Parameters State of Charge at plug-in: Uniform random number between 10% and 75% Time of Availability: Uniform random number between 0.5 and 2 hours Time of Plug-in: Uniform random number between 0 and 2 hours Simulation Run Time: 4 hours Battery Capacity: Uniformly distributed between 6 Ah and 15 Ah Number of times the simulation was run for each algorithm: 100 Percentage of vehicles leaving with SoC 55% or higher Number of vehicles leaving with SoC 35% or lower Optimal Allocation for SoC Maximization 81.8% (409) 2 0.4% Percentage of Vehicles (%) State of Charge at Plug out - Optimal Allocation for SoC Maximization SOC at Plug out Dynamic Priority Allocation 69.6% (348) % Equal Priority Allocation 67.6% (339) % Intelligent Energy Management System Simulator for PHEVs at a Municipal Parking Deck in a Smart Grid Environment 14
15 Conclusions This paper proposes an iems for managing power at a parking deck System components, functions and behavior are outlined A simulator (test-bed) is developed to simulate the real world scenario The simulator will contribute towards evaluation of varied scenarios and iems algorithms Optimization on a chosen objective is formulated and simulation results presented Intelligent Energy Management System Simulator for PHEVs at a Municipal Parking Deck in a Smart Grid Environment 15
16 Future Work Exploration of different objectives for optimization Extension of the problem to multi-objective optimization and incorporation of additional constraints Network in the Loop iems performance evaluation with communication delay, packet drop, and signal strength Decision on optimal sampling time Extension of the concept to distributed control Real world implementation and demonstration of the iems Intelligent Energy Management System Simulator for PHEVs at a Municipal Parking Deck in a Smart Grid Environment 16
17 Acknowledgement This work was partially supported by the National Science Foundation (NSF) under Award Number EEC Intelligent Energy Management System Simulator for PHEVs at a Municipal Parking Deck in a Smart Grid Environment 17
18 THANK YOU! Intelligent Energy Management System Simulator for PHEVs at a Municipal Parking Deck in a Smart Grid Environment 18
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