This study tackles the challenge of optimizing light emitting diode (LED) street lighting to enhance energy efficiency without compromising safety and comfort in dynamic urban environments. Conventional adaptive lighting systems often overlook real-time microlocation variations, such as fluctuating traffic, pedestrian activity, and weather conditions, resulting in excessive energy consumption or inadequate illumination. To address this limitation, we develop and validate a simulation environment that integrates a model predictive control (MPC) algorithm designed to predict and dynamically adjust lighting intensity using high-resolution environmental data. Our primary objectives are to establish a robust MPC framework for real-time streetlight optimization under varying conditions, to validate its performance through simulation and real-world deployment, and to quantify its energy savings. Theoretically, this work contributes to a novel MPC-based control strategy for predictive modeling of dynamic urban variables, allowing proactive lighting adjustments. Practically, we demonstrate the system’s feasibility through a case study in Sisak, Croatia, where it achieved 31.94% energy savings while maintaining illumination quality. This research advances smart city infrastructure by offering a scalable and tunable control framework that harmonizes energy efficiency with public safety.

The large-scale integration of renewable energy has significantly improved the low-carbon performance of power systems, but has also increased operational uncertainty. Under extreme weather conditions, wind and solar power may experience consecutive days of simultaneous output shortfalls—referred to as “renewable energy drought”—leading to persistently high net load and severe challenges to supply guarantee. To address this issue, this paper proposes a multi-source coordinated supply-guarantee dispatch strategy for consecutive-day renewable energy drought scenarios. First, net load is defined as the total system load minus the available wind and solar output. Based on magnitude and duration thresholds, renewable energy drought events are extracted from historical data to generate representative scarcity scenarios. Second, a multi-source coordinated optimization dispatch model is constructed, incorporating wind power, solar power, thermal units, battery energy storage, and pumped-storage hydro. The objective is to minimize the total system operating cost, which includes thermal fuel cost, start-up/shut-down costs, storage cycling cost, wind/solar curtailment penalty cost, and load shedding penalty cost. The load shedding penalty coefficient is set to a magnitude much higher than conventional costs to highlight the priority of supply guarantee. The model accounts for operational constraints such as minimum up/down times, deep regulation capability, ramping limits of thermal units, and charge/discharge power limits of storage. Taking a provincial power system in China for the year 2030 as a case study, a dispatch case covering four consecutive days (96 time periods) is designed. Based on a baseline scenario, eight groups of sensitivity analyses are conducted to comprehensively investigate the impacts of key factors on the supply-guarantee strategy, including: the minimum up/down time of thermal units, deep regulation capability, load shedding penalty cost, load level, rated energy capacity and charge/discharge efficiency of battery energy storage, rated energy capacity and pumping/generating efficiency of pumped-storage hydro, thermal fuel cost coefficient, and renewable energy capacity. Simulation results show that the proposed strategy can effectively coordinate multiple resources under consecutive-day drought conditions; reducing the minimum up/down time of thermal units improves supply flexibility but increases start-up/shut-down costs; enhancing deep regulation capability optimizes storage utilization and reduces total system cost; the load shedding penalty cost directly determines the trade-off between supply guarantee and economic efficiency; and as load level decreases by 5%, 10%, and 15%, the total system operating cost reduces by approximately 6.3%, 12.5%, and 18.8%, respectively. This study provides a quantitative method and technical support for supply-guarantee dispatch decisions and resource allocation in high-renewable power systems under persistent drought conditions.