(29e) A Multi-Scale Modeling Paradigm for Energy System Operation and Design

In the modern smart grid paradigm, hierarchical markets, including the day-ahead markets (DAM), the real-time markets (RTM), and the ancillary service markets, coordinate diverse energy systems to synchronize electricity supply and demand. Participation in these markets provides electrical energy generation companies, energy-intensive industries, grid-connect storage operators, and prosumers new revenue opportunities [1,2,3]. Driven by diverse political, social, economic, and environmental factors there is great interest in large-scale (e.g., >50%) integration of renewable energy into the electric grid. Unfortunately, non-dispatchable renewables introduce high frequency (seconds to hours) disturbances that must be counteracted, which creates both new incentives for next-generation energy systems as well as new modeling and operational challenges. For example, techno-economic analysis with process-centric models including recent work on air separation units [4,5], concentrated solar plant [6], and redox flow battery [7], models the electric grid as an “infinite bus” capable of absorbing any (reasonable) amount of power generation/consumption. This ubiquitous assumption is likely invalid under large-scale renewable integration scenarios. On the other hand, grid-centric modeling efforts focus on the operational aspect of the electric grid; for example, recent work considers optimization with more accurate AC power flow models or robustness to generator outages with N-1 security constraints. But these grid-centric models abstract the details of individual generators as either dispatchable point sources or loads with a handful of (often linear) model representations. The model representations work well for conventional generators (e.g., coal, natural gas, pumped hydro), but likely fail to capture the full benefits of hybrid systems and distributed energy storage. We argue design of new energy systems for reliable, economic, and resilient integration of renewable energy into the grid requires a new multi-scale modeling paradigm that transcends current process- and grid-centric silos.

As part of the Institute for the Design of Advanced Energy Systems (IDAES) ecosystem, we are developing new multi-scale modeling approaches that bridge process-centric and grid-centric models. In this talk, we present a five-step framework to fully simulate interactions between process-centric models and grid-centric production cost models that capture sub-hourly dynamics and operational aspects. Key steps include:

1. Generate forecasts for market prices and conditions [9];
2. Compute optimal (time-varying) bids that incorporate the energy system state and market forecasts into a two(multi)-stage stochastic program [9];
3. Clear the market by solving a sequence of unit commitment and economic dispatch optimization problems as part of grid-scale Production Cost Model [8];
4. Track market dispatch signals by solving model predictive control problems;
5. Calculate market settlements, i.e., payments to resources based on their actual performance (as calculated with detailed process-centric models).

Using this framework, we explore the interaction between process-centric and grid-centric models in three case studies. In case study 1, we show that self-schedule is sensitive to price forecast errors, whereas bidding requires forecasts covering extreme events [9]. This finding suggests that recent analysis of hybrid or emerging energy systems that assume self-schedule underestimate the technology value. In case study 2, we show a small change in the bid for a target thermal generator that only slightly changes its dispatch schedule, but induces significant impacts on the entire network, including unit commitment and market price changes [10]. This finding is important because it illustrates that interactions between resource and grid operations are complex and simplified assumptions, e.g., price taker, may be invalid. Design and analysis of emerging flexible energy systems with dynamic operation must capture interactions with the balance of the grid in order to accurately capture economic impacts and rewards. In case study 3, we quantify improved operational flexibility from integrating energy storage into existing conventional fossil generators. Through a sensitivity analysis, we find that as the size of the storage system increases, the total number of start-ups reduced by 25% and 33.6%, and the total mileage reduced by 86.5% and 62.5%, respectively, for coal and natural gas systems with augmented energy storage. However, without changing the bidding strategies, the hybrid systems can only slightly lower the total cost by 1.1% and 2.2%.

Reference:

[1] Chmielewski, D. J., “Smart grid the basics-what? why? who? how?,” Chemical Engineering Progress, vol. 110, no. 8, pp. 28–33, 2014.

[2] Dowling, A. W., & Zavala, V. M. (2018). Economic opportunities for industrial systems from frequency regulation markets. Computers & Chemical Engineering, 114, 254-264.

[3] Dowling, A. W., Kumar, R., & Zavala, V. M. (2017). A multi-scale optimization framework for electricity market participation. Applied Energy, 190, 147-164.

[4] Ierapetritou, M.G., Wu, D., Vin, J., Sweeney, P. and Chigirinskiy, M., 2002. Cost minimization in an energy-intensive plant using mathematical programming approaches. Industrial & engineering chemistry research, 41(21), pp.5262-5277.

[5] Zhang, Q., Grossmann, I.E., Heuberger, C.F., Sundaramoorthy, A. and Pinto, J.M., 2015. Air separation with cryogenic energy storage: optimal scheduling considering electric energy and reserve markets. AIChE Journal, 61(5), pp.1547-1558.

[6] Dowling, A.W., Zheng, T. and Zavala, V.M., 2017. Economic assessment of concentrated solar power technologies: A review. Renewable and Sustainable Energy Reviews, 72, pp.1019-1032.

[7] Fares, R.L., Meyers, J.P. and Webber, M.E., 2014. A dynamic model-based estimate of the value of a vanadium redox flow battery for frequency regulation in Texas. Applied Energy, 113, pp.189-198.

[8] Knueven, B., Ostrowski, J. and Watson, J.P., 2020. On mixed-integer programming formulations for the unit commitment problem. INFORMS Journal on Computing, 32(4), pp.857-876.

[9] Gao, X. and Dowling, A.W. (2020). Making Money in Energy Markets: Probabilistic Forecasting and Stochastic Programming Paradigms. 2020 American Control Conference (ACC) 168-173.

[10] Gao, X. and Dowling, A.W. (2020). “Optimal Scheduling And Control Of Hybrid Energy Systems In Multiscale Electricity Markets”. 2020 Virtual AIChE Annual Meeting, Nov. 19, 2020.