Real-time energy optimization and scheduling of buildings integrated with renewable microgrid

Real-time energy optimization is essential for effective load scheduling, cost reduction, maintaining demand and supply balance, and ensuring reliable power system operations. However, real-time energy optimization is challenging due to the unpredictable nature of renewable energy sources (RES) and the behavior of electric loads. On this note, a rigid model is required that can deal with this dilemma. Thus, the Lyapunov optimization technique (LOT) emerged as a solution for the real-time energy optimization problem. This work investigates a smart home equipped with inflexible loads (TV, computer, light, etc.), flexible loads (EVs, HVAC, water heaters, etc.), and RES (photovoltaic and wind energy) in a grid-connected mode that ensures energy trading (purchasing and selling of energy). The aim is to optimize total cost, thermal discomfort cost, and batteries and EVs charging/discharging using LOT by real-time energy optimization, which does not require any system parameters to be anticipated. The proposed algorithm employs LOT for four queues to solve the real-time energy optimization problem. Simulations are conducted for different scenarios and varying weather conditions to endorse the effectiveness of the developed real-time energy optimization solution in various aspects of the performance metrics.

Automated summary

Real-time energy optimization is crucial for load scheduling, cost reduction, demand and supply balance, and power system reliability. However, the unpredictable nature of renewable energy and electric loads poses challenges for real-time optimization. The Lyapunov optimization technique has emerged as a solution to this problem. This research investigates a smart home with various loads and renewable energy sources in a grid-connected mode to optimize cost and energy storage using the Lyapunov optimization technique.