6 research outputs found
Optimal operation of a multi vector district energy system in the UK
The large price drop in solar PV and electrical batteries offer new opportunities for optimizing district energy plants, but requires a more complex daily operation of these plants. Solar PV production used locally by a ground source heat pump (GSHP) with a minimal use of the national grid is one opportunity. Even if, for the benefit of the GSHP, the share of electricity for boosting the temperatures of district heating water goes up when lowering forward temperatures in the network down to as low as 45 °C, the overall operational income is improved
Multi-dwelling refurbishment optimization: problem decomposition, solution and trade-off analysis
A methodology has been developed for the multiobjective
optimization of the refurbishment of
domestic building stock on a regional scale. The
approach is based on the decomposition of the
problem into two stages: first to find the energy-cost
trade-off for individual houses, and then to apply it
tomultiple houses.
The approach has been applied to 759 dwellings
using buildings data from a survey of the UK
housing stock. The energy use of each building and
their refurbished variants were simulated using
EnergyPlus using automatically-generated input files.
The variation in the contributing refurbishment
options from least to highest cost along the Pareto
front shows loft and cavity wall insulation to be
optimal intially, and solid wall insulation and double
glazing appearing later
Developing a geographically detailed housing stock model for the North East of England
Housing stock models predict long term changes in the stock to
inform national policy. They operate with a set of reference dwellings
representing the national stock, which are changed in response to different
scenarios. However, national level models do not consider geographical
variations (urban location/rural surroundings, index of multiple deprivation score,
etc.), so cannot aid in targeting improvement measures (eg: insulation, microgeneration,
etc.) locally. A geographically varying model can identify which
measures are most appropriate in a particular location. In this paper a method
has been designed and implemented using information at LSOA level (c. 700
dwellings each) to introduce geographical variation for a model of the North
East of England. It has been tested against DECC meter data and over 80% of
LSOAs are predicted to within ±25% of DECC’s data. The model allows
localised policies and interventions to be tested, and is principally of interest to
local government and energy efficiency initiatives
Coupling a stochastic occupancy model to EnergyPlus to predict hourly thermal demand of a neighbourhood
When designing and managing integrated renewable
energy technologies at a community level, prediction
of hourly thermal demand is essential. Dynamic
thermal modelling, using deterministic occupancy
profiles, has been widely used to predict the highresolution
temporal thermal demand of individual
buildings. Only in recent years has this approach
started to be applied to simulate all buildings in a
neighbourhood or an entire housing stock of a region.
This study explores the potential of predicting hourly
thermal demand for a group of dwellings by applying
a stochastic occupancy model to dynamic thermal
modelling. A case study with 125 new houses
demonstrates the approach. The result was a more
realistic and representative hourly thermal demand
profile, compared to using standard deterministic
occupancy profiles
Modelling and calibration of a domestic building using high-resolution monitoring data
Reducing energy consumption and managing energy supply/demand responses are key challenges facing the future built environment. The use of de-carbonised electricity to deliver space heating will make significant impact on CO2 emissions for the UK. A likely technology in UK homes is to replace conventional gas boilers with heat pumps. A high coefficient of performance may mean a reduction in energy consumed, in addition the potential to contribute to demand side response through switching controlled via pricing signals. Evaluating the likely energy demand patterns from such systems and understanding how the characteristics of such systems might affect comfort can be estimated using building simulation. This paper describes the modelling and calibration process of an UK family dwelling using high-resolution monitoring data. Monitoring data describing gas, electricity, hot water, window operation and room temperature at minutely interval are used in the process
Dynamic modelling of a large scale retrofit programme for the housing stock in the North East of England
Housing stock models have long been employed to estimate the
baseline energy demand of the existing housing stock, as well as to predict the
effectiveness of applying different retrofit measures and renewable technologies
on reducing the energy demand and corresponding CO2 emissions. This
research aims to develop a dynamic housing stock model to simulate the hourby-hour
energy demands of 1.2 million dwellings in the North East (NE) of
England using the 2008-9 English Housing Survey (EHS) data. The model is
validated by comparison to a steady-state energy model. Using the model, new
results predicting the impact of a large scale retrofit programme for the NE
housing stock are generated