Multi-Layer Model Predictive Optimization of Energy Efficient Building Microgrids
- Resource Type
- Periodical
- Authors
- Fatehi, N.; Nazari, M.H.
- Source
- IEEE Access Access, IEEE. 12:13037-13045 2024
- Subject
- Aerospace
Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Engineering Profession
Fields, Waves and Electromagnetics
General Topics for Engineers
Geoscience
Nuclear Engineering
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Buildings
Microgrids
Lighting
Optimization
Predictive models
Optimal scheduling
Load modeling
Sensitivity analysis
Energy efficiency
Internet of Things
Building microgrid
energy efficiency
IoT-enabled dispatchable loads
multi-layer model predictive optimization
sensitivity analysis
- Language
- ISSN
- 2169-3536
This paper introduces a multi-layer model predictive optimization (mLMPO) framework for energy management of building microgrids with Internet of Things (IoT)-enabled dispatchable loads and Distributed Energy Resources (DERs). The goal is to achieve high energy efficiency and demand response capability, while satisfying occupants’ comfort. Due to the diversity of on-site resources and complexity of occupancy modeling, traditional building management systems (BMS) cannot always optimize energy efficiency and maintain occupant comfort simultaneously. This paper will address this gap and develop a new framework for implementing mLMPO in building microgrids. The data from a large academic building in California is used for simulation studies. The results of this paper can provide a road map for co-optimization of energy efficient and occupants comfort in IoT-enabled smart buildings and microgrids.