Energy Smart Buildings: Parallel Uniform Cost-Search with Energy Storage and Generation

TL;DR

This paper presents a novel scheduling method for energy-efficient building operation that integrates energy storage, local generation, and dynamic pricing, using parallel uniform cost-search to optimize costs and reduce computation time.

Contribution

It introduces a new optimization strategy employing parallel uniform cost-search for building energy management considering storage and generation.

Findings

Including local energy storage reduces costs by up to 22.64%.

Parallel uniform cost-search accelerates scheduling by a factor of 4.7.

The approach is validated with real-world data and modular architecture.

Abstract

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.