A Single-Level Rule-Based Model Predictive Control Approach for Energy Management of Grid-Connected Microgrids
article
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
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
Topics
Energy management systemMixed logical dynamical (MLD) systemsModel predictive control (MPC)optimizationElectric energy storageEnergy managementInteger programmingMicrogridsPredictive control systemsRenewable energy resourcesEnergy Storage Systems (ESSs)Local productionLong-term predictionMicrogrid modelingMixed-integer linear programmingRenewable energy sourceRule-based methodRule-based modelsModel predictive control
TNO Identifier
882213
ISSN
10636536
Source
IEEE Transactions on Control Systems Technology, 28(6), pp. 2364-2376.
Publisher
Institute of Electrical and Electronics Engineers Inc.
Article nr.
8876882
Pages
2364-2376
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