  The amalgamation of Internet of Things and the smart grid enables the energy
optimal scheduling of appliances based on user needs and dynamic energy prices.
Additionally, progress in local storage technology calls for exploiting
additional sources of flexibility. In this paper, we propose a scheduling
approach for building operation management, considering factors such as energy
storage, local energy generation, and dynamic energy prices. In addition, we
propose a new optimization strategy to discover the optimal scheduling of
devices. Our approach utilizes parallel uniform cost-search to explore the
complex search space and to find the optimal schedule within a user-acceptable
amount of time. The evaluation utilizes real-world data for the devices, and
the price signals, while the architecture is designed following a micro-service
approach, enabling modularity and loose-coupling. The evaluation shows that
including local energy storage as part of the optimization problem further
reduces overall costs by up to 22.64\% when compared to schedules without
energy storage. Parallel uniform cost-search decreases the time to find the
optimal schedule by a factor of 4.7 with respect to the traditional uniform
cost-search algorithm.
