(591g) Spatio-Temporal Modeling and Optimization of Energy Infrastructure Networks

Jalving, J., University of Wisconsin Madison
Zavala, V. M., University of Wisconsin-Madison
Tatara, E., Argonne National Laboratory
The operation of natural gas and electrical power infrastructure has become increasingly complex over the last decade [1,2]. Intermittant power generation stemming from new wind and solar installations has resulted in complex power grid management, and the risk of critical contingencies (e.g. extreme weather and cyber security threats) has led to increased uncertainty about infrastructure resiliency [3]. At the same time, abunduant natural gas supplies have promoted investment in gas-fired power plants [4] which offer the dynamic flexibility (e.g. fast ramping) needed to manage intermittent wind and solar power sources and to respond to contingencies, but the resulting frequent ramping of gas-fired generators triggers complex spatiotemporal transient phenomena in gas transmission systems which complicates their operation [5].

This talk introduces optimization formulations and strategies that address the spatiotemporal challenges facing natural gas and power grid operation. We first discuss an optimization-based state-estimation framework [6] that can be used to track internal pipeline line pack [7] (the gas stored inside the gas network to ensure reliable delivery) or perform leak-detection tasks during dynamic transients [8]. The estimation problem is challenging due to the high-dimensional nature of the states (dynamic pressure and flow throughout the system) and so we present a moving horizon strategy [9] that incorporates prior information to accurately track dynamic pressure and flow profiles using limited measurement information.

We next show how dynamic state estimates of gas infrastructure networks can inform operational planning and contingency optimization problems [10,11] using
a graph-based optimization formulation [12]. We see that this formulation faciliates expressing and decomposing the control problem in time and space which (i) enables the use of high performance
parallel solution methods [13] (ii) enables community detection methods [14] that reveal non-intuitive connections between
gas and electric infrastructures which can inform the design of their market structures for daily operation [15]. We demonstrate the proposed optimization formulations on a large-scale coupled gas-electric system.


[1] Growing concerns, possible solutions: The interdependencyof natural gas and electricity systems, MIT Energy Initiative, 2014.

[2] A. Zlotnik, L. Roald, S. Backhaus, M. Chertkov and G. Andersson, Coordinated Scheduling for Interdependent Electric Power and Natural Gas Infrastructures. In IEEE Transactions on Power Systems, 2017.

[3] Korkali, M., Veneman, J. G., Tivnan, B. F., Bagrow, J. P., & Hines, P. D. H. Reducing Cascading Failure Risk by Increasing Infrastructure Network Interdependence, Nature Publishing Group, 2017.

[4] U.S. Department of Energy, Energy Information Administration, Independent Statistics & Analysis. EIA monthly survey tracks US power plant additions, June 2018.

[5] M. Chertkov, M. Fisher, S. Backhaus, R. Bent, and S. Misra. Pressure fluctuations in natural gas networks caused by gas-electric coupling. In IEEE 48th Hawaii International Conference on System Sciences, 2015.

[6] Jalving, J., & Zavala, V. M. An Optimization-Based State Estimation Framework for Large-Scale Natural Gas Networks, Industiral & Engineering Chemistry Research, 2018.

[7] Rachford, H. H., Carter, R. G., & Dupont, T. F. Using Optimization In Transient Gas Transmission. Pipeline Simulation Interest Group, 2009.

[8] Dolan, R., & Learn, S. Fractional Factorial Analysis of Parameters Affecting Leak Detection Model Transient Resolution. Pipeline Simulation Interest Group, 2017.

[9] Rawlings, J.B. and Mayne, D.Q. Model predictive control: Theory and design Madison, Wisconsin: Nob Hill Pub, 2009.

[10] Zlotnik, Anatoly et al. Optimal control of transient flow in natural gas networks. 54th IEEE Conference on Decision and Control, 2015.

[11] Zavala, V. M. Stochastic optimal control model for natural gas networks. Computers & Chemical Engineering. 64, 103−113, 2014.

[12] Jalving, J., Cao, Y., & Zavala, V. M. Graph-based modeling and simulation of complex systems, Computers & Chemical Engineering, 125:134–154, 2019.

[13] S. Fortunato. Community detection in graphs. Physics Reports, 486: 75–174, 2010.

[14] Chiang, N.-Y., Zavala, V.M. Large-scale optimal control of interconnected natural gas and electrical transmission systems, Applied Energy, 168: 226–235, 2016.

[15] P. Weigand, G. Lander, R. Malme, Synchronizing natural gas & power markets: A series of proposed solutions, 2013.