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

Joonsung Jung, Hyunjoong Kim, Hyunghwan Shin, Jip Kim

TL;DR

This work tackles the challenge of providing fast frequency response in power systems with reduced inertia due to high renewable penetration. It proposes a community energy management system (CEMS) with a three-level hierarchical control that coordinates DERs, energy storage, HVAC, and PV under PV uncertainty, combining Level1 FFR capacity allocation, Level2 scenario-based SMPC for real-time operation, and Level3 frequency-correction adjustments. Case studies on a campus building cluster show that energy costs can be reduced by 10% and FFR capacity increased by 24% while maintaining occupant comfort and system reliability. The results demonstrate that energy sharing and hierarchical optimization across buildings can enhance grid frequency stability and reduce operating costs in distributed building portfolios.

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.

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

TL;DR

This work tackles the challenge of providing fast frequency response in power systems with reduced inertia due to high renewable penetration. It proposes a community energy management system (CEMS) with a three-level hierarchical control that coordinates DERs, energy storage, HVAC, and PV under PV uncertainty, combining Level1 FFR capacity allocation, Level2 scenario-based SMPC for real-time operation, and Level3 frequency-correction adjustments. Case studies on a campus building cluster show that energy costs can be reduced by 10% and FFR capacity increased by 24% while maintaining occupant comfort and system reliability. The results demonstrate that energy sharing and hierarchical optimization across buildings can enhance grid frequency stability and reduce operating costs in distributed building portfolios.

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.

Paper Structure

This paper contains 17 sections, 8 equations, 7 figures.

Table of Contents

  1. Introduction
  2. Model Formulation
  3. Community Management Structure
  4. PV Generation Modeling
  5. Energy Sharing and Power Balance
  6. Residential Thermal Modeling
  7. ESS Operation Modeling
  8. Hierarchy Framework for CEMS
  9. CEMS Control Method
  10. Level 1: FFR Allocation
  11. Level 2: Optimal Operation Scheduling
  12. Level 3: Frequency Control
  13. Case Study
  14. Basic data
  15. Energy Sharing and FFR Provision
  16. ...and 2 more sections

Figures (7)

  • Figure 1: Provision of FFR through community structure and energy sharing between buildings
  • Figure 2: Hierarchical control with SMPC in CEMS: the solid line represents day-ahead Level 1, the dashed line indicates real-time Level 2 with SMPC, and the dotted line shows Level 3 for FFR.
  • Figure 3: PV generation
  • Figure 4: Community net demand with energy sharing
  • Figure 5: ESS operation and reserve analysis for CEMS
  • ...and 2 more figures