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