A novel transactive integration system for solar renewable energy into smart homes and landscape design: A digital twin simulation case study

Abstract

The use of solar renewable energy in landscape design is becoming increasingly popular as the world strives to reduce its reliance on fossil fuels. With the cost of solar energy falling, more and more people are turning to solar as a way to power their homes and businesses. In this article, we will explore the integration of solar renewable energy into landscape design through a case study. Using local photovoltaics along with suitable power storage systems, a household power management system is proposed to optimize the scheduling of demand-responsive appliances. The beta likelihood distribution function for sun irradiance is used to model the unpredictable behavior of photovoltaic energy production in residential power management systems. The PV system was integrated into the building’s existing landscape design by incorporating the system’s components into the existing landscape elements. This included the installation of a large solar array on the roof of the building, as well as the integration of solar panels into the existing garden beds and pathways. The solar array was also connected to the building’s existing electrical system, allowing the building to draw power from the solar array when needed. Among the major contributions of the study would be to optimize the planning of demand-responsive devices within a domestic power management system by using the improved leader particle swarm optimization method (ILPSO). The purpose would be to reduce peak-to-average ratios (PARs) and energy usage costs in the smart home. A number of scenarios are designed and simulated in a digital twin structure for a household user with various initial loads, uninterruptible deferrable, and interruptible deferrable devices using a real-time power cost program to demonstrate the performance of the suggested optimization method. Based on comparisons with various metaheuristics previously documented, the new ILPSO algorithm efficiently optimizes energy usage costs and PARs.