Multi-Stage Integrated Transmission and Distribution Expansion Planning Under Uncertainties With Smart Investment Options

Abstract

The shift towards a decentralized paradigm in power systems in response to decarbonization and deregulation efforts necessitates stronger coordination between transmission and distribution operators for cost-effective operation and planning. However, long-term uncertainties in the transition to net-zero are posing major challenges for decision-making. Moreover, literature has traditionally focused on the transmission and distribution expansion planning problems independently, as is customary in industry, leading to a lack of sophisticated integrated planning methods and inefficient expansion decisions in practice. This paper proposes a novel multi-stage stochastic programming framework for the integrated transmission and active distribution networks expansion planning under multi-dimensional uncertainties. Infrastructure investments are co-optimized with non-network alternatives with diverse techno-economic characteristics to support flexible planning. To manage the increased computational complexities, a machine learning-assisted multi-cut Benders decomposition approach is implemented. The case studies firstly highlight the strategic and economic advantages of the proposed multi-stage formulation, and then demonstrate the significant role and value of smart investment options in managing uncertainty. Lastly, the application of the proposed model on a study involving a 229-bus test system and 18 long-term scenarios validates its scalability and practical applicability.