Coordinated Scheduling of Residential Distributed Energy Resources to Optimize Smart Home Energy Services

TLDR

The study develops algorithmic enhancements to a residential decision‑support tool that lets consumers optimize their electrical energy services and evaluates the consumer value of coordinated DER scheduling. Using particle swarm optimization with stochastic repulsion, the tool schedules distributed energy resources to maximize net benefits and compares coordinated schedules against separate ones to compute cost savings. The results show that users can determine whether coordinated DER scheduling is advantageous given their service needs, DER mix, and tariff structure.

Abstract

We describe algorithmic enhancements to a decision-support tool that residential consumers can utilize to optimize their acquisition of electrical energy services. The decision-support tool optimizes energy services provision by enabling end users to first assign values to desired energy services, and then scheduling their available distributed energy resources (DER) to maximize net benefits. We chose particle swarm optimization (PSO) to solve the corresponding optimization problem because of its straightforward implementation and demonstrated ability to generate near-optimal schedules within manageable computation times. We improve the basic formulation of cooperative PSO by introducing stochastic repulsion among the particles. The improved DER schedules are then used to investigate the potential consumer value added by coordinated DER scheduling. This is computed by comparing the end-user costs obtained with the enhanced algorithm simultaneously scheduling all DER, against the costs when each DER schedule is solved separately. This comparison enables the end users to determine whether their mix of energy service needs, available DER and electricity tariff arrangements might warrant solving the more complex coordinated scheduling problem, or instead, decomposing the problem into multiple simpler optimizations.