Sustainable Energy Management in Buildings
Optimizing interconnected energy systems reduces carbon emissions in modern buildings.
― 5 min read
Table of Contents
- Seasonal Thermal Energy Storage
- Problem Statement
- System Representation
- Energy Demand and Production
- Electricity Production
- Heat Production
- Storage Systems
- Electrical Storage
- Thermal Storage
- Optimization Approach
- Rolling Horizon Strategy
- Prediction Horizon
- Evaluation of the Prediction Horizon
- Key Findings
- Hybrid Approach
- Comparison of Strategies
- Results Summary
- Conclusion
- Future Directions
- Original Source
- Reference Links
Buildings contribute significantly to carbon emissions, making it essential to create more sustainable structures. One method to achieve this is by improving existing buildings to rely more on clean energy sources. A project at a university campus in France aims to address these issues by using solar panels and energy storage systems for both electricity and heat. The project incorporates a heat pump to connect these systems, enhancing overall efficiency.
Seasonal Thermal Energy Storage
Seasonal thermal energy storage is an effective way to decrease a building's carbon footprint, offering advantages over short-term storage. However, managing daily operations without considering long-term storage can overlook the importance of ensuring enough energy is available for future needs. Conversely, looking too far ahead can lead to inefficiencies, especially since accurate forecasts become challenging over longer periods.
There's substantial research on similar systems, but those typically focus on shorter storage durations. A limited number of studies address longer-term storage. Generally, model predictive control (MPC) is used for managing these systems, incorporating a rolling approach where decisions are made based on both immediate and future conditions.
Problem Statement
The goal is to find the best way to manage energy transfer in a building with interconnected storage systems. To do this, one needs to optimize how both electrical and thermal energy are used over time. This involves managing multiple components, such as solar panels, energy storage, and heat pumps, to ensure that energy demands are met effectively.
System Representation
The system being studied includes various components represented as nodes. Each node has specific energy flow connections to others, allowing for better management of energy production and consumption. Key variables include the power flow between different nodes and the energy stored in the storage systems.
Energy Demand and Production
Energy demand consists of electricity and heating needs. Solar panels produce electricity, while solar thermal systems generate heat. Additionally, energy from air conditioning units can be stored during summer months, contributing to heating needs in the winter.
Electricity Production
The electricity needs of the building are met through solar panels that convert sunlight into energy. By installing numerous panels, the building can significantly reduce its reliance on the grid.
Heat Production
Heat for the building comes from a combination of solar thermal energy and electricity. This heat can be stored for later use, especially during winter months when demand peaks.
Storage Systems
The project involves two main storage types: electrical and thermal. Each has specific limits regarding how much can be charged or discharged, along with efficiency considerations.
Electrical Storage
The electrical storage system is designed to handle fluctuations in energy use. It helps balance supply and demand by storing excess electricity generated during peak sunlight hours for use when the sun isn't shining.
Thermal Storage
Thermal storage allows the building to retain heat produced during warmer months for use in colder months. The operation of this system requires careful planning to ensure that it charges effectively when there is an abundance of energy and discharges when there is a demand for heat.
Optimization Approach
Effective management of these storage systems is critical for minimizing costs and maximizing efficiency. The operation needs to be optimized over time to ensure that both short-term and long-term energy needs are addressed.
Rolling Horizon Strategy
A rolling horizon approach is employed to make daily operational decisions while keeping future needs in mind. This method allows for adjustments based on changing energy demands, weather conditions, and energy production rates.
Prediction Horizon
The prediction horizon is the length of time over which forecasts are made and decisions are based. Finding the optimal length is vital, as it impacts the quality of the operational decisions. An optimal prediction horizon will allow the system to manage energy needs effectively without overshooting production or running into inefficiencies.
Evaluation of the Prediction Horizon
To determine the ideal prediction horizon for the system's operations, several approaches can be tested. Each approach involves evaluating a different length of the prediction horizon to find a suitable balance between short-term needs and long-term goals.
Key Findings
Research indicates that the ideal prediction horizon may shift throughout the year based on energy consumption patterns, solar production, and other factors.
Hybrid Approach
A hybrid strategy combines the benefits of short-term rolling horizons with longer-term goals for thermal storage. By setting target levels for storage based on historical data, the system can adjust daily operations while ensuring enough energy is available for future demands.
Comparison of Strategies
Different operational strategies are assessed to compare their effectiveness and cost. Options evaluated include the rolling horizon approach, a longer predetermined prediction horizon, and the hybrid model. Results will indicate which method better balances operational efficiency and cost savings.
Results Summary
The analysis shows that using a shorter prediction horizon can lead to inefficiencies, while longer horizons may result in higher complexity and running time. The hybrid approach tends to perform well by incorporating historical target levels, effectively bridging short-term and long-term energy management.
Conclusion
Optimizing energy management in buildings with interconnected electrical and thermal storage systems is critical for reducing carbon emissions. This study highlights the importance of developing strategies that consider both short-term needs and future energy demands. By implementing a hybrid approach, the system can operate efficiently, ensuring that energy resources are utilized appropriately throughout the year.
Future Directions
Further research should explore more complex system models and consider varying parameters, such as temperature changes and the degradation of storage systems. Adapting these models could lead to improved operational strategies and greater efficiency in energy management for buildings.
Title: Optimal Operation of a Building with Electricity-Heat Networks and Seasonal Storage
Abstract: As seasonal thermal energy storage emerges as an efficient solution to reduce CO2 emissions of buildings, challenges appear related to its optimal operation. In a system including short-term electricity storage, long-term heat storage, and where electricity and heat networks are connected through a heat pump, it becomes crucial to operate the system on two time scales. Based on real data from a university building, we simulate the operation of such a system over a year, comparing different strategies based on model predictive control (MPC). The first objective of this paper is to determine the minimum prediction horizon to retrieve the results of the full-horizon operation problem with cost minimization. The second objective is to evaluate a method that combines MPC with setting targets on the heat storage level at the end of the prediction horizon, based on historical data. For a prediction horizon of 6 days, the suboptimality gap with the full-horizon results is 4.31%, compared to 11.42% when using a prediction horizon of 42 days and fixing the final level to be equal to the initial level, which is a common approach.
Authors: Eléa Prat, Pierre Pinson, Richard M. Lusby, Riwal Plougonven, Jordi Badosa, Philippe Drobinski
Last Update: Sep 13, 2024
Language: English
Source URL: https://arxiv.org/abs/2409.08721
Source PDF: https://arxiv.org/pdf/2409.08721
Licence: https://creativecommons.org/licenses/by/4.0/
Changes: This summary was created with assistance from AI and may have inaccuracies. For accurate information, please refer to the original source documents linked here.
Thank you to arxiv for use of its open access interoperability.