MILP-based optimal day-ahead scheduling for system-centric CEMS supporting different types of homes and energy trading

TL;DR

This paper presents an MILP-based community energy management system that optimizes day-ahead scheduling for diverse homes, reducing costs and improving comfort through local energy trading.

Contribution

It introduces a novel MILP model for system-centric community energy scheduling that efficiently handles diverse home types and local energy trading.

Findings

Community CEMS reduces energy costs by 50% compared to prosumer-centric CEMS.

The MILP model solves scheduling in approximately 118 seconds for 500 homes.

Simulation confirms improved thermal comfort and economic benefits.

Abstract

Optimal day-ahead scheduling for a system-centric community energy management system (CEMS) is proposed to provide economic benefits and user comfort of energy management at the community level. Our proposed community includes different types of homes and allows prosumers to trade energy locally using mid-market rate pricing. A mathematical model of the community is constructed and the optimization problem of this model is transformed into an MILP problem that can be solved in a short time. By solving this MILP problem, the optimization of the overall energy cost of the community and satisfaction of the thermal comfort at every home are achieved. For comparison, we also establish two different scenarios for the same community: a prosumer-centric CEMS and no CEMS. The simulation results demonstrate that the overall energy cost of the community with the system-centric CEMS is the smallest among the three scenarios and is only half that of the community with the prosumer-centric CEMS. Moreover, by using linear transformation, the computational time of the optimization problem of the proposed system-centric CEMS is only 118.2 s for a 500-home community, which is a short time for day-ahead scheduling of a community.