Cloud-Based Renewable Energy and Manufacturing as a Service Integration (CREMSI)

Cloud-Based Renewable Energy and Manufacturing as a Service Integration (CREMSI) 

Optimising manufacturing’s value chains faces significant challenges due to the ongoing fluctuations in energy markets and the need for agility in delivering sustainably manufactured products. CREMSI addresses these challenges by pairing the concepts of Manufacturing-as-a-Service (MaaS) and energy flexibility enabled by on-site renewable energy.

The increased digitalisation of manufacturing allows for further incorporation of “as-service” business models under the MaaS paradigm. In this regard, the model of “Energy-as-a-Service” can be put into action by effectively managing on-site renewable energy sources such as hydrogen (stored or generated), solar and biomass. While most of the research on energy flexibility focuses on discreet manufacturing, CREMSI aims to propose new methods to be implemented in the process industry in terms of multidisciplinary modelling techniques (chemical modelling, discrete event simulation and system dynamics) on the way to building digital twins of various manufacturing levels such as the component level (e.g., product, manufacturing unit, energy generation unit) at the low TRL levels (1-3) and then augmenting these models in higher levels like production planning and enterprise resource planning at higher TRL levels (4-6). Once these models are created, it becomes viable to enable renewable energy labelling and documentation, scheduling of energy use and price compensation models to achieve optimal grid load.

Cloud-based solutions will allow for more computational resources to conduct life cycle assessments (LCA) and intelligent decision-making algorithms that assist manufacturers in identifying the best production scenarios. Although the project's focus is on manufacturing energy management, it establishes the basis for energy trading mechanisms as energy generation and consumption data will be collected, allowing for training machine learning models and data-driven decision-making.  

The modular approach of CREMSI ensures scalability, allowing its energy flexibility models to extend beyond the process industry into sectors like automotive, electronics, and pharmaceuticals. This adaptability aligns with Industry 4.0 objectives, paving the way for broader adoption across global supply chains.