(136c) Real-Time Scheduling System for the Optimal Operation of Utility Plants with Renewable Energy Assets

At the COP21 in Paris 2015, it was agreed globally to limit global warming to 2°C and work towards zero net carbon emissions. This was recently reinforced when countries including the US, UK, and the EU agreed to achieve carbon neutrality by 2050. These agreements have led governments to increase reporting requirements, taxes and incentives for carbon reduction, energy efficiency and carbon capture. This has significantly mobilized many industrial companies, which are major players in CO2 emissions, to take actions in a variety of forms, including studies on retrofitting processes to become compliant, investment on renewable energy resources or even exploring the possibility of changing fundamentally the basic processes that they have relied on for many decades. This has led to a massive energy transition which is ongoing.

In 2019, the National Renewable Energy Laboratory published a report [1] which suggested that oil and gas production can leverage clean power to decrease costs and maximize production while reducing environmental impact. However, this can only happen if costs and operational barriers are properly handled. A key challenge that arises in the operations of energy systems is given by the presence of multiple interacting assets [2]. Adding renewable sources with the corresponding uncertainty along with the management of energy storages introduce a much higher level of complexity.

Even though several studies have focused on understanding the economical impact of integrating renewable assets in the Oil and Gas facilities at a design stage [3,4], how to properly operate such systems in real time, to take maximum advantage of the renewable energy sources and energy storage to reduce emissions effectively is a key unanswered question that requires a holistic view of the system [5].

With this in mind, in this paper, we introduce a real-time scheduling system for the optimal energy management of process plants while accounting for renewable energy assets. This system builds on the scheduling framework outlined in [6,7] which targets fossil-fuel-based energy systems by incorporating key features, such as built-in models for wind turbines, solar panels and PV energy storages. In order to reduce the uncertainty of the energy production of such assets, frequent schedule updates are necessary. This is accomplished by integrating the scheduling system within a digitalized environment that allows seamless data connectivity across the utility plant. The resulting autonomous system is capable of gathering data and re-parametrizing the optimization models in real time by following a moving window.

We illustrate the benefits of such comprehensive system in several case studies drawn from our experience. We focus our attention on the interplay between traditional fossil-fuel-based power and steam generators such cogeneration units, steam turbines and boilers with renewable energy sources and energy storages. In particular, we outline key challenges the system is able to address when deployed under a real-time digitalized environment.

[1] Ericson, S., Engel-Cox, J., Arent, D., (2019), Approaches for Integrating Renewable Energy Technologies in Oil and Gas Operations, Technical Report, NREL/TP-6A50-72842.

[2] Serralunga, F., Ruiz J.P., Ruiz, D., Ruiz C. (2017), Integration of Decision Tools in EMS. Computer Aided Chemical Engineering 40, 2467-2472.

[3] Azadeh, M. and Elkamel, A. (2016), Integration of Renewable Energy into Oil & Gas Industries : Solar-aided Hydrogen Production.

[4] Alnifro, M., Taqvi, S.T., Ahmad, M.S., Bensaida, K., and Elkamel, A. (2017), Optimal Renewable Energy Integration into Refinery with CO2 Emissions Consideration. An Economic Feasibility Study. IOP Conf. Ser.: Earth Environ. Sci. 83 012018.

[5] Ruiz, J.P., Ruiz, C. (2021), Optimal Energy and Emissions Management during the Energy Transition. Decarbonization Technology Magazine.

[6] Ruiz, J. P. (2017), Decision-Making Tool for the Optimal Planning and Scheduling of Utility Assets, AICHE Spring Meeting 2017, San Antonio, TX.

[7] Ruiz, J. P., Santollani, O. (2016), Optimal Planning and Scheduling of Utility Assets, Industrial Energy Technology Conference (IETC) Proceedings.