Profiling and disaggregation of electricity demands measured in MV distribution networks
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Despite the extensive deployment of smart-meters (SMs) at the low-voltage (LV) level, which are either fully operational or will be in the near future, distribution network operators (DNOs) are still relying on a limited number of permanently installed monitoring devices at primary and secondary medium-voltage (MV) substations, for purposes of network operation and control, as well as to inform and facilitate trading interactions between generators, distributors and suppliers. Accordingly, improved and sufficiently developed models for the analysis of aggregate demands at the MV-level are required for the correct assessment of load variability, composition and time-dependent evolution, necessary for: addressing issues of robustness, security and reliability; accomplishing higher penetration levels from renewable/distributed generation; implementing demand-side-management (DSM) schemes and incorporating new technologies; decreasing environmental and economic costs and aiding towards the realisation of automated and proactive ''smart-grid'' networks. The analysis of MV-demand measurements provides an independent source of information that can capture network characteristics that do not manifest in the data collected at the LV-level, or when such data is restricted or altogether unavailable. This information describes the supply/demand interactions at the mid-level between high-voltage (HV) transmission and LV end-user consumption and opens possibilities for validation of existing bottom-up aggregation approaches, while addressing issues of reliance on survey-based data for technical and economic power system studies. This thesis presents improved and novel methodologies for the analysis of aggregate demands, measured at MV-substations, aimed at more accurate and detailed load profiling, temporal decomposition and identification of the drivers of demand variability, classification of grid-supply- points (GSPs) according to consumption patterns, disaggregation with respect to customer-classes and load-types and load forecasting. The developed models are based on a number of traditional and modern analytical and statistical techniques, including: data mining, correlational and regression analysis, Fourier analysis, clustering and pattern recognition, etc. The approaches are demonstrated on demand datasets from UK and European based DNOs, thus providing specific information for the demand characteristics, the dependencies to external parameters and to socio-behavioural factors and the most likely load composition at the corresponding geographical locations, while the approaches are also intendent to be easily adaptable for studies at equivalent voltage and demand aggregation levels.