Optimal design and allocation of photovoltaic solar energy for critical distributed traffic light loads using an iterative TOPSIS method

The uninterrupted operation of critical and distributed loads, such as traffic lights in urban environments, during power outages presents a significant challenge for city management and infrastructure authorities. Even brief power disruptions due to energy crises can trigger cascading issues, including traffic congestion, delays, accidents, and delays in emergency response vehicles, such as fire trucks and ambulances. Consequently, guaranteeing a reliable supplementary power supply for these essential loads is becoming increasingly crucial. This study offers a comparative analysis of existing power supply solutions, including traditional grid connections, renewable energy systems, and hybrid configurations that incorporate energy storage technologies. A case study focusing on traffic lights at major intersections in Qazvin City demonstrates the practical application and efficacy of the proposed methods in real-world scenarios. The investigation evaluates the performance of a solar energy system during short-term outages, emphasizing the impact of seasonal variations in solar radiation and ambient temperature. Response strategies to planned electricity interruptions are examined, with recommendations formulated to improve system reliability for critical loads. The results indicate that the highest operational continuity occurs during outages around 9 AM, while capacity diminishes at night, predominantly due to the system’s dependence on battery storage. Monthly data projections suggest operation times of approximately 4–8 h at 50% battery charge and 5–9 h at 80% charge. In this optimization problem, the mutual goals of traffic and electrical performance were simultaneously considered as a multi-objective framework. Moreover, the optimal selection of candidate intersections for solar system deployment was performed using the TOPSIS method in both the conventional and iterative modes. The iterative approach, which integrates the influence of prior selections into subsequent decision-making, yielded higher accuracy and more consistent results. To enhance the robustness of the analysis, a sensitivity analysis was conducted by varying one or two parameters simultaneously within the TOPSIS framework. This process ensured that the results were not only reliable but also resilient to parameter fluctuations, providing a precise and dependable foundation for strategic decision-making in critical urban-energy applications.