1. Heterogeneous Delay Tolerant Task Scheduling and Energy Management in the Smart Grid with Renewable Energy Shengbo Chen Electrical and Computer Engineering & Computer Science and Engineering

2. 2 The Smart Grid Next generation power grid: full visibility and pervasive control on both supplier and consumers  Smart meters Dynamic electricity prices according to demand  Shift demand from peak time Renewable energy  Reduce cost and greenhouse gas emission  Energy harvesting: highly dynamic  Battery: limited capacity With these new features and challenges, there is a need for comprehensive solutions for the smart grid

3. 3 task schedule Model of Information Delivery Real-time communication between operator and consumers  Smart meters  Controller: operator/customer side Operator Smart Meter 1 Smart home appliances demand requests Smart Meter 2 Controller demand requests task schedule Controller electricity prices electricity prices

4. 4 Energy Supply and Demand Attributes of energy supply  Unlike communication network — Storable  Renewable vs. Non-renewable  Intermittent vs. Stable supply Energy Supply Energy Demand Energy Management Attributes of energy demand  Time-varying  Unpredictable vs predictable  Elastic vs. Non-elastic Random demand meets with possibly uncertain supply

5. 5 I. Delay-tolerant Task Scheduling Intuition: Postpone delay-tolerant tasks to the period with low electricity price  E.g. dish washer, washer, electricity vehicle, air conditionerdish washer, washer, electricity vehicle, air conditioner Objective: Minimize cost of electricity tasks by leveraging the delay tolerance property and renewable energy Constraints  Hard deadlines for job completion  Average “dissatisfaction” constraint Control variables  Delay in starting a job  Amount of energy drawn/stored from/to the battery in each time slot Challenges  Uncertainty in job arrivals, incoming renewable energy and price of electricity  Appliances have diverse electricity usage patterns and scheduling flexibility

6. 6 Energy Model Demand = Supply l(t) = g(t)+b(t) Demand = Supply l(t) = g(t)+b(t)

7. 7 Related Works Task scheduling [Koutsopoulos and Tassiulas, 2010]  Convex cost function Renewable energy management scheme [Neely, 2010]  No battery & task scheduling Dynamic programming technique [Papavasiliou and Oren, 2010]  Distribution of power demand needs to be known in advance Demand peak optimization [Facchinetti and Vedova, 2011]

8. 8 Example Key factors  Factor 1: Time-varying electricity price & Delay tolerant property  Factor 2: Battery energy management Electricity Price P(t) Time 123456 1 2 3 4 5 6 7 8 Task SchedulesCost Non-scheduling $11 Scheduling w F1 $10 Scheduling w F1,F2 $7

9. 9 Problem Statement Models  Electricity price assumed to be known in the near future  Dissatisfaction function U Average dissatisfaction constraint Don’t delay too many jobs by too much Cost of electricity Cost reduction by drawing from battery Starting delay for job i arriving in timeslot t Energy drawn/stored from/to the battery Job must finish before deadline Hard deadline Job duration Energy constraint

10. 10 Solution Methodology Virtual Queue Q(t)  Deal with the average dissatisfaction constraint Lemma: If the virtual queue is stable, the average dissatisfaction constraint is satisfied Lyapunov optimization technique  Define Lyapunov function  Minimize the Lyapunov drift Q(t)

11. 11 In each time slot, the delay in starting a job is computed as In each time slot, the battery charge/discharge is given by Algorithm Sketch Cost of electricity Measure of dissatisfaction for this job Measure of accumulated dissatisfaction

12. 12 Battery level is always bounded:  Only require finite battery capacity Average delay dissatisfaction is always less than Performance is within a constant gap of the optimum Main Results Constant gap Diminish as V becomes large A tradeoff between the battery size and the performance

13. 13 Simulation Results Compared to the non-scheduling case Cost reduction over slots (V=100) Cost reduction versus V S. Chen, N. Shroff and P. Sinha, “Heterogeneous Delay Tolerant Task Scheduling and Energy Management in the Smart Grid with Renewable Energy,” to appear in IEEE Journal on Selected Areas in Communications (JSAC). S. Chen, N. Shroff and P. Sinha, “Scheduling Heterogeneous Delay Tolerant Tasks in Smart Grid with Renewable Energy,” in Proceeding of IEEE CDC, pp. 1130-1135, Dec, 2012.

14. 14 Summary Cost reduction  Leverage dynamic electricity prices and delay-tolerant property  Renewable energy and battery Delay constraints  Hard deadlines  Average dissatisfaction constraint Scheme performance is within a constant gap of the optimum The constraint means that we can only draw energy from the grid What if this constraint does not exist? Sell energy back to the grid!

15. 15 II. Energy Trading Intuition: Dynamic electricity price combining an energy storage battery implies a trading opportunity (similar to stock) Objective: Maximize the profit by opportunistically selling energy to the grid Control variables  Amount of energy drawn/stored from/to the battery in each time slot Challenges  Uncertainty of incoming renewable energy, price of electricity and energy demand Energy selling price is always less than the energy buying price

16. 16 Example Key factors:  Time-varying electricity price & Battery energy management

17. 17 Problem Statement Models  Energy selling price is smaller by a factor of  Energy demand l(t) is exogenous process Profit of selling energy Cost of buying energy from the grid Energy drawn/stored from/to the battery Battery level Maximal output of the battery

18. 18 Denote In each time slot, the energy allocation is given as follows  Case 1: If  Case 2: If  Case 3: If Algorithm Sketch Sell: Price is high or battery level is high Buy: Price is low and battery level is low Equal: Price and battery level are mild

19. 19 Battery level is always bounded:  Only require finite battery capacity Asymptotically close to the optimum as T tends to infinity Main Results Diminish as V becomes large A tradeoff between the battery size and the performance

20. 20 Simulation Results Compared to the greedy scheme: first use the renewable energy for the demand, and sell the extra if any Annual profit versus Beta (V=1000) Annual profit versus V (Beta=0.8) S. Chen, N. Shroff and P. Sinha, “Energy Trading in the Smart Grid: From End-user’s Perspective,” to appear in Asilomar Conference on Signals, Systems and Computers, 2013. (Invited paper)

21. 21 Open Problems Different Model  Preemptive & non-preemptive  HVAC system optimization Game theory based schemes  The behavior of large number of customers can influence the market price Network Economics

22. 22 Low-Latency Algorithm in Cloud Storage Objective: Developed a queueing delay optimal algorithm for downloading data in cloud storages by leveraging multiple parallel threads and FEC codes System model  (n,k) codes Request arrivals Queue Queueing Delay … Threads Dispatcher Read Time

23. 23 When k = 1, given that the downloading time of each individual thread is i.i.d. following exponential distribution and the arrival process is Poisson, any work-conserving scheme is throughput optimal and also delay optimal. When k > 1, given that the downloading time of each individual thread is i.i.d. following exponential distribution and the arrival process is Poisson process, the greedy scheme is delay optimal. Main Results S. Chen, L. Huang and X. Liu, “Optimal-Latency Data Retrieving Scheme in Storage Clouds by Leveraging FEC Codes,” under submission, 2013. G. Liang, S. Chen and U. Kozat, “On Using Parallelism and FEC in Delivering Reliable Delay Performance over Storage Clouds: A Queueing Theory Perspective,” under submission, 2013.

24. 24 Energy allocation and routing schemes in rechargeable sensor networks Objective: Maximize the total utility/throughput performance for a rechargeable sensor network Main results  Finite time-horizon — Optimal Offline: Shortest path  Infinite time-horizon — Simple asymptotically optimal S. Chen, P. Sinha, N. Shroff, and C. Joo, “A Simple Asymptotically Optimal Energy Allocation and Routing Scheme in Rechargeable Sensor Networks,” Proc. of IEEE INFOCOM, Orlando, Florida, pp 379-387, Mar 2012. S. Chen, P. Sinha, N. Shroff, and C. Joo, “Finite-Horizon Energy Allocation and Routing Scheme in Rechargeable Sensor Networks,” Proc. of IEEE INFOCOM, Shanghai, pp 2273-2281, April 2011. S. Chen, P. Sinha, N. Shroff, and C. Joo, “A Simple Asymptotically Optimal Joint Energy Allocation and Routing Scheme in Rechargeable Sensor Networks,” Under Minor Revision, Transactions on Networking.

25. 25 Lifetime Tunable Design in WiFi Objective: Improve the system performance for energy- constrained WiFi devices Resulting scheme  Near-optimal proportional-fair utility performance for single access point scenarios  Alleviating the near-far effect and hidden terminal problem in general multiple AP scenarios Performance improvement  Lifetime: high energy efficiency by avoiding idle listening  Fairness: providing high priority to the low throughput devices  Throughput: smaller collision probability S. Chen, T. Bansal, Y. Sun, P. Sinha and N. Shroff, “ Life-Add: Lifetime Adjustable Design for WiFi Networks with Heterogeneous Energy Supplies,” To appear in proceedings of Wiopt 2013.

26. 26

27. 27 Cost of electricity

28. 28 System Model Demand = Supply l(t) = g(t)+b(t) Demand = Supply l(t) = g(t)+b(t)

29. 29 System Model g(t) = l(t)-b(t)