Architecture and Algorithms for the LSE to Manage Thermal Inertial Loads

Dr. Abhishek Halder (TAMU), Grid-X Program Presentation on Day 1 (Nov.3) of Workshop on Architecture and Economics of the Future Grid

Transcript

1. Architecture and Algorithms for the LSE to Manage Thermal Inertial Loads Abhishek Halder Department of Electrical and Computer Engineering Texas A&M University College Station, TX 77843 Joint work with X. Geng, F.A.C.C. Fontes, P.R. Kumar, and L. Xie
2. Context Controlling Air Conditioners Direct Control for Demand Response
3. Architecture
4. Research Scope Objective: A theory of operation for the LSE Challenges: 1. How to design the target consumption as a function of price? 2. How to control so as to preserve privacy of the loads’ states? 3. How to respect loads’ contractual obligations (e.g. comfort range width ∆)?
5. Two Layer Block Diagram
6. First Layer: Planning Optimal Consumption minimize {u1 (t),...,uN (t)}∈{0,1}N T 0 P η price forecast π (t) (u1 (t) + u2 (t) + . . . + uN (t)) dt subject to (1) ˙ θi = −αi θi (t) − θa (t) − βi Pui (t) ∀ i = 1, . . . , N, (2) T 0 (u1 (t) + u2 (t) + . . . + uN (t)) dt = τ · = ηE NP (< T, given) (3) Li0 ≤ θi (t) ≤ Ui0 ∀ i = 1, . . . , N. Optimal consumption: P∗ ref (t) = P η N ∑ i=1 u∗ i (t)
7. Second Layer: Real-time Setpoint Control optimal reference P∗ ref (t) = P η N ∑ i=1 u∗ i (t), error e(t) = P∗ ref (t)− measured Ptotal (t) , v(t) = PID velocity control kpe(t) + ki t 0 e(ς)dς + ki d dt e(t) , dsi dt = gain ∆i broadcast v(t) , Lit = Ui0 ∧ [Li0 ∨ (si (t) − ∆i )] , Uit = Li0 ∨ [Ui0 ∧ (si (t) + ∆i )] .
8. Boundary Control: Deadband → Liveband 0 2000 4000 6000 8000 10000 12000 14000 0 2 4 6 0 2000 4000 6000 8000 10000 12000 14000 20 22 24 26 28 30 t (seconds) Power (kW) t (seconds) Temperature (◦C) Lt Ut θ(t) s(t) U0 L0 Pref total (t) Ptotal (t)
9. Simulation: 500 homes + ERCOT DA price
10. How Can the LSE Price A Contract
11. Details in 1. A. Halder, X. Geng, G. Sharma, L. Xie, and P.R. Kumar, "A Control System Framework for Privacy Preserving Demand Response of Thermal Inertial Loads", SmartGridComm, 2015. 2. A. Halder, X. Geng, P.R. Kumar, and L. Xie, "Architecture and Algorithms for Privacy Preserving Thermal Inertial Load Management by A Load Serving Entity", in revision, IEEE Trans. Power Systems, 2016. 3. A. Halder, X. Geng, F.A.C.C. Fontes, P.R. Kumar, and L. Xie, "Optimal Power Consumption for Demand Response of Thermostatically Controlled Loads", under review, ACC, 2017. 4. A. Halder, X. Geng, F.A.C.C. Fontes, P.R. Kumar, and L. Xie, "Deterministic and Stochastic Optimal Control of Thermal Inertial Loads", working manuscript, available upon request.
12. Thank You
13. Backup Slides
14. First Layer: "discretize-then-optimize"
15. First Layer: "discretize-then-optimize" Numerical challenges for MILP and LP Solution: continuous time PMP w. state inequality constraints
16. Differential Privacy Preserving Sensing
17. Houston Data for August 2015 5 PM
18. Limits of Control Performance ⇡ 0.000