Smart Home Energy CPS Scheduling Professor Shiyan Hu Department of Electrical and Computer Engineering Michigan Technological University 1 1

Smart Home: Academic Perspective 5% energy efficiency improvement in residential home energy systems leads to carbon emission reduction equivalent to removing 53 million cars in U.S. 2

Smart Home http://www.yousharez.com/2010/11/20/house-of-dreams-a-smart-house-concept/ To Minimize Expense, Balance Energy Usage and Maximize Renewable Energy Usage 3

Why we schedule? The Single User Smart Home 4 Power flow Internet Control flow 4

Varying Energy Consumption Typical summer energy load profile in State of Ontario, Canada. One can see the peak load around 7:00pm which usually involves a lot of human activities. Peak Average PAR Source: Ontario Energy Board 5

Dynamic Electricity Pricing Set high prices at peak energy hours to discourage energy usage there for energy load balancing Hourly Price from Ameren Illinois 6

Renewable Energy 7

Energy Scheduling for a Single Smart Home Given the electricity pricing, to decide when to launch a home appliance at what power level for how long utilize renewable energy subject to scheduling constraints Targets Reduce user bill Reduce PAR (peak to average ratio) of grid energy usage Maximize renewable energy usage The smart home scheduler computes scheduling solutions for future, so it needs the future pricing. How? 8

Two Pricing Models: Guideline and Realtime Pricing Guideline price: utility publishes it one day ahead to guide customers to schedule their appliances, through providing the predicted pricing in the next 24 hours. Real time price: utility uses it to bill customers, e.g., it obtains the total energy consumption in the past hour, computes the total bill as a quadratic function of the total energy, and then distributes the bill to each customer proportionally. 9

Electric Vehicles (EV) Powered by one or more Electric Motors 10

Multiple Mode Charging of EV 2014 Honda Accord PHEV 120-volt: less than 3 hours 240-volt: one hour 2013 Toyota Prius PHEV 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 11

12 Landry machine Dish washer EV AC Start End …… 13:00 18:00 09:00 08:00 17:00 N/A 12

13 Multiple Power Level (VFD) Impact Power Powerr 5 cents/kwh 2 1 2 3 Time Time (b) (a) cost = 10 kwh * 5 cents/kwh = 50 cents cost = 5 kwh * 5 cents/kwh + 5 kwh * 3 cents/kwh = 40 cents 13

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? 14

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 15

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 16

The 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 17

Linear Programming for Deterministic Scheduling with Continuous Power Level minimize: subject to: 18

Max Load Constraint To avoid tripping out, in every time window we have load constraint 19

Appliance Load Constraint Sum up in each time window appliance power consumption is equal to its input total power 20

Appliance Speed Limit and Execution Period Constraint The power is upper bounded Appliance cannot be executed before its starting time or after its deadline 21

Power Resource Power resource can be various 22

Solar Energy Distribution Constraint Solar Energy can be directly used by home appliances or stored in the battery 23

Battery Energy Storage Constraint and Charging Cost Solar Energy Storage Battery Charging Cost 24

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 25

Greedy based Deterministic Scheduling for Task i Power t1 t2 t3 t4 Time Price Time Cannot handle noninterruptible home appliances 26

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 27

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                       Choose the solution with total energy equal to E and minimal monetary cost 28

Characterizing For a solution in time interval i, energy consumption e and cost c uniquely characterize its state Time interval i Time interval i+1 (ei, ci) (ei+1, ci+1)   29

Pruning For one time interval, (e1, c1) will dominate solution (e2, c2), if e1>= e2 and c1<= c2 Time interval i (15, 20) (15, 25) (11, 22) 30

Dynamic Programming based Appliance Optimization Power level: {1, 2, 3} Dynamic Programming returns optimal solution     (6, 9) (5, 8) (4, 7) (5, 7) (4, 6) (3, 5) (4, 5) (3, 4) (2, 3) (3, 3) (2, 2) (1, 1) (3,6) (3,3) Price (2,4) (2,2) (1,2) (1,1) Time (0,0) t1 (0,0) t2 31

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 32

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 33

Variation impacts the Scheme Worst case design It can be improved Cost can be reduced Best Price Window t1 t2 t3 t4 34

Best Case Design t1 t2 t3 t4 35

Variation Aware Design An adaptation variable β is introduced to utilize the load variation. t1 t2 t3 t4 36

Uncertainty Aware Algorithm Trip rate = trip out event / total event 37

The Design Flow Uncertainty Aware Algorithm 38

Algorithmic Flow Input: Task set with tasks which can be scheduled Core 1 up date task load based on β Generate appliances schedule by solving the LP Derive current trip rate using Monte Carlo simulation Current trip rate ≤ Target Update β No Yes Core 2 Core 3 Core 4 β from 0 to 0.25 β from 0.25 to 0.5 β from 0.5 to 0.75 β from 0.75 to 1 Yes up date task load based on β Generate appliances schedule by solving the LP Derive current trip rate using Monte Carlo simulation Current trip rate ≤ Target Update β No Output: Schedule 39

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 40

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 41

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 42

Experimental Setup The proposed scheme was implemented in C++ and tested on a Pentium Dual Core machine with 2.3 GHz T4500 CPU and 3GB 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 KD200-54 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 kW 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.MacGill, “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 KD200-54 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://www2.ameren.com. 43

Experimental Setup on Weekday Using DP 44

Energy Consumption Distribution on Weekday Fig1. Energy consumption distribution comparison of Test Case I. (a) Traditional scheduling (b) Dynamic Programming based scheduling. 45

Monetary Cost Distribution on Weekday Fig2. Monetary cost comparison of Test Case I. (a) Traditional scheduling (b) Dynamic Programming based scheduling. 46

Experimental Setup on Weekend Using DP 47

Energy Consumption Distribution on Weekend Fig3. Energy consumption distribution comparison of Test Case II. (a) Traditional scheduling (b) Dynamic Programming based scheduling. 48

Monetary Cost Distribution on Weekend Fig4. Monetary cost comparison of Test Case II. (a) Traditional scheduling (b) Dynamic Programming based scheduling. 49

Experimental Results Using LP Energy Cost (cents) Runtime (s) Cost time household appliances household appliances 50

Traditional vs. LP vs. Discrete Greedy Cost Household appliances 51

Only DP Can Handle Non Interruptible Task set Cost Household appliances 52

Comparison of Worst Case, Best Case Design and Stochastic Design Energy Cost (cents) Trip Rate (%) Cost Rate 10 seconds Household appliances Household appliances 53

Online vs. Offline Cost (cents) Household appliances 54

Example of a Task Set 55

The Implementation Using FPGA 56

Schematic of FPGA Implementation 57

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. 58

Multiple Users in a Community 59

Multiple Users Pricing at 10:00am is cheap, so how about scheduling everything at that time? Energy Accumlation 10:00am 60

Game Theory Based Scheduling 61

Game Theory Based Scheduling For every player in a game, there is a set of strategies and a payoff function which is the profit of the player. Each player chooses from the set of strategies in order to maximize its payoff. When no player can increase its payoff without decreasing other users’ payoff, Nash Equilibrium is reached. 62

Game Formulation in Community Level Players: All users in the community   Strategy: Choose power levels and launch time to maximize payoff while satisfying constraints 63

Community Size Small community: Less than 100 users Medium community: 100 ~5,000 users Large community: More than 5,000 users 64

Small Community: Fully Distributed Architecture In the fully distributed architecture, each customer uses own smart home scheduler to communicate with other users for information exchange and computes smart home scheduling solution. 65

Algorithmic Illustration For Small Community Communication/Synchronization …… Equilibrium/Schedule … Embedded Processor User2 Usern User1 Iteration 1 Iteration 2 66

Algorithmic Flow For Small Community Each user schedules their own appliances separately to maximize payoff using dynamic programming Appliances Determine scheduling appliances order All users share information with each other Schedule current appliance by dynamic programming Each user reschedules their own appliances separately by dynamic programming No No All appliances scheduled Equilibrium Yes Yes Schedule Schedule 67

Single User Smart Home Scheduling Baseline Energy Usage Scheduling Range Energy Usage Scheduling Range Energy Usage Scheduling Range Energy Usage 68

An Example for Two Customers Total Energy Load Change this Customer 1 Customer 2 69

Problem With The Fully Distributed Architecture Communication/synchronization problem Assume that there are 100 iterations needed for the game theory based algorithm. Communication/synchronization needs to be performed at the end of every iteration. It is not realistic for big community to deploy the fully distributed architecture due to the complexity of synchronization among a large number of users. Each user performs the game theory based algorithm at their own side and communicates with all other users after every single iteration. 70

Medium Community: Fully Centralized Architecture Users only communicate with computer cluster twice, at the beginning and end. Communication/synchronization is not needed any more among users. Communication/synchronization within computers or CPU cores is much easier and faster. Each user sends the scheduling tasks to a computer cluster which compute the scheduling solutions of all users. 71

Algorithmic Illustration For Medium Community Parallel Computing … Each core schedules assigned tasks of users in parallel All cores share information with each other to synchronize Each core reschedules the assigned tasks given the information of other users Schedule Equilibrium Run iteratively until convergence Interface User1 User2 Usern 72

Algorithmic Flow For Medium Community Solve the continuous fashion problem combinatorially Discretize the continuous solution Flag all computers to be available Assign task fractionally to the available computer with lowest ratio of 𝒄/𝒇 Sort all computers increasingly by ratio of 𝒄/𝒇 Runtime of computer is reaching TC Flag the computer to be unavailable Yes No Each computer runs tasks of users in parallel All computers share information with each other to synchronize Each computer reruns the tasks of users given the information of other users Schedule Equilibrium Run iteratively Users send tasks to computers Schedule tasks of users to computers Game theory based algorithm Computers send back the results to users # iterations = kϒ …… User1 User2 User3 Usern

Problem With The Fully Centralized Architecture Cannot handle large community Communication delay Limited computation power and high maintenance cost Security concerns 74

Large Community: Hierarchical Architecture There are 10 million users in a big community. It can be partitioned into 2k smaller groups, in which the number of users is 5k. The communication overhead within each group is acceptable. There is no flooding packets problem. 75

Algorithmic Flow For Intra-Community Optimization Parallel Computing … Each core schedules assigned tasks of users in parallel All cores share information with each other to synchronize Each core reschedules the assigned tasks given the information of other users Schedule Equilibrium Run iteratively until convergence Interface User1 User2 Userx1 Continue to Inter-community optimization 76

Algorithmic Flow For Inter-Community Optimization Energy consumption summation of Intra-community optimization Pick k time intervals with the largest total energy consumption Reduce the k energy consumption by δ Pick k time intervals with the smallest total energy consumption Increase the k energy consumption by δ Continue to Intra-community optimization/Schedule 77

Algorithmic Illustration For Large Community Parallel Computing … Each core schedules assigned tasks of users in parallel All cores share information with each other to synchronize Each core reschedules the assigned tasks given the information of other users Schedule Equilibrium Run iteratively until convergence Interface User1 User2 Userx1 Continue to Inter-community optimization Energy consumption summation of Intra-community optimization Pick k time intervals with the largest total energy consumption Reduce the k energy consumption by δ Continue to Intra-community optimization/Schedule Pick k time intervals with the smallest total energy consumption Increase the k energy consumption by δ 78

Summary Single user smart home scheduling and variation aware optimization in smart home scheduling Multiple user smart home scheduling 79