Mitigating household energy poverty through energy expenditure affordability algorithm in a smart grid

One of the criteria for measuring household energy poverty is the percentage of the household's income spent on energy expenses. In this work, an autonomous income-based energy scheduling demand side management (DSM) technique called energy expenditure affordability algorithm (EEAA) is proposed to ensure that household energy expenditure is below the nation's approved energy expenditure threshold. The EEAA problem was formulated as a mixed integer linear programming (MILP) problem and verified with real household data collected from families living in bachelor flats in Johannesburg, South Africa. Consumer preferences and satisfaction were enhanced by using the dynamic time warping (DTW) technique to minimize the distance between nominal and EEAA load profiles. Furthermore, the effects of distributed energy generation (DEG) and distributed energy storage (DES) were also investigated in light of energy expenditure affordability for improved consumer-friendly and satisfying DSM. The EEAA-DSM technique is shown to reduce household energy expenditure below the energy expenditure threshold, offering energy expenditure affordability as well as utility grid peak demand reduction (PDR). Furthermore, grid reliability and sustainability, environmental preservation and gendered energy poverty are consequential benefits of the EEAA. It also offered the households considered an average financial savings from 12% to 82%, depending on the level of implementation of distributed storage and generation to the consumer's local energy mix.