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

TL;DR

The paper tackles scheduling of residential loads to balance supply and demand in grids with high renewable penetration, while preserving user comfort and data privacy. It formulates a neighborhood-level MILP for demand response and solves it with a distributed Dantzig-Wolfe (DW) approach using a restricted master problem and column generation, enabling scalable coordination across thousands of homes. Key contributions include a practical DR strategy that integrates renewables, a privatized and scalable distributed algorithm with near-optimal performance (optimality gap < 1%) compared to a centralized solver, and demonstrated load shaping under varying PV conditions. The results indicate substantial improvements in grid reliability and reduced external purchases, making the framework suitable for real-time or near-real-time operation in large communities. The work paves the way for robust, scalable DR in distribution networks with high renewable integration and complex home-level preferences.

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.

Figures (6)

• Figure 1: Information exchange between participating homes and the aggregator.
• Figure 2: Change in total load consumption of the community with 1000 homes after optimization under low (top) and high (bottom) PV generation scenarios.
• Figure 3: Change in the difference between total demand and renewables for a community with 1000 homes after optimization.
• Figure 4: Load shaping capacity of the formulation under different rates of incentive payments.
• Figure 5: Change in optimization time with respect to community size.