Towards Optimal Planning and Scheduling in Smart Homes ()
ABSTRACT
This research addresses the planning
and scheduling problem in and among the
smart homes in a community microgrid. We develop a bi-linear algorithm, named
ECO-Trade to generate the near-optimal schedules of the households’ loads, storage and
energy sources. The algorithm also facilitates Peer-to-Peer (P2P) energy
trading among the smart homes in a community microgrid. However, P2P trading
potentially results in an unfair cost distribution among the participating
households. To the best of our knowledge, the ECO-Trade algorithm is the first
near-optimal cost optimization algorithm which considers the unfair cost
distribution problem for a Demand Side Management (DSM) system coordinated with
P2P energy trading. It also solves the time complexity problem of our
previously proposed optimal model. Our results show that the solution time of
the ECO-Trade algorithm is mostly less than a minute. It also shows that 97% of
the solutions generated by the ECO-Trade algorithm are optimal solutions.
Furthermore, we analyze the solutions and identify that the algorithm sometimes
gets trapped at a local minimum because it alternately sets the microgrid price
and quantity as constants. Finally, we describe the reasons of the cost
increase by a local minimum and analyze its impact on cost optimization.
Share and Cite:
Raisul Alam, M. , St-Hilaire, M. and Kunz, T. (2019) Towards Optimal Planning and Scheduling in Smart Homes.
Smart Grid and Renewable Energy,
10, 179-202. doi:
10.4236/sgre.2019.107012.