Using Demand Response to Improve Power System Voltage

Using Demand Response to Improve Power System Voltage Stability Margins Mengqi Yao 1 Johanna L. Mathieu 1 Daniel K. Molzahn 2 1 University of Michigan 2 Argonne National Laboratory

Outline • Background and Motivation • Problem Description • Optimization Model • Iterative Sensitivity Smallest Singular Value Approach • Results • Conclusion and Future Work 6/21/2017 Power. Tech 2

Background • Power System Stability[1] • Voltage Stability • Frequency Stability • Rotor Angle Stability • Demand Response (Air conditioners, Electric vehicles) • Time shifting • Spatial shifting Frequency Stability[2, 3] Voltage Stability How to use Demand Response to improve the voltage stability ? [1] P. Kundur, J. Paserba, V. Ajjarapu, G. Andersson, A. Bose, C. Canizares, N. Hatziargyriou, D. Hill, A. Stankovic, C. Taylor, T. Van, and V. Vittal, “Definition and classification of power system stability, ”, 2004 [2] J. Short, D. Infield, and L. Freris, “Stabilization of grid frequency through dynamic demand control, ”, 2007 [3] D. Callaway, “Tapping the energy storage potential in electric loads to deliver load following and regulation, with application to wind energy, ”, 2009 6/21/2017 Power. Tech 3

Static Voltage Stability Margin • Loading Margin • Smallest Singular Value [2] Source: [1] S. Greene, I. Dobson, and F. L. Alvarado, “Sensitivity of the loading margin to voltage collapse with respect to arbitrary parameters, ”, 1997 [2] R. J. Thomas and A. Tiranuchit, “Voltage instabilities in electric power networks, ”, 1986 6/21/2017 Power. Tech 4

Motivation and Objective • Verify: flexible loads can also be used to improve power system static voltage stability margins. • Shifting loads in space, in order not to affect frequency stability. • Formulate an optimization model: Smallest Singular Value Power flow equations Engineering constraints 6/21/2017 Power. Tech 5

Outline • Background and Motivation • Problem Description • Optimization Model • Iterative Sensitivity Smallest Singular Value Approach • Results • Conclusion and Future Work 6/21/2017 Power. Tech 6

Problem Description Path 1: A disturbance happens X 2 X 1 3 X 4 Feasible space 6/21/2017 Path 2: Reallocate flexible load X Path 3: Generation re-dispatch; Energy pay-back Path 4: Return to initial operating point Power. Tech 7

Related Work • S. Greene, I. Dobson, and F. L. Alvarado “Sensitivity of the loading margin to voltage collapse with respect to arbitrary parameters, ”, 1997 • P. -A. Lof, T. Smed, G. Andersson, and D. Hill “Fast calculation of a voltage stability index, ”, 1992 • R. J. Avalos, C. A. Cañizares, F. Milano, and A. J. Conejo “Equivalency of continuation and optimization methods to determine saddle-node and limitinduced bifurcations in power systems”, 2009 6/21/2017 Power. Tech 8

Outline • Background and Motivation • Problem Description • Optimization Model • Iterative Sensitivity Smallest Singular Value Approach • Results • Conclusion and Future Work 6/21/2017 Power. Tech 9

SSV Maximization Problem s. t. Eigenvalue Sensitivity[1] Iterative Sensitivity Smallest Singular Value Approach [1] L. Rouco and F. Pagola, “An eigenvalue sensitivity approach to location and controller design of controllable series capacitors for damping power system oscillations, ’’, 1997 6/21/2017 Power. Tech 10

Iterative Sensitivity Smallest Singular Value Approach Begin Solve base power flow Solve the linear optimization problem Update variables + Run AC power flow N Y Output End 6/21/2017 Power. Tech 11

Benchmark • Brute Force Smallest Singular Value Approach • Brute Force Loading Margin Approach • Optimal Loading Margin Approach[1] R. J. Avalos, C. A. Cañizares, F. Milano, and A. J. Conejo “Equivalency of continuation and optimization methods to determine saddle-node and limit-induced bifurcations in power systems”, 2009 6/21/2017 Power. Tech 12

Outline • Background and Motivation • Problem Description • Optimization Model • Iterative Sensitivity Smallest Singular Value Approach • Results • Conclusion and Future Work 6/21/2017 Power. Tech 13

Test Case: IEEE 9 -bus system 2 4 8 1 9 7 5 6 3 Smallest singular value 0. 8959 6/21/2017 Smallest singular value 0. 8995 Power. Tech 14

6/21/2017 Power. Tech 15

Test Case: IEEE 30 -bus system 6/21/2017 Power. Tech 16

Test Case: IEEE 118 -bus Computation time: 10 s 6/21/2017 Power. Tech 17

Outline • Background and Motivation • Problem Description • Optimization Model • Iterative Sensitivity Smallest Singular Value Approach • Results • Conclusion and Future Work 6/21/2017 Power. Tech 18

Conclusion • Demand response can improve static voltage stability margins • We may obtain significantly different loading patterns when we maximize different kinds of stability margins • The iterative sensitivity smallest singular value approach is able to seek the loading pattern with maximum smallest singular value Future Work • • Comparing the solution from semidefinite programming Developing strategy to pay back the energy More realistic load model Other kinds of stability margins Contact info: Mengqi Yao mqyao@umich. edu Support from NSF Grant EECS-1549670 and the U. S. DOE, Office of Electricity Delivery and Energy Reliability under contract DE-AC 02 -06 CH 11357