Causal Dynamics provides technical feasibility solutions that support the industry’s energy transition. They pioneer in leveraging artificial intelligence (AI), advanced analytics and energy systems modelling tools to accurately simulate energy systems and effectively optimise costs.
These innovative capabilities brought together under the banner of a single company gives Causal Dynamics the unique capacity to deliver complete and holistic assessments of the asset performance and financial returns of any integrated systems.
The rapid advancement and inherent risks of renewable energy systems pose both opportunities and complex challenges to developers and operators, who must contend with the variable nature of renewables, rapidly changing regulations, uncertainty of future demand, and risks to supply chains.
Founded on the notion that the global energy transition requires different ways of thinking, Causal Dynamics does the hard work of removing complexity from multifaceted energy systems, so operators can focus on improving reliability, efficiency, and project economics.
Dr Ashkan Rafiee, Co-founder and Chief Executive Officer at Causal Dynamics, told Green Review that understanding the dynamic nature of energy sources and their associated systems required analysis at the level of root causality, not just correlation, to effectively address advanced engineering problems.
He said: “The inherent complexity of engineering and industry challenges necessitates finding the optimum design; however, the solutional space is going to be completely non-linear.
“Navigating the complexity and scale of this solution space is extraordinarily difficult for the human brain, and that is where the AI solutions come into play – we can run these systems through many different scenarios to identify the right or optimal parameters.”
Dr Rafiee added that energy operations often have multiple conflicting business objectives, such as maximising efficiency while minimising cost, which is further complicated when combined with constraints around project design.
To understand these complex integrated objectives, find the optimal design, and execute it while mitigating diverse sets of risks, complex simulations or analyses aided with machine learning are required. Moreover, despite exponential growth in capacity, computing power is still limited by cost – running thousands of simulations will predictably give more accurate data, but it is prohibitively expensive.
Causal Dynamics’s approach is to use its academic rigour and cutting-edge technological skillset to obtain the same accuracy but with fewer simulations. AI is essential to this form of advanced analysis because it allows for the simulation of hundreds if not thousands of scenarios to identify a suitable pathway that accounts for every aspect of a project.
This can create new opportunities to build and operate energy assets, as well as increase the accuracy of data for decision making across project design and operations.
Dr Nitin Repalle, Co-founder and Chief Technology Officer at Causal Dynamics, noted that the best solutions for a rapidly evolving energy landscape come from blending multi-disciplinary skills andiverse viewpoints.
He said: “Our interdisciplinary approach allows us to analyse problems from multiple perspectives and develop holistic solutions tailored to specific needs. “Developers of renewables and decarbonisation projects have been asked to take start-up level risks on billion-dollar projects, and the margins that traditional established industries carried may not be feasible for these new ventures.”
“What we want to bring to the energy industry is that capability to look at a project from a bird’s eye view, while also giving insights into the execution, operation and decommissioning all at once, so operators and developers have complete visibility of what they are getting into.”
The company can tailor its models to suit the scale and scope of every project stage and budget, from high-level assessment of project fundamentals at the integrated systems level, to structural modelling at the system level, and right down to indepth analyses of critical engineering detail at the component level.
The business further enhances its comprehensive approach with the flexibility of analysing projects from both a top-down and bottom-up perspective, gaining valuable insights from the resulting multilayered data. A top-down view starts with design and planning through to execution, and analyses how various project elements fit together, while bottom-up involves starting with component level optimisation, which is then integrated up to the system level and finally up to the hub level.
This is demonstrated in Causal Dynamics’s most comprehensive project, which involved the technoeconomical modelling of an entire energy hub, comprised of wind farms, solar farms, energy storage units, and hydrogen production facilities.
The model examined energy flows between the hub’s various nodes and analysed hourly wind and solar data over a full year. Causal Dynamics then assessed the net present value of the total energy portfolio, maximising it through optimisation while adhering to production level constraints and carbon emission standards.
In addition to the renewables hub modelling, the advanced framework can also be applied to the emerging carbon industry, where energy hub models for carbon capture as well as the generation of lowcarbon fuels can be implemented.
An example that highlights their energy systems modelling is the in-depth computational fluid dynamics simulation they completed for a wind energy operator. The simulation modelled the interaction between the wind and a wind turbine, with a particular focus on vortex shedding, and was essential for understanding the cyclic loading on turbine blades, a key factor in determining their fatigue life.
Causal Dynamics was able to predict and improve blade durability and performance by accurately modelling the physical forces acting on wind turbine blades, which underscores the importance of detailed analysis needed for new energy systems.
Similarly, complex models that simulate fluid dynamics can be vital for the broader offshore industry, including for the analysis of wave energy.
