Remote and rural settlements with weak grid infrastructure face compounding challenges: unreliable electricity supply, high fossil fuel transportation costs, and difficulty integrating variable renewables. While distributed renewable energy offers promise, wind and solar intermittency becomes especially problematic in isolated grids, where backup capacity is limited and expensive.
Researchers have proposed a novel solution combining airborne wind energy (AWE) with advanced scheduling and demand management. Airborne wind systems—tethered devices that operate at higher altitudes where wind is more consistent—capture steadier resources than ground-level turbines. By adjusting altitude dynamically, operators can fine-tune power output to match system needs, effectively decoupling generation from weather variability in ways traditional wind farms cannot.
The framework integrates three key innovations. First, a two-stage optimization model allows AWE altitude and output to participate directly in scheduling decisions rather than treating them as fixed parameters. Second, virtual battery-based demand response represents how flexible loads can absorb or reduce consumption, acting like distributed storage without physical batteries. Third, a Wasserstein distance-driven distributionally robust optimization approach quantifies tail risks using Conditional Value-at-Risk, ensuring the system remains stable even under worst-case scenarios.
Testing on a township-level system in Hami, Xinjiang demonstrated measurable improvements: total operating costs fell 3.67% and tail-risk losses decreased 7.52% versus baseline approaches. Virtual battery demand response alone contributed an additional 10.3% savings by enabling load flexibility.
This methodology addresses a critical gap in energy access. Weak-grid regions—common in developing nations, mountainous terrain, and island communities—cannot rely on centralized generation or interconnection solutions. By combining altitude-adjustable wind capture with intelligent load management and robust planning under uncertainty, the framework offers practical scalability. It enables high renewable penetration without expensive grid upgrades or fossil fuel dependence, making reliable electricity economically viable in previously underserved areas.
