Robust optimization for optimal electrical asset placement in small and medium-sized industries

Abstract

The increasing number and diversity of electricity-operated devices, machines, and equipment in small and medium-sized industries present significant challenges for energy arbitrage in supporting the main grid. These challenges include managing unpredictable load fluctuations, maintaining grid stability during peak demand, and balancing dynamic generation and consumption profiles. To address these issues, this study proposes integrating the system with new assets, which encompass controllable resources on both the generation and consumption sides—such as distributed renewable generation systems, electricity operated machine and equipment. The primary objective is to determine the optimal placement of these assets within industrial networks to maximize their efficiency in energy arbitrage operations. We employ robust optimization techniques that leverage historical operational data while accommodating uncertainties, including the maximum fluctuation rate in the generation and consumption of these assets, contracted capacity limits, and defined budgets of uncertainty. Comprehensive case studies demonstrate that optimal asset placement not only reduces operational costs and improves grid performance for industrial customers but also provides valuable insights for policymakers in formulating effective energy management strategies.