Adaptive-DMPC based energy management and pre-installation techno-economic analysis of a grid-tied cyber-physical community microgrid

The trends for creating smart cities is increasing as it gives assurance of high-quality living standards, however, converting an existing city into a smart one needs more time, investments and is highly tedious. Another concept of community based sustainable living that can be installed in and around outskirts of any city. All the features of smart cities can be brought in by most economical, sustainable, and environmental friendly way. A cyber-physical Community Microgrid (CMG) can have various loads such as heating, cooling, security and lighting purposes. The dependency on grid supply can be reduced by using renewable sources of electricity. Solar water heaters and biogas plant can take care of heating loads. Handling uncertainties in generation, time-varying load scenarios, and dynamics of supply grid are crucial for smooth operation of cyber-physical CMG. In this work, an Energy Management Strategy (EMS) for CMG is proposed using an adaptive Distributed Model Predictive Control (DMPC) that improves scalability with modular architecture, allow enhanced flexibility and adaptability, robustness and reliability, enhanced optimization and predictive capabilities. All possible intermittencies types due to renewables, time-variable demand profiles, smooth charging/discharging cycles and longevity of storage batteries, curtailing of noncritical loads, and dynamic price of grid consumption are applied. Further, use of EMS outcomes is done to develop pre-installation techno-economic analysis for establishing a CMG with optimal component sizing. The given DMPC scheme for CMG is verified by conducting comprehensive test case studies over 24 hrs interval on the Matlab/Simulink and hardware-in-loop testing.