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Model Predictive Control-based Energy Management System for Enhancing Home Energy Resiliency During Extreme Weather Outages

Core Concepts
An intelligent Model Predictive Control (MPC)-based controller is proposed to manage the trade-off between competing requirements and provide energy resiliency to a house equipped with rooftop solar PV and battery storage during unplanned outages caused by extreme weather events, while maintaining thermal comfort.
The content discusses the development of an intelligent home energy management system (HEMS) based on Model Predictive Control (MPC) to provide energy resiliency to a house during unplanned power outages caused by extreme weather events. The key highlights are: The house is equipped with a rooftop solar PV system and a battery storage system to provide an energy-resilient solution during grid outages. The HEMS needs to manage the trade-off between competing requirements, such as maintaining thermal comfort and servicing critical and non-critical loads, in the face of finite energy supply. The proposed MPC-based controller is compared with a Baseline controller (representing a commercially installed PV-battery system without intelligent control) and a Rule-Based controller (with some intelligence) based on three resiliency metrics. Extensive simulations are performed for various scenarios involving different PV-battery system sizes and air conditioning (AC) startup power requirements, using real weather and load demand data from a house in Florida after Hurricane Irma in 2017. The results show that the MPC controller performs better than the other controllers, especially in more energy-constrained scenarios (smaller PV-battery size, larger AC startup power requirement), in providing both thermal comfort and balanced servicing of critical and non-critical loads. The study demonstrates that a controller based on intelligent planning and forecast information utilization is more reliable than reactive rule-based controllers in providing home energy resiliency against extreme weather events.
The PV system has a rated power of 0.325 kW per module, and the rooftop PV system size is αpv × 10.075 kW, where αpv ∈ {0.25, 0.5, 0.75, 1}. The battery system is a single Tesla Powerwall with a capacity of αbat × 13.5 kWh, where αbat ∈ {0.25, 0.5, 0.75, 1}. The AC system has a rated power of 3 kW, and the startup power is (1-αV) × αI × 3 kW, where αV = 0.3 and αI ∈ {3, 4, 5, 6, 7, 8}.

Deeper Inquiries

How can the performance of the MPC controller be further improved, especially in terms of providing thermal comfort, by tuning the weighting factors in the cost function

To enhance the performance of the Model Predictive Control (MPC) controller in terms of providing thermal comfort, tuning the weighting factors in the cost function is crucial. By adjusting the weighting factors, the controller's behavior can be tailored to prioritize thermal comfort while still managing critical and other loads efficiently. Here are some strategies to improve thermal comfort through cost function tuning: Increase Weight on Thermal Comfort: By assigning a higher weight to the term that minimizes the temperature slack variable (ζh), the controller will prioritize maintaining thermal comfort within the desired bounds. This adjustment ensures that the house temperature is regulated effectively, even in energy-constrained scenarios. Dynamic Weighting: Implementing dynamic weighting factors that adapt based on real-time conditions such as weather forecasts, battery state-of-charge, and load demand can optimize thermal comfort. For instance, increasing the weight on thermal comfort during extreme weather events or when the battery is low can improve overall performance. Balancing Trade-offs: Fine-tuning the weights of different objectives in the cost function can help strike a balance between thermal comfort, critical load resiliency, and energy efficiency. Finding the optimal trade-off based on the specific requirements of the house and the available resources is key to enhancing performance. Sensitivity Analysis: Conducting sensitivity analysis on the impact of varying weighting factors can provide insights into the controller's response to different scenarios. This analysis helps in understanding the controller's behavior under different conditions and optimizing the weights accordingly. By iteratively adjusting and optimizing the weighting factors in the cost function, the MPC controller can be fine-tuned to prioritize thermal comfort effectively while ensuring the efficient management of critical and other loads.

What would be the impact of incorporating forecast uncertainty into the MPC formulation, and how could this affect the controller's performance compared to the deterministic MPC approach presented

Incorporating forecast uncertainty into the MPC formulation can have a significant impact on the controller's performance compared to the deterministic approach presented. Here are some key considerations and potential effects of incorporating forecast uncertainty: Robustness to Uncertainty: By accounting for forecast uncertainty in variables such as solar irradiance, load demand, and weather conditions, the MPC controller can become more robust to variations and inaccuracies in predictions. This robustness can help mitigate the impact of forecast errors on decision-making. Stochastic Optimization: Transitioning from deterministic to stochastic optimization in the MPC formulation allows the controller to consider a range of possible scenarios and make decisions that are robust across different forecast outcomes. This approach enables the controller to adapt to changing conditions and uncertainties more effectively. Risk Management: Incorporating forecast uncertainty enables the MPC controller to manage risks associated with inaccurate predictions. By quantifying and addressing uncertainty in the optimization process, the controller can make decisions that minimize the potential negative impact of forecast errors on system performance. Adaptive Strategies: With forecast uncertainty considerations, the MPC controller can dynamically adjust its strategies based on the level of uncertainty present. This adaptive capability allows the controller to react proactively to changing forecast conditions and optimize its decisions in real-time. Overall, incorporating forecast uncertainty into the MPC formulation enhances the controller's adaptability, robustness, and risk management capabilities, leading to improved performance in handling unforeseen variations in weather conditions and load demands.

How can the proposed home energy management framework be extended to a community-level distributed control system to provide energy resilience for a group of heterogeneous houses during extreme weather events

Extending the proposed home energy management framework to a community-level distributed control system for providing energy resilience to a group of heterogeneous houses during extreme weather events requires a scalable and adaptive approach. Here are key steps to extend the framework: Aggregated Forecasting: Implement a centralized forecasting system that aggregates weather predictions, load profiles, and energy generation data from individual houses in the community. This aggregated forecasting will provide a comprehensive view of energy supply and demand dynamics across the community. Hierarchical Control Architecture: Develop a hierarchical control architecture that includes local controllers at individual houses and a centralized coordinator for optimizing energy distribution and storage at the community level. This architecture enables coordinated decision-making while allowing for local autonomy. Peer-to-Peer Energy Trading: Introduce peer-to-peer energy trading mechanisms that enable houses with surplus energy to share it with those in need. Smart contracts and blockchain technology can facilitate secure and transparent energy transactions within the community. Dynamic Demand Response: Implement dynamic demand response strategies that adjust energy consumption based on real-time conditions and pricing signals. By incentivizing load shifting and flexibility, the community can optimize energy usage and enhance overall resilience. Resilience Planning: Develop contingency plans and resilience strategies at the community level to address potential disruptions and emergencies. This includes backup power systems, coordinated response protocols, and communication channels for effective crisis management. Data Sharing and Interoperability: Ensure interoperability and data sharing standards among different energy systems and devices in the community. This promotes seamless integration, efficient communication, and coordinated control for improved energy resilience. By integrating these elements into a community-level distributed control system, the proposed framework can enhance energy resilience, optimize resource utilization, and foster collaboration among heterogeneous houses during extreme weather events.