Title
A Single-Level Rule-Based Model Predictive Control Approach for Energy Management of Grid-Connected Microgrids
Author
Pippia, T.
Sijs, J.
de Schutter, B.
Publication year
2020
Abstract
A single-level rule-based model predictive control (RBMPC) scheme is presented for optimizing the energy management of a grid-connected microgrid composed of local production units, renewable energy sources, local loads, and several types of energy storage systems (ESSs). The single-level controller uses two different models that yield different descriptions of the microgrid and use different sampling times. The model with a smaller sampling time provides a more detailed description of the microgrid, in order to keep track of the fast dynamics, while the model with a higher sampling time provides a less detailed description and is used for making long-term predictions when it is not needed anymore to track the fast dynamics. Moreover, we propose a novel RBMPC method that assigns the value to the binary decision variables in the hybrid microgrid model, e.g., ON or OFF status of the generators and charging or discharging mode of ESSs, through if–then–else rules, which rely on the price of electricity and the local net imbalance. The standard method of applying model predictive control (MPC) to a hybrid model results in a mixed-integer linear programming (MILP) problem. Our proposed rule-based method is able to convert the standard MILP problem into a linear one. We compare our approach through simulations to the MILP approach and show that our method yields almost no loss in performance while providing a significant reduction in the computation time. Index Terms— Energy management system, energy storage
Subject
Energy management system
Mixed logical dynamical (MLD) systems
Model predictive control (MPC)
optimization
Electric energy storage
Energy management
Integer programming
Microgrids
Predictive control systems
Renewable energy resources
Energy Storage Systems (ESSs)
Local production
Long-term prediction
Microgrid modeling
Mixed-integer linear programming
Renewable energy source
Rule-based method
Rule-based models
Model predictive control
To reference this document use:
http://resolver.tudelft.nl/uuid:cf6b6295-baf0-4a51-987e-6c879e9240fb
TNO identifier
882213
Publisher
Institute of Electrical and Electronics Engineers Inc.
ISSN
1063-6536
Source
IEEE Transactions on Control Systems Technology, 28 (28), 2364-2376
Document type
article