Decision tree aided planning and energy balancing of planned community microgrids

TL;DR

This paper introduces a decision tree-based tool for planning and real-time energy balancing in microgrids within planned communities, enhancing their safety, resilience, and efficiency.

Contribution

It presents a novel decision tree approach for energy storage planning and control in community microgrids, validated through case studies and real-time implementation.

Findings

Effective energy storage planning demonstrated

Successful real-time energy balancing implementation

Improved resilience and safety of community microgrids

Abstract

Planned Communities (PCs) present a unique opportunity for deployment of intelligent control of demand-side distributed energy resources (DER) and storage, which may be organized in Microgrids (MGs). MGs require balancing for maintaining safe and resilient operation. This paper discusses the implications of using MG concepts for planning and control of energy systems within PCs. A novel tool is presented, based on decision trees (DT), with two potential applications: (i) planning of energy storage systems within such MGs and (ii) controlling energy resources for energy balancing within a PC MG. The energy storage planning and energy balancing methodology is validated through sensitivity case studies, demonstrating its effectiveness. A test implementation is presented, utilizing distributed controller hardware to execute the energy balancing algorithm in real-time.