Noise-Aware Bayesian Optimization Approach for Capacity Planning of the Distributed Energy Resources in an Active Distribution Network

TL;DR

This paper introduces a noise-aware Bayesian optimization method for capacity planning of distributed energy resources in active distribution networks, effectively handling environmental noise to improve cost efficiency and computational performance.

Contribution

The paper proposes a novel noise-aware Bayesian optimization algorithm tailored for capacity planning in ADNs, considering RES variability, demand response, and security constraints.

Findings

Outperforms traditional methods in noisy environments

Achieves lower annual costs in simulations

Demonstrates higher computational efficiency

Abstract

The growing penetration of renewable energy sources (RESs) in active distribution networks (ADNs) leads to complex and uncertain operation scenarios, resulting in significant deviations and risks for the ADN operation. In this study, a collaborative capacity planning of the distributed energy resources in an ADN is proposed to enhance the RES accommodation capability. The variability of RESs, characteristics of adjustable demand response resources, ADN bi-directional power flow, and security operation limitations are considered in the proposed model. To address the noise term caused by the inevitable deviation between the operation simulation and real-world environments, an improved noise-aware Bayesian optimization algorithm with the probabilistic surrogate model is proposed to overcome the interference from the environmental noise and sample-efficiently optimize the capacity planning model under noisy circumstances. Numerical simulation results verify the superiority of the proposed approach in coping with environmental noise and achieving lower annual cost and higher computation efficiency.