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The star-labelling programme for residential buildings was introduced by India in 2020 and applies to all residential buildings with no lower limit on the built-up area or electrical demand. The energy-star label for a residential building is awarded against the notified standard by the regulatory body and electric vehicles (EVs) have not been accommodated as a load for residential buildings. The energy consumption of an existing residential building is taken from a study already carried out and compared with the requirement of the Indian residential star-labelling programme with an EV as a plugged-in load. An annual energy gap of 6060 kWh for the existing residential buildings considered in this study for five-star building energy labels increases to 7784 kWh if the EV load is added to the building load. The residential building will lose two energy stars if it caters to the EV load and, to bridge this energy gap, the replacement of existing electrical appliances with five-star-rated energy appliances, employing grid-connected rooftop solar photovoltaics (PV) and retrofit of the building envelope are considered. The techno-economic potential of rooftop solar PV and building envelope retrofitting for existing residential buildings is explored using RETScreen® and eQUEST software, respectively. The study establishes that the installation of rooftop solar PV can accommodate the additional load of EVs and can bridge half and three-quarters of the energy gap to achieve five energy stars for an existing building with and without EVs, respectively. It is the most economical option among the options explored in this study. The target Energy Performance Index is achievable by high-end energy consumers (12 000 kWh/year) by additional measures, the replacement of inefficient electrical appliances and building envelope retrofitting in addition to the installation of rooftop solar PV. [ABSTRACT FROM AUTHOR]

This report does some work on big data analysis of EV loads and extends automatic generation control (AGC) to automatic active power control (APC) by combining the current operating conditions of the grid and the spatial and temporal distribution characteristics of the adjustable capacity of EV loads. One year of user data from three EV charging stations provided by Chongqing is processed, cleaned, and clustered to analyze several typical EV load behaviors and the assessment of their adjustable characteristics. The data processing methods and the comparison of the K-means algorithm, the direct clustering, and the hierarchical clustering algorithm DPC clustering algorithm are also shown for this example.