Renewable Electrical Energy Strategies for Low and Zero Carbon Homes David Cowan, Issa Chaer* and Graeme Maidment London South Bank University, 103 Borough Road, London SE1 0AA * Corresponding Author – Chaeri@lsbu.ac.uk ABSTRACT This paper presents the results of a study carried out at London South Bank University into how current electrical power generation and storage technologies might be integrated with advanced energy management systems to achieve zero carbon dwellings to 2016 (implementation of the Code for Sustainable Homes (CSH) Level-6) and beyond. Whereas thermal energy demands of buildings are tending to decrease as they become more thermally efficient, electrical energy demands for dwellings are unlikely to decrease significantly, because the increasing efficiency of electrical equipment and appliances tends to be offset by higher levels of ownership and utilization. Baseline energy demand profiles were developed for a range of typical dwellings built to Part L 2006 building standards [1] and then used to predict the energy demand for similar dwellings if built in 2016 to CSH Level-6 [2,3]. A renewable electrical power generation and storage model was developed and used with the electrical energy demand profiles to determine the overall hourly energy balance for the dwelling and to establish the feasibility of achieving a ‘zero carbon’ performance. A practical design approach, using wind turbine and solar PV generation, together with a battery power storage system, was shown to give net zero carbon on an annual basis, although not on a daily basis during winter months. The inclusion of a fuel cell in the energy model could make good the electrical energy generation shortfalls in the winter months. Introduction A key concern with renewable energy generation, particularly for electrical power, is that peak generation rarely matches the demand profile and in the case of wind and solar PV energy in particular, the power generation may also be unpredictable, except over relatively long timescales. A significant challenge for the designers of low and zero carbon homes will therefore be not only to generate enough renewable electrical energy on-site (or off-site using private wire connection), but also to deliver it when it is needed. The national grid meets the demand of all energy users through a combination of scale, diversity (geographical and mix of generation types), frequency management and the use of standby and backup generation capacity and energy storage (e.g. pumped hydro). However, for a single dwelling or small scale development at CSH Level-6, the variability of local renewable energy generation will present challenges in meeting such demands. Combining different technologies such as solar PV and micro-CHP generation may help to smooth the imbalance. However, commercially available micro-CHP systems are fuelled on natural gas, operate with high heat-to-power ratios and do not qualify as renewable energy sources. Also CHP system cannot track fast changing loads and overshoots, so there would still be significant import and export of energy from and to the grid over the 24 hour period unless a local energy stores were used to balance short term differences. This paper presents the results of an investigation into how current renewable electrical power generation and storage technologies might be integrated to achieve low or zero carbon dwellings to 2016 and beyond. Dwelling Types and Energy Models A range of typical dwelling types were considered, including terraced, semi-detached and detached houses, bungalows and flats. The parameters used for each dwelling are in line with the BRE parameters for their standard dwelling and are listed in Table1. Dwelling Type Dwelling Floor Area m 2 Flat 60.9 End-terrace 78.8 Det-bungalow 67.3 Semi-house 88.8 Det-house 104 Table 1: Floor areas for the selected dwellings The overall heat transfer coefficient (U) values used in the Level-6 BREDEM-12 models were the RAB [4] figures, 0.8 W/m 2 K for windows and doors and 0.15 W/m 2 K for ground floors, walls and roofs. In order to determine the maximum available area for PV panels on the different dwelling types a simple spreadsheet model was