A decision-making design framework for the integration of PV systems in the urban energy planning process

Researchers typically use performance analysis tools to support the design decisions of PV systems on urban scales. However, performance simulation tools are mostly used for analysis only, providing feedback to confirm the system performance rather than influencing the system design to respond to the intended performance goals. Hence, it is essential to use simulation for synthesis and generation instead of analytical simulation in designing PV systems. Employing evolutionary algorithms (AEs) seems to be a great way to solve multi-objective optimization problems that aim to design PV systems in accordance with the electricity demand and peak times of neighborhoods. This study proposes a novel design decision-making framework to develop, evaluate, and interact with the design of the PV system at the neighborhood scale in one interface to better achieve the intended objectives. The design objectives of this study include increasing the self-consumption (SC), decreasing the payback period (PB), maintaining higher self-sufficiency (SS) of the PV system, and reducing the net load variance over the grid. The study aims to increase the correlation between the PV system's electricity production and the aggregate electricity demand of buildings in neighborhoods. This is done by optimizing the range of orientation of the PV panels and the number of panels on each orientation. The design decision-making framework is guided by multiple scenarios, and a residential neighborhood located in Los Angeles, California, is considered a case study. The results show how the PV production pattern differs when various design objectives are considered in the optimization process. In general, the optimized PV system in all tested scenarios has more PV panels in the south-west orientations as a response to the late demand and the higher electricity prices in the evening in the studied neighborhood.