Community Energy Management System for Fast Frequency Response: A Hierarchical Control Approach

The increase in renewable energy sources (RES) has reduced power system inertia, making frequency stabilization more challenging and highlighting the need for fast frequency response (FFR) resources. While building energy management systems (BEMS) equipped with distributed energy resources (DERs) can provide FFR, individual BEMS alone cannot fully meet demand. To address this, we propose a community energy management system (CEMS) operational model that minimizes energy costs and generates additional revenue, which is provided FFR through coordinated DERs and building loads under photovoltaic (PV) generation uncertainty. The model incorporates a hierarchical control framework with three levels: Level 1 allocates maximum FFR capacity, Level 2 employs scenario-based stochastic model predictive control (SMPC) to adjust DER operations and ensure FFR provision despite PV uncertainties, and Level 3 performs rapid load adjustments in response to frequency fluctuations detected by a frequency meter. Simulation results on a campus building cluster demonstrate the effectiveness of the proposed model, achieving a 10\% reduction in energy costs and a 24\% increase in FFR capacity, all while maintaining occupant comfort and enhancing frequency stabilization.

Publication Details

Journal
ArXiv.org
Published
2025-03-07
Primary Topic
Smart Grid Energy Management
Type
article
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article

Community Energy Management System for Fast Frequency Response: A Hierarchical Control Approach

ArXiv.org
Smart Grid Energy Management
article

Community Energy Management System for Fast Frequency Response: A Hierarchical Control Approach

article en

Abstract

The increase in renewable energy sources (RES) has reduced power system inertia, making frequency stabilization more challenging and highlighting the need for fast frequency response (FFR) resources. While building energy management systems (BEMS) equipped with distributed energy resources (DERs) can provide FFR, individual BEMS alone cannot fully meet demand. To address this, we propose a community energy management system (CEMS) operational model that minimizes energy costs and generates additional revenue, which is provided FFR through coordinated DERs and building loads under photovoltaic (PV) generation uncertainty. The model incorporates a hierarchical control framework with three levels: Level 1 allocates maximum FFR capacity, Level 2 employs scenario-based stochastic model predictive control (SMPC) to adjust DER operations and ensure FFR provision despite PV uncertainties, and Level 3 performs rapid load adjustments in response to frequency fluctuations detected by a frequency meter. Simulation results on a campus building cluster demonstrate the effectiveness of the proposed model, achieving a 10\% reduction in energy costs and a 24\% increase in FFR capacity, all while maintaining occupant comfort and enhancing frequency stabilization.

ArXiv.org
Affordable and clean energy
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Smart Grid Energy Management
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