(418e) Toward Future Energy Generation Systems: Multi-Scale Optimization with Market Interactions

There has been growing interest within power systems communities towards the development of Integrated Energy Systems (IES) [1]. Such generation systems have the flexibility to integrate diverse energy sources (e.g., electricity, heat, steam, or chemicals) in hybrid configurations to facilitate new renewable integration, curtailment of emissions, and reduction in grid operating costs [2]. A key advantage afforded by IES flexibility is the means to participate in electricity markets by offering energy and regulation services. Because an IES can dynamically apportion its energy output (e.g. using thermal and/or electrical storage), it can provide responsive generation to grid operators and help to improve grid reliability [3]. Consequently, the design and optimization of an IES must consider both technical performance of the energy system and its economic viability within dynamic energy markets.

Fundamental challenges toward the development of an IES include accounting for multiple energy market time-scales (e.g. real-time, day-ahead, and long-term capacity markets), and exogenous uncertainty that arises due to decisions as a market participant that affect the entire system [4]. The effects of exogenous uncertainty can be particularly important to capture. For instance, an IES design decision like the size of storage will impact how it bids into the market (e.g., ramping rate), which impacts both (1) the market revenues of the entire system and (2) how the IES is asked to respond from the system operator. Market prices are also often set by the marginal resource, i.e., the resource with the highest cost out of those that are dispatched. As a result, slight perturbations in the parameters of an IES that are marginal (such as generation) can induce large fluctuations in the market, both for the IES and its neighbors. Accepted modeling approaches typically use "price-taker" assumptions [5] that do not capture the exogenous uncertainty inherent to designing new generation systems, which necessitates the development of new optimization formulations.

This talk discusses novel optimization-based approaches that capture market interactions (i.e. exogenous uncertainty) to enable tractable solution approaches for IES design problems. We first discuss key IES design parameters from an energy market standpoint based on current power dispatch formulations [6], and we then present a detailed analysis of market simulations [7] over a candidate power network [8] that predict long-term revenues and operational profiles. We then discuss a surrogate modeling framework [9] that captures market revenue and dispatch as tractable algebraic functions within our optimization framework. We show that our developed surrogates well-approximate the high-fidelity simulation data [10,11], and that they confirm our intuition with respect to key design parameters. We lastly introduce a complete IES conceptual design problem that combines our market-based surrogate models with detailed plant physics in a stochastic programming framework. Our results demonstrate the advantages gained by capturing exogenous market uncertainty versus using standard modeling assumptions when designing new generation systems. We finally close with discussion towards developing fully dynamic conceptual design problems that include market participation.

[1] M. O. Malley et al., “Energy Systems Integration : Defining and Describing the Value Proposition”, 2016

[2] D. Arent et al., "Multi-input, Multi-output Hybrid Energy Systems," Joule, 2021

[3] A. W. Dowling and V. M. Zavala, “Economic opportunities for industrial systems from frequency regulation markets,” Computers and Chemical Engineering, 2018

[4] F. Sorourifar, V. M. Zavala, and A. W. Dowling, “Integrated Multiscale Design , Market Participation , and Replacement Strategies for Battery Energy Storage Systems,” IEEE Transactions on Sustainable Energy, 2020

[5] A. W. Dowling, R. Kumar, and V. M. Zavala, “A multi-scale optimization framework for electricity market participation,” Applied Energy, 2016

[6] B. Knueven, J. Ostrowski, J. P. Watson, "On Mixed-Integer Programming Formulations for the Unit Commitment Problem", INFORMS Journal on Computing, 2020

[7] J. P. Watson, B. Knueven, R. Concepcion, D. Melander, A. Short, P. Zhang, D. Woodruff, Prescient, Computer software, Available: https://github.com/grid-parity-exchange/Prescient

[8] C. Barrows et al., “The IEEE Reliability Test System: A Proposed 2019 Update,” IEEE Transactions on Power Systems, 2020

[9] A. Cozad, N. V Sahinidis, and D. C. Miller, “Learning Surrogate Models for Simulation-Based Optimization,” AIChE Journal, 2014

[10] E. Plischke, E. Borgonovo, and C. L. Smith, “Global sensitivity measures from given data,” European Journal of Operational Research, 2013

[11] A. Saltelli, P. Annoni, I. Azzini, F. Campolongo, M. Ratto, and S. Tarantola, “Variance based sensitivity analysis of model output . Design and estimator for the total sensitivity index,” Computer Physics Communications, 2010