(463b) Challenges and Opportunities in Industrial Demand-Side Management

Industrial demand side management (iDSM) has been intensively studied in the last two decades [1]. The typical assumption is to, based on a given energy price forecast, adapt the production schedule such that the energy cost is minimized while meeting the aimed production targets [2]. Many contributions consider also various contract types, but these typically follow the day-ahead energy planning structures (time of use, baseline, day-ahead auction) and does not pay too much attention to the inaccuracy of typical forecasts. Instead, the main strategy is to create a best-possible target profile and manage the possible disturbances at a later point of time, for instance through the intraday market. Apart from the better energy consumption overview this also helps creating a good environment for more stable grid planning and defining a dispatching profile that lowers the overall electricity costs.

Unfortunately, demand-side management has not shown much success in many industries [3] due to the complexity of processes or lack of natural buffers for storing energy or energy in form of intermediates. Nonetheless, an improved process energy management [4] is a necessary and important component in future renewable-driven energy systems. Also, a stronger focus on day-ahead scheduling supports a better load forecasting but does not sufficiently comprise the uncertainties involved in, for instance, solar and wind power generation levels, which cannot be exactly evaluated but need to be handled through reserves, that is, ancillary services [5][6]. Recent work also acknowledges this and focuses on better modeling tools, for example Generalized Disjunctive Programming [7], to allow the simultaneous consideration of multiple aspects more efficiently. Various bidding policies are also discussed [8] to acknowledge the heterogenous landscape in energy trading, resulting in complex and large-scale problems that must be decomposed for more efficient solutions. A very recent work [9] evaluates the capability of using a process as a battery through better capturing the process dynamics.

This contribution discusses generic challenges but mainly focuses on how an industrial site could collaborate with the power markets, which vary significantly depending on the region. They may contain different trading opportunities, for instance, both in day-ahead and intra-day markets as well as across various products ranging from energy to ancillary services including both fast and slower reserves. The time granularity of various products can typically range between 5 and 60 minutes. Some products are renumerated through a market clearing price, others follow pay-as-bid principle and there are also penalties involved in not following the committed transactions. The inherent stochasticity of these problems poses further challenges and will also be discussed. It is important that we understand the practical limitations and opportunities to be able to estimate the true iDSM potential, compared to using more conventional battery energy storage systems for supporting future energy systems.

References

[1] Paulus, M., & Borggrefe, F. (2011). The potential of demand-side management in energy-intensive industries for electricity markets in Germany. Applied Energy, 88(2), 432-441. doi:10.1016/j.apenergy.2010.03.017
[2] Zhang, Q., Sundaramoorthy, A., Grossmann, I. E., & Pinto, J. M. (2016). A discrete-time scheduling model for continuous power-intensive process networks with various power contracts. Computers and Chemical Engineering, 84, 382-393. doi:10.1016/j.compchemeng.2015.09.019
[3] Merkert, L., Harjunkoski, I., Isaksson, A., Säynevirta, S., Saarela, A., & Sand, G. (2015). Scheduling and energy - industrial challenges and opportunities. Computers and Chemical Engineering, 72, 183-198. doi:10.1016/j.compchemeng.2014.05.024
[4] Palensky, P., & Dietrich, D. (2011). Demand side management: Demand response, intelligent energy systems, and smart loads. IEEE Transactions on Industrial Informatics, 7(3), 381-388. doi:10.1109/TII.2011.2158841
[5] Zhang, X., Hug, G., Kolter, J. Z., & Harjunkoski, I. (2018). Demand response of ancillary service from industrial loads coordinated with energy storage. IEEE Transactions on Power Systems, 33(1), 951-961. doi:10.1109/TPWRS.2017.2704524
[6] Todd D, Caufield M, Helms B, Starke M, Kirby B, Kueck J. Project report to US Department of Energy contract DE-AC05-00OR22725 Providing reliability services through demand response: a preliminary evaluation of the demand response capabilities of Alcoa Inc Project report to US Department of Energy contractDE-AC05-00OR22725; 2009.
[7] Castro, P. M., Grossmann, I. E., & Zhang, Q. (2018). Expanding scope and computational challenges in process scheduling. Computers and Chemical Engineering, 114, 14-42. doi:10.1016/j.compchemeng.2018.01.020
[8] Varelmann, T., Erwes, N., Schäfer, P., & Mitsos, A. (2022). Simultaneously optimizing bidding strategy in pay-as-bid-markets and production scheduling. Computers and Chemical Engineering, 157 doi:10.1016/j.compchemeng.2021.107610
[9] Semrau, R., & Engell, S. (2023). Process as a battery: Electricity price aware optimal operation of zeolite crystallization in a continuous oscillatory baffled reactor. Computers and Chemical Engineering, 171 doi:10.1016/j.compchemeng.2023.108143