Model Predictive Control Based Demand Response Scheme for Peak Demand Reduction in a Smart Campus Integrated Microgrid

This paper presents an effective solution to manage the power flows exchanges in a campus integrated microgrid for peak reduction/shaving purposes. The campus integrated microgrid is composed of photovoltaic parking shades, an energy storage system, electric vehicles and bikes, loads, an advanced metering infrastructure, and a smart control unit. The latter is based on Model Predictive Control (MPC) whose objective is to reduce/shave the peak load of the campus while satisfying the Energy Storage System ESS, electrical Vehicles (EVs) and Electrical Bikes (EBs) state of charge. The proposed strategy aims to take the advantage of combining storage and photovoltaic (PV) systems to Vehicle to Campus (V2C) and Bike to Campus (B2C) concepts to support the microgrid to pay the minimum billing power while ensuring a good service quality to the EVs and EBs users. For that, the integration of the renewable energy sources and the different storage systems into the microgrid is modeled, and the MPC-based optimization framework is formulated. Besides, the results related to the application of the MPC to real case studies are presented, integrating the effects of static and dynamic weighting factors on the microgrid operation.

Automated summary

This study proposes an effective solution using Model Predictive Control to manage power flow exchanges in a campus integrated microgrid for peak reduction/shaving, aiming to reduce load peaks while satisfying the state of charge of energy storage systems and vehicles. By integrating renewable energy sources and different storage systems, the microgrid is able to pay the minimum billing power while ensuring good service quality for users.