Probabilistic Hosting Capacity Analysis via Bayesian Optimization

TL;DR

This paper introduces a Bayesian Optimization framework to efficiently solve probabilistic hosting capacity analysis in distribution networks, significantly improving solution quality and computational efficiency over existing methods.

Contribution

The paper develops a novel Bayesian Optimization-based method for PHCA, addressing computational challenges and outperforming traditional algorithms in accuracy and speed.

Findings

BayesOpt achieves 25% higher hosting capacity.

Reduces computation time by 70% on average.

Outperforms interior point and active set algorithms.

Abstract

This paper studies the probabilistic hosting capacity analysis (PHCA) problem in distribution networks considering uncertainties from distributed energy resources (DERs) and residential loads. PHCA aims to compute the hosting capacity, which is defined as the maximal level of DERs that can be securely integrated into a distribution network while satisfying operational constraints with high probability. We formulate PHCA as a chance-constrained optimization problem, and model the uncertainties from DERs and loads using historical data. Due to non-convexities and a substantial number of historical scenarios being used, PHCA is often formulated as large-scale nonlinear optimization problem, thus computationally intractable to solve. To address the core computational challenges, we propose a fast and extensible framework to solve PHCA based on Bayesian Optimization (BayesOpt). Comparing with state-of-the-art algorithms such as interior point and active set, numerical results show that the proposed BayesOpt approach is able to find better solutions (25% higher hosting capacity) with 70% savings in computation time on average.