Distributed Energy Trading Management for Renewable Prosumers with HVAC and Energy Storage

TL;DR

This paper presents a distributed energy management method for residential HVAC and batteries that enables privacy-preserving energy trading among users, significantly reducing peak load and overall energy costs.

Contribution

It introduces a novel distributed optimization algorithm based on ADMM for residential energy trading that preserves user privacy and improves cost efficiency.

Findings

Reduces users' energy costs by 23% on average.

Effectively incentivizes energy trading among users.

Decreases peak load in residential energy systems.

Abstract

Heating, ventilating, and air-conditioning (HVAC) systems consume a large amount of energy in residential houses and buildings. Effective energy management of HVAC is a cost-effective way to improve energy efficiency and reduce the energy cost of residential users. This work develops a novel distributed method for the residential transactive energy system that enables multiple users to interactively optimize their energy management of HVAC systems and behind-the-meter batteries. Specifically, this method effectively reduces the cost of smart homes by employing energy trading among users to leverage their power usage flexibility without compromising the users' privacy. To achieve this goal, we design a distributed optimization algorithm based on the alternating direction method of multipliers (ADMM) to automatically operate the HVAC system and batteries, which minimizes the energy costs of users. Specifically, we decouple the optimization problem into a primal subproblem and a dual subproblem. The primal subproblem is solved by the users, and the dual subproblem is solved by the grid operator. Unlike the existing centralized method, our approach only uses the users' private information locally for solving the primal subproblem hence preserves the users' privacy. Using real-world data, we validate our proposed algorithm through extensive simulations in Matlab. The results demonstrate that our method effectively incentivizes the energy trading among the users to reduce users' peak load and reduce the overall energy cost of the system by 23% on average.