No abstract

There are wide applications of block-rate pricing schemes in many countries. However, there are no significant studies that apply this common tariff for smart home energy management systems. In this paper, a three-time-frame energy management scheme has been proposed for photovoltaic (PV)-powered grid-connected smart homes based on the well-known mixed-integer linear programming optimization technique. This paper provides three original and novel smart home energy management algorithms that depend on the most common residential tariff specifically in developing countries. Three different management concepts have been studied for a typical Egyptian house. The concepts of shifting load, vehicle-to-home and reducing air conditioning have been tested according to a commonly applied slab tariff. The proposed scheme considers the home battery extending lifetime constraints. It also preserves comfortable lifestyle limits for home users according to Arab housing climatic conditions and culture. Moreover, the economic feasibility of integrated PV modules for the studied home has been verified according to the Egyptian tariff. The proposed energy management scheme of PV-powered home reduces the electrical power bill significantly in a wide range from 61% to only 19% of the default case bill according to the applied management technique.

Solar-powered homes can be an optimal solution for the lack of continuous power sources problem in initial low-income communities. However, the challenge of PV uncertainty can make it difficult to coordinate this vital solar energy in real-time. This paper proposes a new, low-cost solution for assessing the uncertainty of photovoltaic power generation in smart home energy management systems. The proposed index, inspired by the well-known clearness index, is an adaptive deterministic indicator that only requires free Geographic Information System GIS models and PV power measurement, without the need for expensive high-tech controllers or expert engineers/programmers. The proposed index successfully predicts the daily PV energy with errors of less than 3% for more than 93% of studied days, according to the 2020 measured solar radiation of the studied case.