(372d) Data-Driven Design and Operation of Resilient Renewable Energy Supply Chain Networks Under Demand Uncertainty

Recent years have underscored the critical importance of resilience in global energy supply chains, as geopolitical conflicts and natural disasters have exposed vulnerabilities, especially in import dependent countries with ambitious energy transition targets and rapidly evolving energy systems. The resulting volatility and disruptions in energy supply have hampered economic activity, increased industrial production costs, and limited access to affordable energy. These disruptions have once again revealed the risks of supply concentration and supply chain networks focused only on cost minimization lacking flexibility and redundancy [1]. In this respect, the need to balance cost-efficiency with resilience and security of supply has become increasingly vital, as energy systems shift to match supply and demand in a landscape with an increased share of renewable sources of variable nature. Therefore, there is a call for modeling frameworks to help decision-makers make informed decisions on planning and operation of environmentally conscious and resilient energy supply chains [2].

In this research, we present a data-driven modeling and optimization framework for the design and operation of resilient renewable energy supply chains under demand uncertainty. In this framework, the stochasticity of demand is captured using a data-driven model to generate representative scenarios to handle uncertainty through a systematic approach[3]. In the next step, generated scenarios are utilized in a multi-period stochastic optimization framework that considers multiple production locations and transportation modes for the planning and operation of the optimal resilient and environmentally conscious supply network. An illustrative case study of designing and operation of a renewable energy supply chain network for Germany considering various production locations, energy carriers and transportation modes is considered under resilience and environmental footprint constraints. The impact of demand uncertainty and resilience on the optimal design of the energy supply networks is systematically investigated using the proposed modeling framework.

References:

1. Bechtsis, D., Tsolakis, N., Iakovou, E., & Vlachos, D. 2021. Data-driven secure, resilient and sustainable supply chains: gaps, opportunities, and a new generalised data sharing and data monetisation framework. International Journal of Production Research, 60(14), 4397–4417
2. Iseri, H., Iseri, F., Iakovou, E. and Pistikopoulos, E., 2024. A Multi-Objective Decision-Making Framework for Renewable Energy Transportation. In 2024 AIChE Annual Meeting. AIChE.
3. Iseri, F., Iseri, H, Shah H., Iakovou, E. and Pistikopoulos, E., 2025, Planning strategies in the energy sector: Integrating bayesian neural networks and uncertainty quantification in scenario analysis & optimization, Computers & Chemical Engineering