Periodic Reporting for period 2 - Spatialec (Developing methods to model local area temporal domestic electricity demand)
Reporting period: 2019-11-01 to 2020-10-31
Driven by the need to de-carbonise energy supplies, to reduce overall demand, to shift demand away from systemic or critical peaks and to cope with localised, time-specific and/or intermittent generation, the emerging view of Europe’s (green) smart grid future places substantial emphasis on demand-side response (DR) as a key component of a sustainable electricity system.
There is a clear need to develop a modeling framework which can allow regulators, policy researchers and commercial analysts to understand how particular combinations of scenarios might affect electricity infrastructures at varying levels of geographical scale and at different times of the day, week and year.
This research responded to this need by developing a microsimulation based approach to demand modeling at the household level and integrates this with known ‘spatialisation’ approaches to develop area level temporal demand profiles. The outcome is a model that can produce not only a local area temporal demand (and potential response) ‘map’ under a range of scenarios but also indicators of the range of responses likely as input to network operator infrastructure management analysis and/or local power generation planning decisions.
The work applied the University of Otago’s ‘Energy Cultures’ and ‘GREEN Grid’ research to develop local (neighbourhood) level electricity demand models. The results have been used to analyse the local demand implications of a range of NZ demand response scenarios such as EV uptake, energy efficient lighting or increased heat pump use.
In the return phase the approach has been applied to similar data derived from the ‘SAVE’ Low Carbon Network Fund project to produce a generally applicable model for the South East of England. This model has been validated using area level annual demand data and substation monitoring data before being used to model the local area effects of a number of demand response interventions. The work has been disseminated widely with a view to ensuring exploitation through relevant infrastructure and demand response stakeholders.
There is a clear need to develop a modeling framework which can allow regulators, policy researchers and commercial analysts to understand how particular combinations of scenarios might affect electricity infrastructures at varying levels of geographical scale and at different times of the day, week and year.
This research responded to this need by developing a microsimulation based approach to demand modeling at the household level and integrates this with known ‘spatialisation’ approaches to develop area level temporal demand profiles. The outcome is a model that can produce not only a local area temporal demand (and potential response) ‘map’ under a range of scenarios but also indicators of the range of responses likely as input to network operator infrastructure management analysis and/or local power generation planning decisions.
The work applied the University of Otago’s ‘Energy Cultures’ and ‘GREEN Grid’ research to develop local (neighbourhood) level electricity demand models. The results have been used to analyse the local demand implications of a range of NZ demand response scenarios such as EV uptake, energy efficient lighting or increased heat pump use.
In the return phase the approach has been applied to similar data derived from the ‘SAVE’ Low Carbon Network Fund project to produce a generally applicable model for the South East of England. This model has been validated using area level annual demand data and substation monitoring data before being used to model the local area effects of a number of demand response interventions. The work has been disseminated widely with a view to ensuring exploitation through relevant infrastructure and demand response stakeholders.
The outgoing phase of the work implemented a national and local level electricity demand model for New Zealand. Main results achieved:
- the archival of a clean version of the GREENGrid household electricity demand monitoring data with the UK Data Archive for third party re-use. This data archive is supported by an R package prepared by the Researcher (https://cfsotago.github.io/GREENGridData);
- analysis of the contribution of lighting, hot water, space heating (via heat pumps) to demand profiles;
- analysis demonstrating that the literature on energy demand reduction is beset with under-powered studies using biased samples that can rarely be generalised;
- development of a complex reweighting approach to provide national level estimates constrained by NZ Census 2013 population totals;
- an analysis of the potential consequences of the uptake of LEDs, of curtailing peak demand and of switching to more efficient heating (heat pumps);
- an analysis of the charging patterns of a sample of up to 50 domestic electric vehicles in partnership with a NZ EV owners club (FlipTheFleet;
- development of an Area Unit level temporal electricity demand model for the regions of Taranaki and Hawke's Bay
The return phase of the work implemented a similar model for the South East of England. Main results achieved:
• contribution to the deposit of the UK SAVE data (Rushby et al. 2020) at the UK Data Service for third part re-use for non-commercial research;
• development of proof-of-concept model for the Solent region (Southampton, Portsmouth, Isle of Wight and Hampshire);
• application to local area modelling of demand response trial impacts in the Solent region;
• development of local area scenario models of the impacts of transition to a number of low-carbon heat or electricity demand response interventions with a particular focus on areas such as the Isle of Wight which have both a high penetration of PV and constrained low voltage networks.
In addition a number of other analyses have been conducted as part of the Fellowship:
1. Analysis of local air quality in Southampton during COVID19 lockdown;
2. Analysis of disruption to national and local electricity demand patterns during lockdown in the UK and New Zealand;
3. Modelling of the potential for ultra-low energy input retrofits (e.g. passiv haus standard) to reduce long term heat energy demand in New Zealand.
- the archival of a clean version of the GREENGrid household electricity demand monitoring data with the UK Data Archive for third party re-use. This data archive is supported by an R package prepared by the Researcher (https://cfsotago.github.io/GREENGridData);
- analysis of the contribution of lighting, hot water, space heating (via heat pumps) to demand profiles;
- analysis demonstrating that the literature on energy demand reduction is beset with under-powered studies using biased samples that can rarely be generalised;
- development of a complex reweighting approach to provide national level estimates constrained by NZ Census 2013 population totals;
- an analysis of the potential consequences of the uptake of LEDs, of curtailing peak demand and of switching to more efficient heating (heat pumps);
- an analysis of the charging patterns of a sample of up to 50 domestic electric vehicles in partnership with a NZ EV owners club (FlipTheFleet;
- development of an Area Unit level temporal electricity demand model for the regions of Taranaki and Hawke's Bay
The return phase of the work implemented a similar model for the South East of England. Main results achieved:
• contribution to the deposit of the UK SAVE data (Rushby et al. 2020) at the UK Data Service for third part re-use for non-commercial research;
• development of proof-of-concept model for the Solent region (Southampton, Portsmouth, Isle of Wight and Hampshire);
• application to local area modelling of demand response trial impacts in the Solent region;
• development of local area scenario models of the impacts of transition to a number of low-carbon heat or electricity demand response interventions with a particular focus on areas such as the Isle of Wight which have both a high penetration of PV and constrained low voltage networks.
In addition a number of other analyses have been conducted as part of the Fellowship:
1. Analysis of local air quality in Southampton during COVID19 lockdown;
2. Analysis of disruption to national and local electricity demand patterns during lockdown in the UK and New Zealand;
3. Modelling of the potential for ultra-low energy input retrofits (e.g. passiv haus standard) to reduce long term heat energy demand in New Zealand.
Progress beyond the state of the art includes:
- analysis of the components of residential peak electricity demand in New Zealand. This novel analysis using GREENGrid household electricity demand monitoring data provided the NZ Energy Efficiency and Conservation Authority with insights and though leadership on potential new policy foci;
- the archival of a unique multi-circuit household electricity monitoring data set via the UK Data Archive's ReShare service. This dataset has already seen substantial re-use.
- the development of open source tools to enable the modelling of local area temporal electricity demand scenarios;
- the development of practical guidance for energy research managers and analysts on how to design effective energy intervention studies;
- the application of the tools and methods to existing 'smart meter like' datasets held at the University of Southampton;
- ongoing development of an international network of applied researchers concerned with the modelling of local (neighbourhood) level demand scenarios;
The work has fed into a range of dissemination activities with potential for strong commercial and policy impact including submissions to UK and NZ policy consultations; engagement with local authorities and commercial stakeholders. This. is enabling at least one UK electricity distribution business to develop strategies to model the reconfiguration of demand. This is particular important in the context of the need to transition to a low emissions electricity system (National Grid ESO 2020; Committee on Climate Change 2019) and to avoid over-investment in under-utilised network capacity (Strbac et al. 2016; ENA 2017; Interim Climate Change Commission 2019).
The work is providing ways to conduct localised cost-benefit analyses of the value of different kinds of interventions and/or the potential threats inherent in novel gird-edge technologies. This in turn improves the competitiveness of local network operators by enabling them to appropriately invest in particular (local) regions and/or understand the potential new demands to be placed on the network (Ford et al. 2019).
This is expected to reduce the cost of service provision and thus the cost of electricity to customers in the long run whilst at the same time enabling the least cost integration of renewable generation into the network. In both cases these are likely to ensure enhanced commercial and climate-related sustainability of the sector over the long term (Committee on Climate Change 2019). This directly contributes to EU research policy objectives of ensuring Europe produces world-class science and technology that drives (sustainable) economic growth. In addition the insights and methods developed will support the EU policy objectives of increasing the share of energy consumption from renewable sources to 20% and improving energy efficiency in order to reduce the use of primary energy by 20% against forecast.
- analysis of the components of residential peak electricity demand in New Zealand. This novel analysis using GREENGrid household electricity demand monitoring data provided the NZ Energy Efficiency and Conservation Authority with insights and though leadership on potential new policy foci;
- the archival of a unique multi-circuit household electricity monitoring data set via the UK Data Archive's ReShare service. This dataset has already seen substantial re-use.
- the development of open source tools to enable the modelling of local area temporal electricity demand scenarios;
- the development of practical guidance for energy research managers and analysts on how to design effective energy intervention studies;
- the application of the tools and methods to existing 'smart meter like' datasets held at the University of Southampton;
- ongoing development of an international network of applied researchers concerned with the modelling of local (neighbourhood) level demand scenarios;
The work has fed into a range of dissemination activities with potential for strong commercial and policy impact including submissions to UK and NZ policy consultations; engagement with local authorities and commercial stakeholders. This. is enabling at least one UK electricity distribution business to develop strategies to model the reconfiguration of demand. This is particular important in the context of the need to transition to a low emissions electricity system (National Grid ESO 2020; Committee on Climate Change 2019) and to avoid over-investment in under-utilised network capacity (Strbac et al. 2016; ENA 2017; Interim Climate Change Commission 2019).
The work is providing ways to conduct localised cost-benefit analyses of the value of different kinds of interventions and/or the potential threats inherent in novel gird-edge technologies. This in turn improves the competitiveness of local network operators by enabling them to appropriately invest in particular (local) regions and/or understand the potential new demands to be placed on the network (Ford et al. 2019).
This is expected to reduce the cost of service provision and thus the cost of electricity to customers in the long run whilst at the same time enabling the least cost integration of renewable generation into the network. In both cases these are likely to ensure enhanced commercial and climate-related sustainability of the sector over the long term (Committee on Climate Change 2019). This directly contributes to EU research policy objectives of ensuring Europe produces world-class science and technology that drives (sustainable) economic growth. In addition the insights and methods developed will support the EU policy objectives of increasing the share of energy consumption from renewable sources to 20% and improving energy efficiency in order to reduce the use of primary energy by 20% against forecast.