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.
Highlights
The significance of smart home research is growing rapidly because of increasing industrial demand
Based on the results presented in this paper, we can conclude that the ECO-Trade algorithm is a better alternative to the previously proposed optimal model considering the accuracy and the solution time
We analyze the solutions generated by the ECO-Trade algorithm for a wide range of problem sizes and identify that the sub-optimal cost potentially arises when the microgrid price reaches the boundary limits
Summary
The significance of smart home research is growing rapidly because of increasing industrial demand. This issue may discourage end-users from participating in energy trading We address this unfair cost distribution problem by assuring Pareto optimality among the households in the microgrid. We can conclude that the ECO-Trade algorithm is a better alternative to the optimal model considering both accuracy and solution time. In contrast to our previous paper [3] where we identified the impact of peer-to-peer trading, this paper analyzes the conditions when the proposed ECO-Trade algorithm breaks down and generates sub-optimal solutions. We describe the reasons of the cost increase by a local minimum and analyze its impact on cost optimization This analysis may help the future researcher to improve the proposed algorithm.
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