Model Predictive Control for a Heat Pump System with Thermal Storage Tanks: Economical Operation and Demand-Side Management

Model Predictive Control for a Heat Pump System with Thermal Storage Tanks: Economical Operation and Demand-Side Management

Tang, Weihong (TU Delft Mechanical, Maritime and Materials Engineering)

Keviczky, T. (mentor)
Li, Y. (mentor)
Walker, S. (graduation committee)
Khosravi, M. (graduation committee)

Delft University of Technology

Mechanical Engineering | Systems and Control

2024-01-25

In light of the pressing challenges posed by global climate change and the imperative to reduce CO2 emissions, innovative approaches in energy management are critically important. This thesis presents an exploration of heat pumps integrated with Thermal Storage System (TES) systems, an area of research and application pivotal for enhancing energy efficiency and environmental sustainability. The combination of heat pumps and TES systems emerges as a key factor in reducing greenhouse gas emissions and optimizing the utilization of renewable energy. Such integration plays a crucial role in minimizing operational costs, reducing environmental negative impact, and augmenting system efficiency by enabling the storage and later use of energy from renewable sources. Moreover, this integration facilitates the effective management of demand-side energy, bolstering the capacity to incorporate fluctuating renewable generation into the energy grid. This is achieved by dynamically load shifting to balance energy supply and demand.

A central aspect of this thesis is the utilization of Model Predictive Control (MPC) for advanced energy management. The research delves into the use of MPC to optimize the operational economy of the system, aiming to maximize cost-efficiency. Additionally, an innovative MPC-based Demand-Side Management (DSM) strategy is introduced. This strategy involves two key steps: initially establishing a model to assess the system's energy flexibility, followed by harnessing this flexibility to respond to demand fluctuations. Such an approach facilitates dynamic adaptation to varying energy demands, ensuring optimal resource utilization. The predictive capability of MPC, which accounts for future disturbances including demand forecasts, electricity pricing, and weather conditions, is exploited to improve the system’s responsiveness and operational efficiency.

Experimentation was conducted both in simulations and through the implementation in real systems. These practical applications demonstrated significant savings in energy costs and energy consumption, achieving economical operation. Furthermore, the execution of the proposed two-step demand-side management strategy successfully managed energy demands. This not only underscores the practical effectiveness of the proposed system but also highlights its potential in real-world scenarios.

In summary, this research underscores how the integration of heat pumps, TES systems, and advanced control strategies like MPC can significantly improve energy efficiency, reduce operational costs, and enhance energy flexibility. It highlights the vital role of incorporating sophisticated control mechanisms into sustainable energy systems, aligning with the strategic goals of modern energy policies and advancing the field of sustainable energy management.

MPC
Heat pump
Demand Side Management
Model Predictive Control

http://resolver.tudelft.nl/uuid:6afd744c-906a-430d-a573-4382492596b3

2026-01-25

Student theses

master thesis

© 2024 Weihong Tang