Household-differentiated demand modelling for communities

Micro-generation schemes are increasingly being proposed for and incorporated in new-build housing developments. Community composition dependant demand prediction for such schemes is poorly understood and modelled. Using a previously developed higher-order Markovchain occupancy model, differentiated for different household types, an occupancy-driven electricity demand model has been developed from high resolution appliance-level data to realistically distribute demand cycles for individual households. The model incorporates a novel event-based method for linking the time-of-day probability of appliance cycles relative to occupancy, which allows accurate replication of expected demand patterns and improves computational efficiency compared to existing models. Additional socio-economic and behavioural factors are also included to better capture demand diversity

An occupant-differentiated, higher-order Markov Chain method for prediction of domestic occupancy

Household energy demand is closely correlated with occupant and household types and their associated occupancy patterns. Existing occupancy model performance has been limited by a lack of occupant differentiation, poor occupancy duration estimation, and ignoring typical occupancy interactions between related individuals. A Markov-Chain based method for generating realistic occupancy profiles has been developed that aims to improve accuracy in each of these areas to provide a foundation for future energy demand modelling and to allow the occupancy-driven impact to be determined. Transition probability data has been compiled for multiple occupant, household, and day types from UK Time-Use Survey data to account for typical behavioural differences. A higher-order method incorporating ranges of occupancy state durations has been used to improve duration prediction. Typical occupant interactions have been captured by combining couples and parents as single entities and linking parent and child occupancy directly. Significant improvement in occupancy prediction is shown for the differentiated occupant and occupant interaction methods. The higher-order Markov method is shown to perform better than an equivalent higher-order ’event’-based approach. The benefit of the higher-order method compared to a first-order Markov model is less significant and would benefit from more comprehensive occupancy data for an objective comparison

A disaggregated, probabilistic, high resolution method for assessment of domestic occupancy and electrical demand

An integrated domestic occupancy and demand model with a 1-min resolution has been developed which better captures the influence of different occupant behaviours than previous models. The occupancy model includes the fundamental link between occupancy and demand, and differentiates between different types and sizes of households. In particular, the likelihood of daytime occupancy is captured by age and employment differentiators. A novel method for identifying appliance use events and linking use to an occupancy profile has been developed that accounts for household specific appliance usage using an event-based approach calibrated directly from measured data. The method has been shown to perform better than both per-timestep probability models and models calibrated from time-use survey activity diaries. To further capture individual household behaviours, the demand model incorporates additional factoring to account for income and random behavioural influences. Whilst improving differentiation of individual household energy usage, due to limitations in the available data, the model incorporates some occupancy and use behaviour factors that are a composite of multiple households, leading to some behaviour averaging in the model output; consequently the model is best employed for energy demand assessment of multiple households

Local pathways to low-carbon domestic heat : exploring the options in the UK

Currently, natural gas is the predominant source of domestic heat provision. Take-up of heat pumps and district heating remains at a minimal penetration of around 0.5%. In total, only around 2.5% of heat comes from low carbon sources, compared with more than 45% of electricity. As heat accounts for around 40% of UK energy consumption and 20% of GHG emissions, the decarbonisation of the heat sector is seen as vital for the UK to reach UK emission reduction targets. Different trajectories in heat provision using parallel energy vectors (electricity, gas, alternative gases, heat networks) imply a range of infrastructure impacts. In order to explore the form of different local energy systems under decarbonisation scenarios, this work seeks to: - Capture the broad forms of ’last-mile’ network: Urban, Suburban, Rural (on/off gas grid ) seen as exemplar of the UK energy system; - Downscale whole system-derived technology mixes and construct demonstrative local energy systems representing key use cases; - Using multi-carrier optimisation, determine the impacts of heat decarbonisation on current and future system actors

Local pathways to low-carbon domestic heat : exploring the options in the UK

Currently, natural gas is the predominant source of domestic heat provision. Take-up of heat pumps and district heating remains at a minimal penetration of around 0.5%. In total, only around 2.5% of heat comes from low carbon sources, compared with more than 45% of electricity. As heat accounts for around 40% of UK energy consumption and 20% of GHG emissions, the decarbonisation of the heat sector is seen as vital for the UK to reach UK emission reduction targets. Different trajectories in heat provision using parallel energy vectors (electricity, gas, alternative gases, heat networks) imply a range of infrastructure impacts. In order to explore the form of different local energy systems under decarbonisation scenarios, this work seeks to: - Capture the broad forms of ’last-mile’ network: Urban, Suburban, Rural (on/off gas grid ) seen as exemplar of the UK energy system; - Downscale whole system-derived technology mixes and construct demonstrative local energy systems representing key use cases; - Using multi-carrier optimisation, determine the impacts of heat decarbonisation on current and future system actors

Developing a statistical electric vehicle charging model and its application in the performance assessment of a sustainable urban charging hub

A statistical model to calculate dynamic, electric vehicle (EV) charging loads at public hubs, which can be used with building simulation tools is presented; it was generated using two, real datasets and shown to faithfully recreate the characteristics of charging seen in the monitored data. The model was used with a building simulation tool to assess the ability of rooftop PV with battery buffering to mitigate the effects of urban EV charging for a charging hub and car park in Glasgow, Scotland. The car park’s 200 kW PV array could fully-offset the demand of a fleet of approximately 50 vehicles. The addition of a small buffering battery

Genetic variation of a bacterial pathogen within individuals with cystic fibrosis provides a record of selective pressures

Advances in sequencing have enabled the identification of mutations acquired by bacterial pathogens during infection1-10. However, it remains unclear whether adaptive mutations fix in the population or lead to pathogen diversification within the patient11,12. Here, we study the genotypic diversity of Burkholderia dolosa within people with cystic fibrosis by re-sequencing individual colonies and whole populations from single sputum samples. Extensive intra-sample diversity reveals that mutations rarely fix within a patient's pathogen population—instead, diversifying lineages coexist for many years. When strong selection is acting on a gene, multiple adaptive mutations arise but neither sweeps to fixation, generating lasting allele diversity that provides a recorded signature of past selection. Genes involved in outer-membrane components, iron scavenging and antibiotic resistance all showed this signature of within-patient selection. These results offer a general and rapid approach for identifying selective pressures acting on a pathogen in individual patients based on single clinical samples

Assessing the ability of roof-mounted photovoltaic (PV) canopies to support electric vehicle (EV) charging in cities

This paper assesses the ability of roof-mounted photovoltaic (PV) canopies and a battery to support public, electric vehicle (EV) charging at car-park-based charging hubs. A proposed EV charging hub in central Glasgow was modelled using a combination of tools including the ESP-r simulation package, a Matlab load flow model and an EV charging tool developed specifically for this paper. ESP-r has an integrated PV model, and this was used to determine the time-varying power output of the PV canopy, which was rated at 200kW. Performance was simulated over a calendar year, using a Glasgow test reference climate. The EV charging tool, which was calibrated using Transport Scotland field data, was used to determine the corresponding time-varying demand from EV charging. Various scenarios where examined, including different sizes of vehicle fleets serviced by the charging hub (10, 20 and 50), different battery sizes (0-500kWh) and two different battery operating strategies. The simulation results indicated that the peak power output of the PV array was 110kW and the annual yield was approximately 110 MWh. Without a battery, only between 35-58% of PV generated electricity was used locally, with the percentage of local consumption falling as the number of vehicles serviced increased. Adding a battery improved on-site consumption of PV generated electricity, and reduced the peak power drawn from the local grid. However the peak exported power to the grid was insensitive to battery size. It was determined that a battery size of 8-10 kWh per vehicle serviced gave the best return in terms of increasing consumption of locally produced power and reducing peak power demand

Exploring value and performance parameters for thermal energy storage in low carbon buildings and districts

There is much effort focussed on development and implementation of thermal energy storage (TES) for future energy systems. This paper explores the context within which TES has potential to provide benefits from a range of perspectives. First the wider role of storage and demand side flexibility is explored and then the potential roles of TES, both explicit TES (i.e. designed storage systems) and inherent TES (e.g. in standard building structure) are examined. The potential benefits of storage are categorised as: (i) short term supply side response, (ii) load shaping for supply side optimisation, (iii) local supply optimisation, (iv) capital investment and return on investment optimisation, (v) comfort and resilience. A set of potential downsides for TES systems is also given. For each category performance metrics are proposed which could be used to support the quantification of the benefits of TES in modelling and other assessments