Stochastic Energy Management Strategy of Smart Building Microgrid with Electric Vehicles and Wind-Solar Complementary Power Generation System

This paper presents a power flow management strategy for a Smart Building Micro Grid (SBMG) integrated with Electric Vehicles Batteries (EVBs), solar and wind generation in a grid-connected architecture. Proposed optimal power flow management topology uses Stochastic Model Predictive Control (SMPC) architecture to cater the uncertainties caused by stochastic behaviour of Variable Renewable Energy Sources (VRES) and load demand. Two optimization objectives have been considered to achieve technically and economically superior control. Firstly, Demand Response (DR) frame work has been exploited to cater the stochastic behaviour and generation forecasting error of intermittent sources. Secondly degradation of EVBs has been also incorporated to keep the power flow economical for both Electric Vehicles (EVs) owners and micro-grid management authority. A mathematical model is simulated on MATLAB to determine levelized cost of electricity (LCOE) for the proposed SBMG architecture and grid without VRES architecture. Proposed mathematical SBMG architecture is verified by using Hybrid Optimization of Multiple Energy Resources (HOMER) based simulation setup. With the proposed SMPC optimization architecture a significant decrease of 33% in battery degradation has been observed. Negative Cash flow is also decreased in terms of replacement cost of batteries by incorporating battery degradation in optimization architecture. LCOE is decrease from 0.18 to 0.157 ($/kW h) by incorporating SMPC optimization algorithm. The proposed SMPC algorithm is compared with the greedy algorithm to show the significant improvement in the power flow management.