When Renewable Energy Meets Building Thermal Mass: A Real-time Load Management Scheme
This work addresses the challenge of real-time load management in smart buildings with renewable energy, but the contribution is incremental as it applies known stochastic optimization techniques to a specific domain.
The paper tackles optimal power management in renewable-driven smart building microgrids under noise-corrupted conditions, proposing a stochastic optimization framework with a user satisfaction and electricity consumption balanced profit model. A Bregman projection-based mirror descent algorithm is designed, with convergence guarantees and upper bounds, and simulations demonstrate its effectiveness.
We consider the optimal power management in renewable driven smart building MicroGrid under noise corrupted conditions as a stochastic optimization problem. We first propose our user satisfaction and electricity consumption balanced (USECB) profit model as the objective for optimal power management. We then cast the problem in noise corrupted conditions into the class of expectation maximizing in stochastic optimization problem with convex constraints. For this task, we design a Bregemen projection based mirror decent algorithm as an approximation solution to our stochastic optimization problem. Convergence and upper-bound of our algorithm with proof are also provided in our paper. We then conduct a broad type of experiment in our simulation to test the justification of our model as well as the effectiveness of our algorithm.