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A. Meyer
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Analysing Dutch Microgrid Performance
A study on the performance of Dutch microgrids under different testing conditions
Net congestion is becoming a more pressing issue in the Netherlands, partly due to the increased penetration of renewable energy generation sources in the Dutch energy mix. The implementation of residential microgrids into the hundreds of planned Energy Hubs in the Netherlands might alleviate the pressure on the main utility grid through local production, consumption and storage. However, when PV systems have period of low production and the BESSs are depleted, the microgrids would all rely on the main utility grid for electricity supply, potentially only extending the limits needed to be reached by the main grid. Conversely, if all microgrids would have a high PV production and storage would be full, electricity might be exported at such a high rate that the main utility grid is challenged heavily. These situations form the basis of the research objective of this thesis.
A literature review has indicated the need for further research on Dutch residential microgrids, while using historical weather condition data for prolonged periods of time. In addition a clear quantitative definition for the performance of the microgrid. Combining the research objective with the knowledge gaps, the following main research question has been formulated:
How does the performance of residential microgrids in the Netherlands vary under different testing conditions?
By varying load patterns, weather condition data longevity, BESS types, and microgrid sizes throughout the various sub-questions, different scenarios have been developed to assess the performance of the microgrid on different performance metrics. The metrics that different scenarios are scored on are: cumulative deficit, import period duration, import and export power, and import and export ramp rates. Taking on the modelling approach has allowed for answering the sub-questions, filling the knowledge gaps, and achieving the research objective. Using the python library GSEE, PV system production has been estimated and compared against different load patterns (household load patterns with gas heating versus household load patterns with heat pump). Weather data for different time periods have been obtained from the European Commission’s PVGIS, SARAH-3, and ERA5 datasets.
Simulating the different scenarios has provided insights into the effects of different testing conditions on the performance of the microgrid. The factor with the highest impact on the performance was the load pattern, with the addition of a high-impact load in the form of a heat pump to be the scenarios requiring the highest capacity from the main utility grid. BESS type generally also impact the results, with community batteries proving to be successful in reducing the peak import power and ramp rate when compared to home batteries that are used for individual households. Microgrid size only impacts the performance results in a minimal matter.
Analysing the system over a 42-year period has proven to be highly useful in redetermining the upper limits required to be handled by the microgrid and main utility grid, when compared to the singular year (TMY) scenarios. In all scenarios and performance metrics, analysing the system over this prolonged period of time has given new insights into system boundaries. It would, therefore, be highly recommended for future studies on the performance of (Dutch residential) microgrids to take this multidecade perspective and prevent underestimation of the limits the microgrid system is subjected to.
These results have largely been validated by existing academic literature, but are still subject to numerous limitations. This limitations include, but are not limited to, missing values in datasets, low temporal resolution, and the exclusion of the role of monetary costs. Further recommendations would be to focus government policies and subsidies mainly on the demand of Dutch households, as electrification in Dutch households is increasing rapidly. High-impact loads, such as a heat pump, drastically increases the maximum burden the main utility grid has to carry and with the slow development of grid expansion, the electricity grid can not keep up with the additional load. In addition, the Dutch home battery market is still in its infancy stage, but is developing rapidly. To bear the fruits of community batteries, the Dutch government is advised to act quickly and start with the implementation of community atteries in the planned Energy Hubs.
Understanding the interplay between different microgrid components and methods for analysis is vital for successful implementation of the Dutch Energy Hubs and alleviation of the main utility grid. This forms one of the largest challenges of the upcoming decade in the Dutch energy sector. For a full understanding, the results need to be placed in the context of the socio-technical environment. Dutch government instances will need to adjust regulations to incentivise dynamic pricing structures, rethink the cost allocations to allow a fair distribution of costs among households, and setting up a regulatory framework for the emerging Energy Hubs and communities, while also account for behavioural and cultural barriers to smooth implementation of microgrids into the Dutch Energy Hubs. As these Energy Hubs are still in the infancy stage, the Dutch government still has the opportunity to guide the standards, policies, and market mechanisms that will underpin scalable, community-driven Energy Hubs - ensuring they enhance grid stability, foster public trust, and accelerate the transition to a low-carbon energy system. ...
A literature review has indicated the need for further research on Dutch residential microgrids, while using historical weather condition data for prolonged periods of time. In addition a clear quantitative definition for the performance of the microgrid. Combining the research objective with the knowledge gaps, the following main research question has been formulated:
How does the performance of residential microgrids in the Netherlands vary under different testing conditions?
By varying load patterns, weather condition data longevity, BESS types, and microgrid sizes throughout the various sub-questions, different scenarios have been developed to assess the performance of the microgrid on different performance metrics. The metrics that different scenarios are scored on are: cumulative deficit, import period duration, import and export power, and import and export ramp rates. Taking on the modelling approach has allowed for answering the sub-questions, filling the knowledge gaps, and achieving the research objective. Using the python library GSEE, PV system production has been estimated and compared against different load patterns (household load patterns with gas heating versus household load patterns with heat pump). Weather data for different time periods have been obtained from the European Commission’s PVGIS, SARAH-3, and ERA5 datasets.
Simulating the different scenarios has provided insights into the effects of different testing conditions on the performance of the microgrid. The factor with the highest impact on the performance was the load pattern, with the addition of a high-impact load in the form of a heat pump to be the scenarios requiring the highest capacity from the main utility grid. BESS type generally also impact the results, with community batteries proving to be successful in reducing the peak import power and ramp rate when compared to home batteries that are used for individual households. Microgrid size only impacts the performance results in a minimal matter.
Analysing the system over a 42-year period has proven to be highly useful in redetermining the upper limits required to be handled by the microgrid and main utility grid, when compared to the singular year (TMY) scenarios. In all scenarios and performance metrics, analysing the system over this prolonged period of time has given new insights into system boundaries. It would, therefore, be highly recommended for future studies on the performance of (Dutch residential) microgrids to take this multidecade perspective and prevent underestimation of the limits the microgrid system is subjected to.
These results have largely been validated by existing academic literature, but are still subject to numerous limitations. This limitations include, but are not limited to, missing values in datasets, low temporal resolution, and the exclusion of the role of monetary costs. Further recommendations would be to focus government policies and subsidies mainly on the demand of Dutch households, as electrification in Dutch households is increasing rapidly. High-impact loads, such as a heat pump, drastically increases the maximum burden the main utility grid has to carry and with the slow development of grid expansion, the electricity grid can not keep up with the additional load. In addition, the Dutch home battery market is still in its infancy stage, but is developing rapidly. To bear the fruits of community batteries, the Dutch government is advised to act quickly and start with the implementation of community atteries in the planned Energy Hubs.
Understanding the interplay between different microgrid components and methods for analysis is vital for successful implementation of the Dutch Energy Hubs and alleviation of the main utility grid. This forms one of the largest challenges of the upcoming decade in the Dutch energy sector. For a full understanding, the results need to be placed in the context of the socio-technical environment. Dutch government instances will need to adjust regulations to incentivise dynamic pricing structures, rethink the cost allocations to allow a fair distribution of costs among households, and setting up a regulatory framework for the emerging Energy Hubs and communities, while also account for behavioural and cultural barriers to smooth implementation of microgrids into the Dutch Energy Hubs. As these Energy Hubs are still in the infancy stage, the Dutch government still has the opportunity to guide the standards, policies, and market mechanisms that will underpin scalable, community-driven Energy Hubs - ensuring they enhance grid stability, foster public trust, and accelerate the transition to a low-carbon energy system. ...
Net congestion is becoming a more pressing issue in the Netherlands, partly due to the increased penetration of renewable energy generation sources in the Dutch energy mix. The implementation of residential microgrids into the hundreds of planned Energy Hubs in the Netherlands might alleviate the pressure on the main utility grid through local production, consumption and storage. However, when PV systems have period of low production and the BESSs are depleted, the microgrids would all rely on the main utility grid for electricity supply, potentially only extending the limits needed to be reached by the main grid. Conversely, if all microgrids would have a high PV production and storage would be full, electricity might be exported at such a high rate that the main utility grid is challenged heavily. These situations form the basis of the research objective of this thesis.
A literature review has indicated the need for further research on Dutch residential microgrids, while using historical weather condition data for prolonged periods of time. In addition a clear quantitative definition for the performance of the microgrid. Combining the research objective with the knowledge gaps, the following main research question has been formulated:
How does the performance of residential microgrids in the Netherlands vary under different testing conditions?
By varying load patterns, weather condition data longevity, BESS types, and microgrid sizes throughout the various sub-questions, different scenarios have been developed to assess the performance of the microgrid on different performance metrics. The metrics that different scenarios are scored on are: cumulative deficit, import period duration, import and export power, and import and export ramp rates. Taking on the modelling approach has allowed for answering the sub-questions, filling the knowledge gaps, and achieving the research objective. Using the python library GSEE, PV system production has been estimated and compared against different load patterns (household load patterns with gas heating versus household load patterns with heat pump). Weather data for different time periods have been obtained from the European Commission’s PVGIS, SARAH-3, and ERA5 datasets.
Simulating the different scenarios has provided insights into the effects of different testing conditions on the performance of the microgrid. The factor with the highest impact on the performance was the load pattern, with the addition of a high-impact load in the form of a heat pump to be the scenarios requiring the highest capacity from the main utility grid. BESS type generally also impact the results, with community batteries proving to be successful in reducing the peak import power and ramp rate when compared to home batteries that are used for individual households. Microgrid size only impacts the performance results in a minimal matter.
Analysing the system over a 42-year period has proven to be highly useful in redetermining the upper limits required to be handled by the microgrid and main utility grid, when compared to the singular year (TMY) scenarios. In all scenarios and performance metrics, analysing the system over this prolonged period of time has given new insights into system boundaries. It would, therefore, be highly recommended for future studies on the performance of (Dutch residential) microgrids to take this multidecade perspective and prevent underestimation of the limits the microgrid system is subjected to.
These results have largely been validated by existing academic literature, but are still subject to numerous limitations. This limitations include, but are not limited to, missing values in datasets, low temporal resolution, and the exclusion of the role of monetary costs. Further recommendations would be to focus government policies and subsidies mainly on the demand of Dutch households, as electrification in Dutch households is increasing rapidly. High-impact loads, such as a heat pump, drastically increases the maximum burden the main utility grid has to carry and with the slow development of grid expansion, the electricity grid can not keep up with the additional load. In addition, the Dutch home battery market is still in its infancy stage, but is developing rapidly. To bear the fruits of community batteries, the Dutch government is advised to act quickly and start with the implementation of community atteries in the planned Energy Hubs.
Understanding the interplay between different microgrid components and methods for analysis is vital for successful implementation of the Dutch Energy Hubs and alleviation of the main utility grid. This forms one of the largest challenges of the upcoming decade in the Dutch energy sector. For a full understanding, the results need to be placed in the context of the socio-technical environment. Dutch government instances will need to adjust regulations to incentivise dynamic pricing structures, rethink the cost allocations to allow a fair distribution of costs among households, and setting up a regulatory framework for the emerging Energy Hubs and communities, while also account for behavioural and cultural barriers to smooth implementation of microgrids into the Dutch Energy Hubs. As these Energy Hubs are still in the infancy stage, the Dutch government still has the opportunity to guide the standards, policies, and market mechanisms that will underpin scalable, community-driven Energy Hubs - ensuring they enhance grid stability, foster public trust, and accelerate the transition to a low-carbon energy system.
A literature review has indicated the need for further research on Dutch residential microgrids, while using historical weather condition data for prolonged periods of time. In addition a clear quantitative definition for the performance of the microgrid. Combining the research objective with the knowledge gaps, the following main research question has been formulated:
How does the performance of residential microgrids in the Netherlands vary under different testing conditions?
By varying load patterns, weather condition data longevity, BESS types, and microgrid sizes throughout the various sub-questions, different scenarios have been developed to assess the performance of the microgrid on different performance metrics. The metrics that different scenarios are scored on are: cumulative deficit, import period duration, import and export power, and import and export ramp rates. Taking on the modelling approach has allowed for answering the sub-questions, filling the knowledge gaps, and achieving the research objective. Using the python library GSEE, PV system production has been estimated and compared against different load patterns (household load patterns with gas heating versus household load patterns with heat pump). Weather data for different time periods have been obtained from the European Commission’s PVGIS, SARAH-3, and ERA5 datasets.
Simulating the different scenarios has provided insights into the effects of different testing conditions on the performance of the microgrid. The factor with the highest impact on the performance was the load pattern, with the addition of a high-impact load in the form of a heat pump to be the scenarios requiring the highest capacity from the main utility grid. BESS type generally also impact the results, with community batteries proving to be successful in reducing the peak import power and ramp rate when compared to home batteries that are used for individual households. Microgrid size only impacts the performance results in a minimal matter.
Analysing the system over a 42-year period has proven to be highly useful in redetermining the upper limits required to be handled by the microgrid and main utility grid, when compared to the singular year (TMY) scenarios. In all scenarios and performance metrics, analysing the system over this prolonged period of time has given new insights into system boundaries. It would, therefore, be highly recommended for future studies on the performance of (Dutch residential) microgrids to take this multidecade perspective and prevent underestimation of the limits the microgrid system is subjected to.
These results have largely been validated by existing academic literature, but are still subject to numerous limitations. This limitations include, but are not limited to, missing values in datasets, low temporal resolution, and the exclusion of the role of monetary costs. Further recommendations would be to focus government policies and subsidies mainly on the demand of Dutch households, as electrification in Dutch households is increasing rapidly. High-impact loads, such as a heat pump, drastically increases the maximum burden the main utility grid has to carry and with the slow development of grid expansion, the electricity grid can not keep up with the additional load. In addition, the Dutch home battery market is still in its infancy stage, but is developing rapidly. To bear the fruits of community batteries, the Dutch government is advised to act quickly and start with the implementation of community atteries in the planned Energy Hubs.
Understanding the interplay between different microgrid components and methods for analysis is vital for successful implementation of the Dutch Energy Hubs and alleviation of the main utility grid. This forms one of the largest challenges of the upcoming decade in the Dutch energy sector. For a full understanding, the results need to be placed in the context of the socio-technical environment. Dutch government instances will need to adjust regulations to incentivise dynamic pricing structures, rethink the cost allocations to allow a fair distribution of costs among households, and setting up a regulatory framework for the emerging Energy Hubs and communities, while also account for behavioural and cultural barriers to smooth implementation of microgrids into the Dutch Energy Hubs. As these Energy Hubs are still in the infancy stage, the Dutch government still has the opportunity to guide the standards, policies, and market mechanisms that will underpin scalable, community-driven Energy Hubs - ensuring they enhance grid stability, foster public trust, and accelerate the transition to a low-carbon energy system.
Climate change and wind energy are closely intertwined, with wind energy playing a vital role in reducing greenhouse gas emissions that drive climate change. However, climate change also poses a potential threat to the wind energy industry due to possible reductions in wind resources in key regions, though it may enhance resources in other areas. This study evaluates the impact of climate change on wind energy by using climate projections from Global Climate Models (GCMs). To improve resolution and capture finer details, downscaling methods—both statistical and dynamical—are employed.
This research investigates mean wind speed variations at nine global sites using non-downscaled CMIP6 (Coupled model intercomparison project phase 6) GCMs, statistically downscaled CMIP5/6 GCMs, and dynamically downscaled CMIP5 GCMs from CORDEX, under the climate change scenarios RCP4.5/SSP2-4.5 and RCP8.5/SSP5-8.5. It assesses impacts on wind resource availability, annual energy yield, sensitivity factor, and capacity factor, comparing these to historical data. The study also evaluates revenue from Annual Energy Production (AEP) and compares historical projections from GCMs with reanalysis data.
Findings indicate that non-downscaled CMIP6 GCMs and statistically downscaled GCMs exhibit similar trends in predicting wind speed decline at most sites, suggesting the effectiveness of non-downscaled CMIP6 GCMs for this purpose. CORDEX models reveal a significant influence of GCMs on mean wind speed projections, highlighting the need to include multiple GCMs for reliable analysis. The study observes larger decreases in AEP and capacity factors at onshore sites compared to offshore sites, attributed to complex terrain and higher sensitivity factors at the former.
Comparisons with reanalysis data show higher percent bias, mean absolute percentage error (MAPE), and lower correlation for onshore sites, due to their complex terrain. Additionally, no significant correlation was found between the distance of GCM grid points from reanalysis sites and MAPE. These discrepancies arise from differences in dataset resolution, topographical representation, and data assimilation processes, emphasizing the need for careful consideration when comparing GCM projections with reanalysis data.
...
This research investigates mean wind speed variations at nine global sites using non-downscaled CMIP6 (Coupled model intercomparison project phase 6) GCMs, statistically downscaled CMIP5/6 GCMs, and dynamically downscaled CMIP5 GCMs from CORDEX, under the climate change scenarios RCP4.5/SSP2-4.5 and RCP8.5/SSP5-8.5. It assesses impacts on wind resource availability, annual energy yield, sensitivity factor, and capacity factor, comparing these to historical data. The study also evaluates revenue from Annual Energy Production (AEP) and compares historical projections from GCMs with reanalysis data.
Findings indicate that non-downscaled CMIP6 GCMs and statistically downscaled GCMs exhibit similar trends in predicting wind speed decline at most sites, suggesting the effectiveness of non-downscaled CMIP6 GCMs for this purpose. CORDEX models reveal a significant influence of GCMs on mean wind speed projections, highlighting the need to include multiple GCMs for reliable analysis. The study observes larger decreases in AEP and capacity factors at onshore sites compared to offshore sites, attributed to complex terrain and higher sensitivity factors at the former.
Comparisons with reanalysis data show higher percent bias, mean absolute percentage error (MAPE), and lower correlation for onshore sites, due to their complex terrain. Additionally, no significant correlation was found between the distance of GCM grid points from reanalysis sites and MAPE. These discrepancies arise from differences in dataset resolution, topographical representation, and data assimilation processes, emphasizing the need for careful consideration when comparing GCM projections with reanalysis data.
...
Climate change and wind energy are closely intertwined, with wind energy playing a vital role in reducing greenhouse gas emissions that drive climate change. However, climate change also poses a potential threat to the wind energy industry due to possible reductions in wind resources in key regions, though it may enhance resources in other areas. This study evaluates the impact of climate change on wind energy by using climate projections from Global Climate Models (GCMs). To improve resolution and capture finer details, downscaling methods—both statistical and dynamical—are employed.
This research investigates mean wind speed variations at nine global sites using non-downscaled CMIP6 (Coupled model intercomparison project phase 6) GCMs, statistically downscaled CMIP5/6 GCMs, and dynamically downscaled CMIP5 GCMs from CORDEX, under the climate change scenarios RCP4.5/SSP2-4.5 and RCP8.5/SSP5-8.5. It assesses impacts on wind resource availability, annual energy yield, sensitivity factor, and capacity factor, comparing these to historical data. The study also evaluates revenue from Annual Energy Production (AEP) and compares historical projections from GCMs with reanalysis data.
Findings indicate that non-downscaled CMIP6 GCMs and statistically downscaled GCMs exhibit similar trends in predicting wind speed decline at most sites, suggesting the effectiveness of non-downscaled CMIP6 GCMs for this purpose. CORDEX models reveal a significant influence of GCMs on mean wind speed projections, highlighting the need to include multiple GCMs for reliable analysis. The study observes larger decreases in AEP and capacity factors at onshore sites compared to offshore sites, attributed to complex terrain and higher sensitivity factors at the former.
Comparisons with reanalysis data show higher percent bias, mean absolute percentage error (MAPE), and lower correlation for onshore sites, due to their complex terrain. Additionally, no significant correlation was found between the distance of GCM grid points from reanalysis sites and MAPE. These discrepancies arise from differences in dataset resolution, topographical representation, and data assimilation processes, emphasizing the need for careful consideration when comparing GCM projections with reanalysis data.
This research investigates mean wind speed variations at nine global sites using non-downscaled CMIP6 (Coupled model intercomparison project phase 6) GCMs, statistically downscaled CMIP5/6 GCMs, and dynamically downscaled CMIP5 GCMs from CORDEX, under the climate change scenarios RCP4.5/SSP2-4.5 and RCP8.5/SSP5-8.5. It assesses impacts on wind resource availability, annual energy yield, sensitivity factor, and capacity factor, comparing these to historical data. The study also evaluates revenue from Annual Energy Production (AEP) and compares historical projections from GCMs with reanalysis data.
Findings indicate that non-downscaled CMIP6 GCMs and statistically downscaled GCMs exhibit similar trends in predicting wind speed decline at most sites, suggesting the effectiveness of non-downscaled CMIP6 GCMs for this purpose. CORDEX models reveal a significant influence of GCMs on mean wind speed projections, highlighting the need to include multiple GCMs for reliable analysis. The study observes larger decreases in AEP and capacity factors at onshore sites compared to offshore sites, attributed to complex terrain and higher sensitivity factors at the former.
Comparisons with reanalysis data show higher percent bias, mean absolute percentage error (MAPE), and lower correlation for onshore sites, due to their complex terrain. Additionally, no significant correlation was found between the distance of GCM grid points from reanalysis sites and MAPE. These discrepancies arise from differences in dataset resolution, topographical representation, and data assimilation processes, emphasizing the need for careful consideration when comparing GCM projections with reanalysis data.