- Introduction

In this presentation, Hassam Gul discusses the probabilistic flex offers in residential heat pumps while considering uncertain weather forecasts. The study is a collaboration between national and EU projects and offers three significant contributions.

Firstly, the study utilizes the CTSF software to estimate the thermal dynamic coefficient of residential buildings with multitemperatures. This open-source software utilizes building sensor data for training. Secondly, the presentation introduces the use of YouTube STRATEGO as an open-source tool to generate flex offers. This involves assessing the flexibility potential of the building based on minimum and maximum energy consumption patterns of residential heating systems. Finally, the generation of flex offers takes into account the stochastic nature of weather forecasts, responding to weather condition uncertainties. This approach aims to address flexibility potential in residential heating systems caused by differences in indoor temperature thresholds, as well as power and energy flexibility.

The general plan of the suggested approach involves utilizing building sensor data, including indoor temperature, heat consumption, and weather conditions, to estimate thermal dynamic coefficients. The probabilistic flex offers then calculate the flexibility potential of energy systems in response to stochastic variables, with consideration of uncertain weather data. The flexibility potential is evaluated through probability distribution functions for minimal and maximal energy consumption patterns. This study demonstrates the possibilities of open-source software in assessing and leveraging flexibility potential in energy systems.

- Contributions of the Study

The study presents three main contributions. Firstly, the utilization of the ctsm software to calculate the thermal dynamic coefficients of residential buildings, incorporating multitemperatures. This open source software is employed in conjunction with building sensor data to carry out the estimation. Secondly, Youtube stratego, another open source software, is utilized to generate flexible offers by evaluating the flexibility potential of residential heating systems. The software employs the minimum and maximum energy consumption patterns of the heating system to assess flexibility potential. Lastly, the study focuses on generating flexible offers considering the stochastic nature of weather forecasts, leading to the generation of probabilistic flexible offers in response to weather condition uncertainties. Overall, this approach aims to unlock the flexibility potential of energy systems in residential buildings, considering uncertain weather forecasts, and employing open source software for estimation and generation of flexible offers.

- Estimation of Thermal Dynamic Coefficients

In the "Estimation of Thermal Dynamic Coefficients" section of the presentation, the use of the Combined Thermal Simulation Method (CTSM) software was highlighted as a crucial tool for estimating the thermal dynamic coefficients of residential buildings. The CTSM software, an open-source program, was employed to analyze building sensor data, such as indoor temperature, heat consumption, ambient temperature, and solar power radiation. Through this analysis, the CTSM software was able to estimate the thermal dynamic coefficients, including heat resistance and heat capacity of the building.

The three-state thermal dynamics model, which encompasses indoor temperature, low-temperature, and heater temperature, formed the basis of the estimation process. By incorporating these coefficients into the flex offer approach, the study aimed to evaluate the flexibility potential of heating systems in residential buildings. The incorporation of thermal dynamic coefficients into the flex offer approach enables the generation of flex offers, ultimately contributing to the optimization of energy consumption and management in buildings. These flex offers are crucial in responding to the stochastic nature of weather forecasts, ensuring adaptability and efficiency in energy systems.

- Generation of Flex Offers

The Generation of Flex Offers is a crucial aspect of the study, focusing on evaluating the flexibility potential of residential heating systems. The process involves using the minimum and maximum energy consumption patterns of the residential heating system to assess its flexibility potential, thus enabling efficient energy management. Through the utilization of the flex offer approach, the study aims to optimize energy consumption in residential buildings by considering the stochastic nature of weather forecasts.

The flexibility potential of the heating system is determined by the variance between the lower and upper thresholds of indoor temperature. This flexibility is vital for effectively managing energy consumption, with the difference between the maximum and minimum energy consumption representing the energy flexibility. The study integrates the uncertainty of weather data by incorporating a stochastic approach, employing Gaussian noise to the forecasted weather conditions. This method ensures that flex offers account for the probabilistic nature of weather conditions, thereby enhancing the adaptability of residential heating systems to varying weather forecasts. By evaluating cumulative density functions, the study effectively quantifies the flexibility potential of the heating system in response to stochastic variables.

- Probabilistic Felix Offers Considering Weather Forecast Uncertainty

In this study, the focus was on the probabilistic flex offers in residential heat pumps, with an emphasis on uncertain weather forecasts. The study was a joint effort between national and EU projects and contributed to the field in various ways. The first contribution involved the use of the CTSM software to estimate the thermal dynamic coefficients of residential buildings. This open-source software was utilized in conjunction with building sensor data, offering valuable insights into the thermal characteristics of the buildings and their energy consumption patterns.

The second significant contribution was the utilization of YouTube StratEGo as an open-source software to generate flex offers. By analyzing the minimum and maximum energy consumption patterns of residential heating systems, the flexibility potential of the buildings could be evaluated. Finally, the study focused on generating flex offers that considered the stochastic nature of weather forecasts. The probabilistic flex offers were developed as a response to the uncertainty associated with weather conditions, showcasing a proactive approach to managing energy systems in response to unpredictable variables.

- Building Case Study

In the building case study, a 150-square meter digital building with four rooms, including a kitchen, dining room, bathroom, and bedroom, is analyzed. The building features different window dimensions and high-quality concrete envelopes for insulation. The study focuses on thermal dynamic coefficients and their comparison with the actual temperature in the four rooms. The comparison demonstrates high estimation accuracy for three out of the four rooms. However, slight discrepancies are observed in the kitchen, attributed to the baseline heat generated by the refrigeration system and oven, which the thermal dynamic model fails to capture. Additionally, the study showcases the temperature evolution and energy consumption of radiators in the four rooms for both minimum and maximum energy consumption patterns.

Furthermore, the study details the probability distribution functions for minimum and maximum energy consumption, as well as the flexibility potential. The results show that the available energy flexibility is approximately 10 kilowatt, equivalent to 43% of the optimal energy consumption of the building. These findings highlight the impact of stochastic variables on energy systems and emphasize the importance of considering uncertain weather data when evaluating flexibility potential.

- Comparison of Estimated and Actual Room Temperatures

In the "Comparison of Estimated and Actual Room Temperatures" section, the presenter discusses the accuracy of estimating room temperatures using the thermal dynamic coefficients. The analysis reveals that the estimation accuracy for three out of the four rooms is high, demonstrating the effectiveness of the model in capturing temperature dynamics within residential buildings. However, the accuracy for one room, the kitchen, is slightly lower than the others. The presenter attributes this discrepancy to the presence of base heat from appliances such as refrigeration systems and ovens, which the model did not fully capture. This insight highlights the potential influence of internal heat sources on temperature dynamics and the need for comprehensive data integration in the modeling process. Consequently, it underscores the importance of refining the model to account for diverse heat sources and enhance its accuracy across all rooms in the building.

- Evaluation of Energy Consumption Patterns

The evaluation of energy consumption patterns in residential buildings is crucial for understanding the potential for flexibility in energy systems. This study utilizes building sensor data exported to the CTSM software to estimate thermal dynamic coefficients, including heat resistance and heat capacity. The CTSM software, developed by DTU Compute, provides accurate and fast convergence in estimating the thermal dynamics of buildings. With an estimation accuracy of over 90%, the CTSM software proves to be reliable for studying building thermal dynamics.

Furthermore, the study employs the use of probabilistic flex offers to evaluate the flexibility potential of energy systems in response to stochastic variables. By considering uncertain weather data and incorporating stochastic nature of weather forecasts, the flex offers are generated to respond to weather condition uncertainty. The flexibility potential of the heating system in residential buildings stems from the difference between lower and upper thresholds of indoor temperature.

Using probability distribution functions, the study depicts the patterns for minimum and maximum energy consumption, providing insights into available energy flexibility. Based on the table describing average values for energy consumption, the study concludes that the available energy flexibility is approximately 43% of the optimal energy consumption of the building. This approach provides a comprehensive understanding of energy consumption patterns and the potential for flexibility in energy systems.

- Probability Distribution Functions and Energy Flexibility

In this study, the generation of flexible offers considers the stochastic nature of weather forecasts. This is reflected in the generation of probabilistic flex offers in response to the uncertainty in weather conditions. Probability distribution functions for both minimum and maximum energy consumption patterns are depicted in the study, providing a clear visualization of the variability in the energy consumption of residential heating systems. The table presented in the study provides average values for minimum and maximum energy consumption, as well as the available flexibility. The maximum and minimum values of energy consumption were estimated to be around 30 and 20 kilowatt-hours, respectively. As a result, the available energy flexibility was calculated to be approximately 10 kilowatt-hours, constituting 43 percent of the optimal energy consumption of the building. This comprehensive analysis highlights the importance of considering uncertainty and variability in energy consumption patterns, as it allows for the evaluation of the potential flexibility of energy systems in response to stochastic variables.

- Conclusion

In conclusion, this study has demonstrated the potential applications of open-source software in the field of energy systems. The use of the CTSM software has shown high accuracy in estimating the thermal dynamics of buildings, providing researchers with a reliable and efficient tool for studying building thermal dynamics. Additionally, the use of the UPEL software to generate flex offers has paved the way for evaluating the potentials of energy systems in response to stochastic variables. This approach has extended the scope beyond building heating systems, indicating the possibility of developing the Flex offer approach for various energy systems, such as commercial refrigerators, in response to stochastic electricity prices. The findings emphasize the importance of leveraging advanced software tools to further explore and unleash the flexibility potential of energy systems in diverse and dynamic environments. Overall, the use of open-source software in this study has not only uncovered valuable insights but also opened doors for future research and development in the realm of energy systems and thermal dynamics.

Highlight

In this presentation, Hassam Gul Muhammadi discusses the probabilistic flex offers in residential heat pumps considering uncertain weather forecasts. The study aims to generate flex offers considering the stochastic nature of weather forecasts, in response to weather condition uncertainty. The study utilizes building sensor data and CTSF software to estimate thermal dynamic coefficients and generate flex offers, which assess the flexibility potential of heating systems in residential buildings. The study also factors in uncertain weather data by adding a Gaussian noise to the forecasted weather conditions for the next few hours. The findings indicate that the software minimizes energy consumption to reach the lower threshold of indoor temperature and increases energy consumption to reach the upper threshold of the comfort bound.

The presentation emphasizes the significance of open-source software, such as CTSM and UPEL, in studying building thermal dynamics and generating flex offers. It highlights the high accuracy and reliability of the CTSM software in estimating thermal dynamics, taking less than five minutes for each row to converge. Additionally, the flex offer approach is not limited to building heating systems and has the potential to be developed for other applications, such as commercial refrigerators, in response to stochastic electricity prices.

Overall, the findings underscore the importance of considering uncertain variables, such as weather forecasts, in assessing energy system flexibility. The study demonstrates the potential for open-source software to provide opportunities for researchers to develop comprehensive studies in this field.

FAQ

Q: What is the focus of the presentation?

A: The presentation focuses on the probabilistic flex offers in residential heat pumps considering uncertain weather forecasts. It's a joint study between national and EU projects that looks at the potential of energy systems in response to stochastic variables.

Q: What are the key contributions of the study?

A: There are three main contributions. Firstly, the use of the CTSM software to estimate the thermal dynamic coefficients of residential buildings. Secondly, the utilization of YouTube Stratego as an open-source software to generate flex offers based on the minimum and maximum energy consumption patterns of residential heating systems. Lastly, the generation of flexible offers considering the stochastic nature of weather forecasts.

Q: Could you explain the approach used in the study?

A: The study employed building sensor data, including indoor temperature, heat consumption, and weather conditions, to estimate thermal dynamic coefficients using the CTSM software. These coefficients were then incorporated into the flex offer approach. The flexibility potential of heating systems in residential buildings was evaluated based on indoor temperature thresholds and energy consumption patterns.

Q: How was uncertainty in weather data considered in the study?

A: The study considered uncertain weather data by adding Gaussian noise to the forecasted weather conditions for the next few hours. The flex offers were based on probability distribution functions for minimal and maximum energy consumption patterns, taking into account the stochastic nature of weather forecasts.

Q: What are the implications of the study's findings?

A: The study showcased the potential of open-source software, such as CTSM and YouTube Stratego, in analyzing and optimizing energy systems in response to uncertain variables. It also highlighted the significance of considering probabilistic approaches in addressing uncertainty in weather forecasts for better energy management.

Q: Where was the building case study derived from?

A: The building case study was extracted from modernica software, representing a 150-square-meter building with four rooms, including a kitchen, dining room, bathroom, and bedroom. It featured different window dimensions and high-quality concrete for insulation.

Q: What were the main findings regarding the thermal dynamic coefficients and energy consumption patterns?

A: The study found high estimation accuracy for thermal dynamics in three out of four rooms, with the kitchen showing slightly lower accuracy due to the presence of baseline heat from refrigeration systems and ovens that wasn't fully captured by the model.

Q: How were the energy consumption patterns and flexibility potential presented in the study?

A: The study presented the minimum and maximum energy consumption patterns and their associated probability distribution functions, along with the available flexibility based on the average values for energy consumption. The findings indicated a potential energy flexibility of around 43% of the optimal energy consumption of the building.

Q: What are the future implications of the study's outcomes?

A: The study underscores the potential for further research and development in analyzing and optimizing energy systems, not limited to building heating systems. The approach can be extended to explore flexibility potential in various contexts, such as commercial refrigerators responding to stochastic electricity prices.

Q: Where can interested parties access the open-source software used in the study?

A: The CTSM and YouTube Stratego software, which were utilized in the study, are publicly available on their respective websites, providing significant opportunities for researchers to explore and expand their studies in the realm of building thermal dynamics and energy system flexibility.

Conclusion

In conclusion, this presentation discusses the study on probabilistic flex offers in residential heat pumps considering uncertain weather forecasts. The study presents the usage of CTSM software to estimate the thermal dynamic coefficients of residential buildings, as well as the utilization of UPEL Stratego to generate flex offers. The presentation emphasizes the significance of considering stochastic weather data in generating flex offers and highlights the potential applications of the flex offer approach in diverse energy systems beyond building heating systems. The use of open-source software, including CTSM and UPEL, is promoted for further research and development in studying building thermal dynamics and evaluating energy system potentials.