Optimizing Electro-Thermal Microgrid Operations with Predictive Control and Variable Flow Temperatures

Core Concepts

The core message of this article is that by explicitly modeling the thermal dynamics of a district heating grid and utilizing variable flow temperatures, a model predictive control scheme can increase the operational flexibility and efficiency of an electro-thermal microgrid.

Abstract

The article presents a comprehensive modeling approach for an electro-thermal microgrid (ETMG) that combines an electrical layer and a thermal layer, interconnected via heat pumps.
For the thermal layer, the authors derive a discrete-time state-space model that captures the temperature dynamics of the district heating grid (DHG) using 1D Euler equations. This allows the model to explicitly account for the inherent storage capacities and flexibility potential of the thermal network.
The electrical layer is modeled using a DC power flow approximation, and the states of charge of the electrical storage system are also included in the overall ETMG model.
Based on this ETMG model, the authors formulate a model predictive control (MPC) problem that aims to minimize both economic and efficiency-based operating costs. The MPC scheme can exploit the flexibility provided by the variable flow temperatures in the thermal network and the electrical storage to optimize the overall ETMG operation.
The performance of the proposed MPC approach is demonstrated in a case study, which shows that by allowing variable flow temperatures, the MPC can reduce the power demand from the main grid, decrease the peak power of the electrical storage and heat pumps, and improve the overall operational efficiency compared to a conventional supply temperature-based control strategy.

Stats

The case study demonstrates the following key figures:

1.23% reduction in power demand from the main grid
21.90% decrease in peak power of the electrical storage system
16.10% decrease in peak power of the heat pump
16.65% reduction in the used electrical storage capacity
32.16% decrease in the variance of the heat pump power

Quotes

"By admitting variable flow temperatures, the proposed MPC II can exploit the inherent thermal storage capacities of the DHG to reduce the economic and efficiency-related costs."
"MPC II furthermore enables a preheating of the supply pipe, i.e., an increase in T^II_e,1(k), before the thermal demand Q̇_d,2(k) rises (for k < 17). This illustrates how the use of a dynamic DHG model in combination with the predictive nature of MPC can anticipate future peak loads."

Key Insights Distilled From

A Predictive Operation Controller for an Electro-Thermal Microgrid Utilizing Variable Flow Temperatures

by Max Rose,Chr... at arxiv.org 04-03-2024

https://arxiv.org/pdf/2212.07078.pdf

Deeper Inquiries

How can the proposed MPC approach be extended to handle uncertainties in power and heat demand and supply?

Incorporating uncertainties in power and heat demand and supply into the MPC approach can enhance its robustness and effectiveness in real-world applications. One way to handle uncertainties is by integrating probabilistic forecasting models into the MPC framework. By utilizing historical data and advanced forecasting techniques, such as stochastic optimization or scenario-based approaches, the MPC controller can generate multiple scenarios of possible future states based on different demand and supply outcomes. These scenarios can then be used to formulate a robust optimization problem that considers the uncertainties in the predictions.
Furthermore, the MPC algorithm can be extended to include adaptive control strategies that dynamically adjust the control actions based on real-time feedback and sensor data. By continuously updating the model predictions with new information, the controller can adapt to changing conditions and uncertainties in the system. This adaptive approach allows the MPC to react in a more agile manner to unexpected variations in power and heat demand and supply.
Another method to handle uncertainties is through the use of constraint tightening techniques. By incorporating additional safety margins or constraints in the optimization problem, the MPC controller can account for uncertainties and ensure that the system operates within safe limits even under worst-case scenarios. This proactive approach helps mitigate risks associated with uncertain conditions and prevents potential system failures.
Overall, by integrating probabilistic forecasting, adaptive control strategies, and constraint tightening techniques, the proposed MPC approach can be extended to effectively handle uncertainties in power and heat demand and supply, ensuring reliable and optimal operation of the electro-thermal microgrid.

What are the potential drawbacks or limitations of allowing variable flow temperatures in district heating networks, and how can they be addressed?

While allowing variable flow temperatures in district heating networks can offer increased operational flexibility and efficiency, there are potential drawbacks and limitations that need to be considered and addressed:

System Complexity: Managing variable flow temperatures can increase the complexity of the control system and require more sophisticated modeling and optimization techniques. This complexity may lead to challenges in system integration and control.

Energy Losses: Variable flow temperatures can result in higher energy losses due to increased heat dissipation in the network. This can reduce the overall efficiency of the district heating system and lead to higher operational costs.

Temperature Fluctuations: Fluctuations in flow temperatures can impact the comfort and reliability of the heating system for end-users. Sudden changes in temperature may result in discomfort or uneven heating distribution.

Maintenance and Monitoring: Monitoring and maintaining a system with variable flow temperatures may require additional sensors and control mechanisms. Regular maintenance and calibration of equipment are essential to ensure optimal performance.

To address these limitations, several strategies can be implemented:

Advanced Control Algorithms: Utilize advanced control algorithms, such as model predictive control, to optimize the operation of the district heating network while considering variable flow temperatures and minimizing energy losses.

Efficient Heat Exchangers: Implement high-efficiency heat exchangers and insulation to reduce energy losses and improve the overall efficiency of the system.

User Feedback and Monitoring: Incorporate user feedback and real-time monitoring to adjust flow temperatures based on actual demand and comfort requirements, ensuring a balance between energy efficiency and user satisfaction.

Regular Maintenance: Establish a proactive maintenance schedule to ensure the proper functioning of equipment and prevent system failures. Regular inspections and calibration of sensors and control devices are essential for system reliability.

By addressing these potential drawbacks and limitations through advanced control strategies, efficient equipment, user feedback mechanisms, and proactive maintenance, the benefits of allowing variable flow temperatures in district heating networks can be maximized while mitigating associated challenges.

What other types of thermal storage, beyond the inherent storage in the district heating grid, could be integrated into the ETMG model and control framework to further increase the operational flexibility?

In addition to the inherent storage capacities within the district heating grid, several other types of thermal storage technologies can be integrated into the Electro-Thermal Microgrid (ETMG) model and control framework to enhance operational flexibility:

Seasonal Thermal Energy Storage (STES): STES systems store excess thermal energy during warmer seasons for later use in colder months. By incorporating STES technologies, the ETMG can optimize energy utilization and reduce peak demand on the grid.

Aquifer Thermal Energy Storage (ATES): ATES systems utilize underground aquifers to store excess thermal energy for heating and cooling purposes. Integrating ATES into the ETMG can provide a sustainable and efficient way to store and retrieve thermal energy.

Ice Thermal Energy Storage (ITES): ITES systems use ice as a thermal storage medium to shift cooling loads to off-peak hours. By incorporating ITES technologies, the ETMG can optimize cooling operations and reduce energy costs.

Phase Change Materials (PCMs): PCMs store and release thermal energy during phase transitions, providing a compact and efficient storage solution. Integrating PCMs into the ETMG can enhance thermal storage capacity and improve system flexibility.

Thermal Batteries: Thermal batteries store thermal energy in chemical reactions and release it when needed. By incorporating thermal batteries, the ETMG can store excess energy and utilize it during peak demand periods.

Cryogenic Energy Storage: Cryogenic energy storage systems use liquefied gases to store thermal energy. Integrating cryogenic storage technologies into the ETMG can provide a high-density and efficient storage solution for thermal energy.

By integrating these diverse thermal storage technologies into the ETMG model and control framework, the operational flexibility and efficiency of the system can be significantly enhanced. Each type of thermal storage offers unique advantages and can be tailored to meet specific energy storage requirements and operational objectives within the electro-thermal microgrid.