Abstract
The rise of intermittent renewable generation and the electrification of energy uses, such as transportation, heating and cooling, and industrial processes are causing grid congestion and connection delays in some countries, a trend expected to expand with the energy transition. Distribution system operators face high uncertainty in forecasting peak loads due to the uncertain adoption pace of technologies such as electric vehicles, distributed generation, and industrial electrification.This paper presents a stochastic optimization model to determine investment timing and the use of flexibility solutions in distribution networks under uncertainty, based on the real options framework. The methodology employs triggers as decision rules, e.g., investing in a new transformer when the peak load reaches a threshold, enabling adaptable strategies where future decisions are contingent to unfolding information, instead of traditional fixed planning, e.g., invest in a new transformer in a specific year of the planning period. The proposed model optimizes the level of this triggers (i.e., decision rules). Results show a synergy between this approach and flexibility solutions, enhancing economic efficiency. Adaptable strategies may recommend anticipatory investment in some scenarios and deferral in others compared to the traditional planning approach, striking a balance between enabling faster connections and cost-efficiency.
Trigger rules optimization for adaptable electricity distribution planning: untapping the value of flexibility
