ICT-2009-247473
Due to the space limitations and lower levels of primary energy resources, renewable-based generation technologies implemented in built environments are usually realised as micro and small-scale systems, commonly known as micro-generation (MG) technologies. Although the individual MG units are highly dispersed and small in sizes, their total numbers are expected to increase significantly in the future, when their aggregate effects and provided benefits (e.g. in a large urban area) are, in essence, similar to those of medium to large-scale generation systems. Additionally, MG will provide residential customers with an attractive option for reducing overall electricity consumption and energy bills, particularly when appropriate incentives and subsidies are incorporated in their installation grants and when they can negotiate suitable tariffs for generated and grid-exported electricity. Despite the fact that the selection of optimal MG technology is usually determined by locally available renewable energy resources, PV and wind-based MG systems are currently the two most common types of MG in the majority of European countries, requiring, therefore, improved and more accurate aggregate models for their analysis.
The correct analysis and representation of MG systems requires detailed assessment of input energy resources and accurate modelling of applied generation technologies. Using Midlothian region in Scotland, UK (around the city of Edinburgh) as an example, a new methodology for assessing performance of PV/wind MG technologies and analysing their aggregate effects on the operation of typical LV/MV distribution networks will be introduced and discussed. The presented analysis takes into account relatively high levels of temporal and spatial variations of input solar/wind energy resources in urban areas, which typically change on a both short-term scale (e.g. minute-by-minute, or hourly variations) and long-term scale (e.g. daily, weekly, or seasonal variations), as well as from one (geographic or network) location to another. Based on the estimated ranges of these variations, the outputs of aggregate PV and wind MG will be calculated and correlated with the corresponding variations in load demands, allowing to obtain improved and more accurate aggregate system load models, capable of correctly representing not just the connected loads, but also all supply network components and all MG units connected downstream the point of aggregation. As the proposed aggregation methodology allows to identify demand-manageable portion of the load in the total demand, implementation of specific demand-side management schemes in networks with different penetration levels of MG will be discussed and illustrated on a number of selected practical cases and scenarios for energy management in households and built environments in future “smart grids”.