Design Engineering
Showcase 2020

Due to the nature and extent of both the variables and the uncertainty associated with these systems, this kind of optimization problem is likely to become quickly intractable or overly biased toward a specific project in practice. The novelty of this research stems from the use of a deep reinforcement learning approach for exploration of design space combined with a unified framework including resilience, sustainability and flexibility which can be more easily applied and understood by decision makers than current available alternatives.

The system design space for different kinds of projects is proposed to be explored using a combination of physical design variables, subject to a number of feasibility constraints, and the decision rules for flexibility they allow via a deep reinforcement learning approach. An economic value-based formulation is implemented for the reward function to allow mapping of actions (such as including a real option “in” a system or exercising one “on” the system) and states(alternative resulting system design configurations variables) for the formulation of a learning agent policy based on a Markov decision process. New alternative metrics for resilience and sustainability are to be developed which allow better integration into the economic reward function and more efficient exploration of the trade-offs in the design

Incorporating these factors into a single model will provide a credible quantitative analysis tool for designers, allowing better exploration of the trade-offs among those complexities and improving decision making under uncertainty. Adopting an ML approach will help reduce computational costs and modelling requirements for the design and decisionmaking process. Ultimately, the development of such a framework will help enhance the transition towards the UN SDGs to yield more resilient and adaptable energy systems with reduced downside exposure in the face of climate change.