Comprehensive Review on the Control of Heat Pumps for Energy Flexibility in Distribution Networks

TL;DR

This review paper discusses various control strategies for heat pumps to enhance energy flexibility in distribution networks, emphasizing the need for advanced methods like MPC and RL for better grid integration and performance.

Contribution

It provides a comprehensive overview of scheduling-based and real-time optimization methods for heat pump control, highlighting gaps and future research directions in energy flexibility strategies.

Findings

RBCs are commonly used for energy flexibility in practice.

Advanced methods like MPC and RL are underexplored in grid impact studies.

Validation needed for cold-weather regions and demand charge schemes.

Abstract

Decarbonization plans promote the transition to heat pumps (HPs), creating new opportunities for their energy flexibility in demand response programs, solar photovoltaic integration and optimization of distribution networks. This paper reviews scheduling-based and real-time optimization methods for controlling HPs with a focus on energy flexibility in distribution networks. Scheduling-based methods fall into two categories: rule-based controllers (RBCs), which rely on predefined control rules without explicitly seeking optimal solutions, and optimization models, which are designed to determine the optimal scheduling of operations. Real-time optimization is achieved through model predictive control (MPC), which relies on a predictive model to optimize decisions over a time horizon, and reinforcement learning (RL), which takes a model-free approach by learning optimal strategies through direct interaction with the environment. The paper also examines studies on the impact of HPs on distribution networks, particularly those leveraging energy flexibility strategies. Key takeaways suggest the need to validate control strategies for extreme cold-weather regions that require backup heaters, as well as develop approaches designed for demand charge schemes that integrate HPs with other controllable loads. From a grid impact assessment perspective, studies have focused primarily on RBCs for providing energy flexibility through HP operation, without addressing more advanced methods such as real-time optimization using MPC or RL-based algorithms. Incorporating these advanced control strategies could help identify key limitations, including the impact of varying user participation levels and the cost-benefit trade-offs associated with their implementation.