10d092a5b37df0d28605700f53ab69e7.ppt

- Количество слайдов: 76

EE 5900: Advanced Embedded System For Smart Infrastructure Single User Smart Home

Smart Grid 2

Classical Power System v. s. Smart Grid 3

The Classical Power System 4

Smart Grid: Making Every Component Intelligent Distributed Generation and Alternate Energy Sources Real-time Simulation and Contingency Analysis Self-Healing Wide-Area Protection and Islanding Smart Grid Asset Management and On-Line Equipment Monitoring Demand Response and Dynamic Pricing Clean Reliable Secure Energy Efficient Money Efficient Smart Home 5

IBM Smarter Planet 6

Renewable Energy 7

The Integrated Power and Communication System 8

Smart Power Transmission and Distribution § § § More devices integrated such as IED, PMU, FRTU, FDR Improved monitoring and control Improved cybersecurity Energy efficiency Expense efficiency 9

Smart Community http: //www. meti. go. jp 10

Smart Home § Smart home technologies are viewed as users end of the Smart Grid. § A smart home or building is equipped with special structured wiring to enable occupants to remotely control or program an array of automated home electronic devices. § Smart home is combined with energy resources at either their lowest prices or highest availability, e. g. taking advantage of high solar panel output. http: //www. yousharez. com/2010/11/20/house-of-dreams-a-smart-house-concept/ 11

Smart Home System 12

Smart Appliances Characterized by • Compact OS installed • Remotely controllable • Multiple operating modes 13

Home Appliance Remote Control 14

Zig. Bee Home Area Network (HAN) http: //www. zigbee. org/ 15

Zig. Bee Local Area Network (LAN) 16

Smart Home Deployment in Urban Area 17

Relationship With Smart Building 18

Property 1: Dynamic Pricing from Utility Company Price ($/kwh) Illinois Power Company’s price data Pricing for one-day ahead time period 19

Property 2: Renewable Energy Resource § Marcelo Gradella Villalva, Jonas Rafael Gazoli, and Ernesto Ruppert Filho. Comprehensive Approach to Modeling and Simulation of Photovoltaic Arrays. IEEE Transactions on Power Electronics, Vol. 24, No. 5, May 2009 20

Benefit of Smart Home – Reduce monetary expense – Reduce peak load – Maximize renewable energy usage 21

Smart Home System Flow Power flow Internet Control flow 22

Smart Home Scheduling § Smart Home Scheduling – when to launch a home appliance – at what frequency or power level – The variable frequency drive (VFD) is to control the rotational speed of an alternating current (AC) electric motor through controlling the frequency of the electrical power supplied to the motor – for how long – use grid energy or renewable energy – use battery or not § Closely related to Demand Side Management – Demand Side Management is a top down approach – Smart Home Scheduling is a bottom up approach 23

Start Dish washer End 13: 00 18: 00 Landry machine 09: 00 18: 00 PHEV 18: 00 08: 00 AC 17: 00 N/A …… 24

Electric Vehicles (EV) Powered by one or more Electric Motors 25

Plug-in Hybrid Electric Vehicles (PHEV) Powered by an Electric Motor and Engine • Internal combustion engine uses alternative or conventional fuel • Battery charged by outside electric power source, engine, or regenerative breaking • During urban driving, most power comes from stored electricity. Long trips require the engine 26

Charging of PHEV at Home § 2014 Honda Accord PHEV 120 -volt: less than 3 hours 240 -volt: one hour § 2013 Toyota Prius PHEV 120 -volt: less than 3 hours 240 -volt: 1. 5 hours § 2014 Chevrolet Volt PHEV 120 -volt: 10 – 16 hours 240 -volt: 4 hours Using mobile connector 29 miles of range per hour charge The fastest way to charge at home 58 miles of range per hour charge 27

VFD Impact Powerr Power 5 cents/kwh 3 cents / kwh 10 kwh 5 kwh 1 2 (a) Time 1 2 (b) 3 Time cost = 10 kwh * 5 cents/kwh = 50 cents cost = 5 kwh * 5 cents/kwh + 5 kwh * 3 cents/kwh = 40 cents 28

Uncertainty of Appliance Execution Time and Energy Consumption § In advanced laundry machine, time to do the laundry depends on the load. How to model it? 29

Problem Formulation § Given n home appliances, to schedule them for monetary expense minimization considering multiple power level considering variations – Solutions for continuous VFD/power level – Solutions for discrete VFD/power level Solutions for continuous VFD Solutions for discrete VFD 1 2 3 4 30

The Procedure of the Our Proposed Scheme Offline Schedule A deterministic scheduling with continuous power level A deterministic scheduling with discrete power level Stochastic Programming for Appliance Variations Online Schedule for Renewable Energy Variations 31

The Proposed Scheme Outline A deterministic scheduling with continuous power level A deterministic scheduling with discrete power level • Optimal Greedy based Deterministic Scheduling • Optimal DP based Deterministic Scheduling Stochastic Programming for Appliance Variations Online Schedule for Renewable Energy Variations 32

Linear Programming for Deterministic Scheduling with Continuous Power Level minimize: subject to: 33

Max Load Constraint To avoid tripping out, in every time window we have load constraint 34

Appliance Load Constraint Sum up in each time window appliance power consumption is equal to its input total power 35

Appliance Speed Limit and Execution Period Constraint The power is upper bounded Appliance cannot be executed before its starting time or after its deadline 36

Power Resource Power resource can be various 37

Solar Energy Distribution Constraint Solar Energy can be directly used by home appliances or stored in the battery 38

Battery Energy Storage Constraint and Charging Cost Solar Energy Storage Battery Charging Cost 39

The Proposed Scheme Outline A deterministic scheduling with continuous power level A deterministic scheduling with discrete power level • Optimal Greedy based Deterministic Scheduling • Optimal DP based Deterministic Scheduling Stochastic Programming for Appliance Variations Online Schedule for Renewable Energy Variations 40

Greedy based Deterministic Scheduling for Task i Power 0 t 1 t 2 t 3 t 4 Time Price Time Cannot handle noninterruptible home appliances 41

Greedy based Deterministic Scheduling For Multiple Home Appliances Determine Scheduling Appliances Order An appliance Schedule Current Home Appliance by Greedy Algorithm Not all the appliance(s) processed Update Upper Bound of Each Time Interval All appliances are processed Schedule 42

The Proposed Scheme Outline A deterministic scheduling with continuous power level A deterministic scheduling with discrete power level • Optimal Greedy based Deterministic Scheduling • Optimal DP based Deterministic Scheduling Stochastic Programming for Appliance Variations Online Schedule for Renewable Energy Variations 43

Dynamic Programming § Given a home appliance, one processes time interval one by one for all possibilities until the last time interval and choose the best solution 0 0 0 Choose the solution with total energy equal to E and minimal monetary cost 44

Characterizing § For a solution in time interval i, energy consumption e and cost c uniquely characterize its state Time interval i+1 (ei, ci) (ei+1, ci+1) 45

Pruning § For one time interval, (e 1, c 1) will dominate solution (e 2, c 2), if e 1>= e 2 and c 1<= c 2 Time interval i (15, 20) (15, 25) (11, 22) 46

Dynamic Programming based Appliance Optimization Power level: {1, 2, 3} Price (3, 6) (3, 3) (2, 4) (2, 2) (1, 2) 0 Dynamic Programming returns optimal solution (6, 9) (4, 5) (3, 3) (5, 7) (5, 8) (4, 6) (3, 4) (2, 2) (4, 7) (3, 5) (2, 3) (1, 1) (0, 0) t 1 (0, 0) t 2 Time 47

Handling Multiple Tasks § According an order of tasks § Perform the dynamic programming algorithm on each task 48

DP based Deterministic Scheduling For Multiple Home Appliances Determine Scheduling Appliances Order An appliance Schedule Current Home Appliance by DP Not all the appliance(s) processed Update Upper Bound of Each Time Interval All appliances are processed Schedule 49

The Proposed Scheme Outline A deterministic scheduling with continuous power level A deterministic scheduling with discrete power level • Optimal Greedy based Deterministic Scheduling • Optimal DP based Deterministic Scheduling Stochastic Programming for Appliance Variations Online Schedule for Renewable Energy Variations 50

Variation impacts the Scheme Worst case design It can be improved Cost can be reduced Best Price Window t 1 t 2 t 3 t 4 51

Best Case Design t 1 t 2 t 3 t 4 52

Variation Aware Design An adaptation variable β is introduced to utilize the load variation. t 1 t 2 t 3 t 4 53

Uncertainty Aware Algorithm Trip rate = trip out event / total event 54

The Design Flow Uncertainty Aware Algorithm 55

Algorithmic Flow Input: Task set with tasks which can be scheduled β from 0 to 0. 25 Core 1 β from 0. 25 to 0. 5 Core 2 up date task load based on β Derive current trip rate using Monte Carlo simulation No Current trip rate ≤ Target Yes β from 0. 75 to 1 Core 3 Core 4 up date task load based on β β Generate appliances schedule by solving the LP Update β β from 0. 5 to 0. 75 Generate up date task load based on β Generate appliances schedule by solving the LP Generate appliances schedule by solving the LP Update β β No solving the LP Update β Derive current trip rate using Monte Carlo simulation Current trip rate ≤ Target No Yes Current trip rate ≤ Target Yes Derive current trip rate using Monte Carlo simulation No Current trip rate ≤ Target Yes Output: Schedule 56

Algorithm Improvement § Monte Carlo Simulation takes 5000 samples § Latin Hypercube Sampling takes 200 samples Latin Hypercube Sampling is a statistical method for generating a distribution of plausible collections of parameter values from a multidimensional distribution Current S 57

The Proposed Scheme Outline A deterministic scheduling with continuous power level A deterministic scheduling with discrete power level • Optimal Greedy based Deterministic Scheduling • Optimal DP based Deterministic Scheduling Stochastic Programming for Appliance Variations Online Schedule for Renewable Energy Variations 58

Online Tuning § Actual renewable energy < Expected – Utilize energy from the power grid § Actual renewable demand > Expected – Save the renewable energy as much as possible § Actual renewable demand = Expected – Follow the offline schedule 59

Experimental Setup § The proposed scheme was implemented in C++ and tested on a Pentium Dual Core machine with 2. 3 GHz T 4500 CPU and 3 GB main memory. § 500 different task sets are used in the simulation. The number of appliances in each set ranges from 5 to 30, which is the typical number of household appliances [1]. § Two sets of the KD 200 -54 P series PV modules from Inc [2] are taken to construct a solar station for a residential unit which are cost $502. § The battery cost is set to $75 [3] with 845 k. W throughput is taken as energy storage. § The lifetime of the PV system is assumed to be 20 years [4]. § Electricity pricing data released by Ameren Illinois Power Corporation [5] [1] M. Pedrasa, T. Spooner, and I. Mac. Gill, “Coordinated scheduling of residential distributed energy resources to optimize smart home energy services, ” IEEE Transactions on Smart Grid, vol. 1, no. 2, pp. 134– 144, 2010. [2] Data Sheet of KD 200 -54 P series PV modules, available at http: //www. kyocerasolar. com/assets/001/5124. pdf. [3] T. Givler and P. Lilienthal, “Using HOMER software, NRELs micropower optimization module, to explore the role of gen-sets in small solar power systems case study: Sri lanka, ” Technical Report NREL/TP-710 -36774, 2005. [4] Lifespan and Reliability of Solar Panel, available at http: //www. solarpanelinfo. com/solarpanels/solar-panel-cost. php. [5] Real-Time Price, available at https: //www 2. ameren. com. 60

Experimental Setup on Weekday Using DP 61

Energy Consumption Distribution on Weekday Fig 1. Energy consumption distribution comparison of Test Case I. (a) Traditional scheduling (b) Dynamic Programming based scheduling. 62

Monetary Cost Distribution on Weekday Fig 2. Monetary cost comparison of Test Case I. (a) Traditional scheduling (b) Dynamic Programming based scheduling. 63

Experimental Setup on Weekend Using DP 64

Energy Consumption Distribution on Weekend Fig 3. Energy consumption distribution comparison of Test Case II. (a) Traditional scheduling (b) Dynamic Programming based scheduling. 65

Monetary Cost Distribution on Weekend Fig 4. Monetary cost comparison of Test Case II. (a) Traditional scheduling (b) Dynamic Programming based scheduling. 66

Experimental Results Using LP Cost Energy Cost (cents) household appliances time Runtime (s) household appliances 67

Traditional vs. LP vs. Discrete Greedy Cost Household appliances 68

Only DP Can Handle Non Interruptible Task set Cost Household appliances 69

Comparison of Worst Case, Best Case Design and Stochastic Design Cost Energy Cost (cents) Rate Trip Rate (%) s ond sec 10 Household appliances 70

Cost (cents) Online vs. Offline Household appliances 71

Example of a Task Set 72

Summary § This project proposes a stochastic energy consumption scheduling algorithm based on the time-varying pricing information released by utility companies ahead of time. § Continuous power level and discrete power level are handled. § Simulation results show that the proposed energy consumption scheduling scheme achieves up to 53% monetary expenses reduction when compared to a nature greedy algorithm. § The results also demonstrate that when compared to a worst case design, the proposed design that considers the stochastic energy consumption patterns achieves up to 24% monetary expenses reduction without violating the target trip rate. § The proposed scheduling algorithm can always generate a monetary expense efficient operation schedule within 10 seconds. 73

Multiple Users § Pricing at 10: 00 am is cheap, so how about scheduling everything at that time? Will not be cheap anymore 8: 00 74

Game Theory Based Scheduling 75

Thanks 76