Module 7: Microgrids, Distributed Generation, and Energy Management Systems
This module examines the architecture, operation, and control of microgrids—localized energy networks that integrate distributed generation (DG), energy storage, and intelligent control systems. Students will gain an in-depth understanding of how microgrids enhance energy resilience, improve renewable integration, and enable decentralized decision-making in smart grid environments.
Lecture Content
A microgrid can operate either in grid-connected or islanded mode, providing operational flexibility and energy autonomy. It typically includes distributed energy resources (DERs) such as photovoltaic systems, small-scale wind turbines, combined heat and power (CHP) units, and battery storage systems. These components are coordinated through a centralized or decentralized Energy Management System (EMS), which ensures optimal power flow, load balancing, and system stability.
The control of microgrids involves a hierarchical structure similar to the main grid, encompassing primary, secondary, and tertiary control layers. The primary control stabilizes voltage and frequency through droop characteristics, the secondary control restores nominal setpoints, and the tertiary control optimizes energy dispatch based on market and operational criteria. Advanced algorithms—such as model predictive control (MPC), reinforcement learning (RL), and multi-agent systems (MAS)—are increasingly used for autonomous and adaptive operation.
Microgrids also play a key role in energy resilience. During faults or blackouts, they can disconnect from the main grid and continue supplying local loads autonomously. This islanding capability is supported by robust synchronization mechanisms and fault ride-through (FRT) functionalities. Communication-enabled control ensures seamless reconnection once the grid stabilizes.
Energy Management Systems (EMS) within microgrids integrate forecasting tools, optimization models, and real-time control modules. EMS functions include load forecasting, DER scheduling, storage management, and demand response coordination. Emerging frameworks employ blockchain and peer-to-peer energy trading to facilitate transparent and decentralized transactions among prosumers.
Topics Covered
- Microgrid architecture and components
- Distributed generation (DG) sources and grid interfacing
- Hierarchical microgrid control: primary, secondary, tertiary
- Energy Management Systems (EMS) and optimization techniques
- Load forecasting and demand response integration
- Grid-connected vs. islanded operation modes
- Peer-to-peer trading and blockchain-enabled microgrids
- Case studies: Campus and community-based microgrids
Learning Objectives
- Describe the architecture and operational principles of microgrids.
- Analyze control strategies for stability and resilience in distributed systems.
- Design an EMS for optimal scheduling of distributed generation and storage.
- Evaluate the techno-economic and regulatory challenges in microgrid deployment.
Suggested Learning Activities
- Develop a simulation of grid-connected and islanded microgrid operation using MATLAB/Simulink or DIgSILENT PowerFactory.
- Perform an economic dispatch study considering renewable intermittency and battery constraints.
- Design a basic EMS algorithm for optimizing DER utilization and load management.
- Evaluate a real-world microgrid case (e.g., the Brooklyn Microgrid, NY) and identify lessons learned.
Recommended Reading
- Hatziargyriou, N. (2020). Microgrids: Architectures and Control. Wiley-IEEE Press.
- Lasseter, R. H. (2011). “Smart Distribution: Coupled Microgrids.” Proceedings of the IEEE.
- Guerrero, J. M., et al. (2013). “Hierarchical Control of Microgrids—An Overview.” IEEE Transactions on Industrial Electronics.
- U.S. Department of Energy. (2021). Microgrid Research and Development Program Report.