Aug-2021

Optimal energy and emissions management during energy transition

When companies manage energy provision and consumption in real-time, it is possible to significantly reduce total energy use and optimise operations.

Juan Ruiz and Carlos Ruiz
KBC Advanced Technologies, Inc.

Article Summary

The energy landscape is evolving. Energy costs are fluctuating widely, and global concern to reduce greenhouse gas (GHG) emissions grows stronger. Traditional energy sources, such as coal, have declined in importance, while supplies from natural gas and renewables are consistently growing. Additionally, the COVID-19 pandemic impacted consumption patterns, resulting in drastic price changes for oil and petroleum-derived products.

While expectations are that overall consumption will recover in 2021, traditional fuels may never return to their previous price levels. At the same time, competing renewable options expand while their costs decrease. Adding to the complexity of this environment, hourly or sub-hourly changes of power market prices are becoming very common worldwide. Manufacturers are aware of energy’s role in overall costs and emissions. As a result, they put in significant effort to better manage their energy systems.

For large-scale process plants, energy normally accounts for 50% of operating expenses. Consequently, an energy use reduction of 10% can often improve margins by 5%. As companies seek to boost profits and reduce emissions, energy optimisation is naturally one of the first places to look.

When companies manage energy provision and consumption in real-time, it is possible to significantly reduce total energy use with just a few actions. Area by area, a site can quickly make operational changes to improve efficiency and reduce consumption and emissions. On the other hand, large-scale improvement projects, such as installing a new cogeneration system, need careful examination for cost/benefit potential.

Process plants, manufacturing sites, and communities need to consider what is the best way to produce, store, distribute and mix the available energy options in the context of the energy transition. Whether they use traditional, renewable or both sources, there is a need to find an effective way to adapt to this new context by integrating these sources or fundamentally changing the existing energy system infrastructure. The final objective is to simultaneously reduce cost and GHGs emissions in the current context, during the transition and continue doing it when a distributed, renewables-based energy system operates long after, as presented in Figure 1.

Energy systems integration during energy transition
The level of integration that would be achieved during the energy transition will introduce an increasing complexity in the management of these mixed energy systems. Decisions at the operational level, such as when to use the fossil fuel-based co-generators, will depend on expected power and fuel prices and the predictions (i.e., forecasts) of renewable energy availability. This, in turn, depends on weather conditions, such as ambient temperature, wind speed, or solar intensity. Due to uncertainty on the factors that affect renewable energy generation, it is necessary to include power storage facilities and explicitly consider availability and constraints.

Hydrogen production and storage is an example of a power storage facility. Hydrogen is a great fit for this purpose since it has been part of traditional energy systems for many decades. Historically, the least expensive way for a plant to generate hydrogen was by the steam methane reforming (SMR) process. However, SMR produces carbon dioxide as a by-product, which plants normally vent into the atmosphere. A way to eliminate these carbon dioxide emissions is by following a greener alternative. This approach is the use of power to electrolyse water into oxygen and hydrogen (green hydrogen). Deciding if this is economically advantageous depends on the power source and storage options. Considerations to manage green hydrogen production include its use in existing networks, via injection to natural gas, storage in caverns or compressed in cylinders.

To make this complex set of decisions feasible, we believe that any energy management system (EMS) support tool should include and provide the functionalities described below and presented in Figure 2:
- Provide integrated, holistic models that consider equipment or subsystems usually encountered in conventional energy systems and what relates to renewable energy sources, such as photovoltaic (PV), wind, biomass, hydrogen, and so on
- Support forecasting, which estimates future operational site conditions and environment, like weather, power/fuels market conditions, process energy demand, and so on.
- Allow the analysis and monitoring of current and past energy efficiency and performance of the site and renewable sources.
- Support the optimal energy scheduling, taking into consideration availability, forecasted consumption, variability in electricity prices and inventories, as well as multi-period related decisions.
- Automate the integration of the optimal schedule and real-time recommendations to relieve schedulers and operators from complex and time-consuming decision-making activities.
- Target autonomous operation in the short and medium term, allowing a smooth information flow between the different decision levels, going from planning to the regulatory control layer.

Multi-period optimisation
Real-time optimisation and scheduling, working together and properly aligned, is paramount to properly manage energy systems at the minimum cost, while continuously reducing GHG emissions. The inherent variability of the renewable energy sources and electricity market prices, along with the need to coordinate energy storage, conventional production backup and other time-dependent constraints, make optimal energy scheduling a key and pivotal need for tools that aim at managing these energy systems.

The multi-period optimisation (MPO) technology accounts for restrictions that affect multiple time periods, usually in the future. It is indispensable for making decisions involving energy inventories, time-sensitive operating constraints (such as minimum downtime or uptime for pieces of equipment like boilers and gas turbines and their sequencing, and so on), or the start/stop schedule of complex equipment (for example, gas turbines with their related heat recovery steam generation and steam turbo generator). It is useful for different types of renewable energy systems. A few examples showing some of the potential uses are: