A Distributed Optimization Framework to Regulate the Electricity Consumption of a Residential Neighborhood with Renewables

TL;DR

This paper presents a distributed optimization framework that manages residential electricity consumption with renewables, balancing grid needs and user comfort through a novel mixed integer linear programming approach.

Contribution

It introduces a new distributed optimization method based on Dantzig-Wolfe decomposition for residential demand response considering user comfort.

Findings

Effective load shaping demonstrated in simulations

Framework handles large-scale residential communities efficiently

Balances grid reliability with user comfort

Abstract

Demand response services at the distribution level are emerging as enabling strategies for improving grid reliability in the presence of intermittent renewable generation and grid congestion. For residential loads, space heating and cooling, water heating, electric vehicle charging, and routine appliances make up the bulk of the electricity consumption. Controlling these loads is essential to effectively partake into grid operations and provide services such as peak shaving and demand response. However, maintaining user comfort is important for ensuring user participation to such a program. This paper formulates a novel mixed integer linear programming problem to control the overall electricity consumption of a residential neighborhood by considering the users' comfort and preferences. To efficiently solve the problem for communities involving a large number of homes, a distributed optimization framework based on the Dantzig-Wolfe decomposition technique is developed. We demonstrate the load shaping capacity and the computational performance of the proposed optimization framework in a simulated environment.