Distribution Substation Allocation Considering Load Uncertainty based on Information Gap Decision Theory

Abstract

This paper presents a novel methodology for optimizing the location, sizing, and service area of distribution substations, explicitly incorporating load uncertainty. The escalating integration of renewable distributed generation and electric vehicles has introduced substantial uncertainties into distribution loads, exceeding conventional prediction errors. To mitigate this challenge, a robust optimization framework is employed, enabling risk modeling and ensuring secure operation under extreme uncertainty scenarios. Information Gap Decision Theory (IGDT) is utilized to effectively model load uncertainty, with a risk-averse strategy adopted to enhance robustness. The objective is to minimize the total cost of distribution system planning, encompassing investment, maintenance, and loss-related expenditures, while adhering to technical constraints. Initially, the distribution substation problem is solved using predicted load point values. Subsequently, a risk-averse IGDT approach is applied to identify robust solutions under load uncertainty. The efficacy of the proposed methodology is demonstrated using a test system. Results indicate that an average load increment of approximately 42.88% can be accommodated with a corresponding 50% increase in permissible cost. In such scenarios, if potential load uncertainty does not materialize, the average substation load would be approximately 55% of their nominal capacities.