Unmanned Aerial Vehicle (UAV) swarms are often required in off-grid scenarios, such as disaster-struck, war-torn or rural areas, where the UAVs have no access to the power grid and instead rely on renewable energy. Considering a main battery fed from two renewable sources, wind and solar, we scale such a system based on the financial budget, environmental characteristics, and seasonal variations. Interestingly, the source of energy is correlated with the energy expenditure of the UAVs, since strong winds cause UAV hovering to become increasingly energy-hungry. The aim is to maximize the cost efficiency of coverage at a particular location, which is a combinatorial optimization problem for dimensioning of the multivariate energy generation system under non-convex criteria. We have devised a customized algorithm by lowering the processing complexity and reducing the solution space through sampling. Evaluation is done with condensed real-world data on wind, solar energy, and traffic load per unit area, driven by vendor-provided prices. The implementation was tested in four locations, with varying wind or solar intensity. The best results were achieved in locations with mild wind presence and strong solar irradiation, while locations with strong winds and low solar intensity require higher Capital Expenditure (CAPEX) allocation.
翻译:无人驾驶航空飞行器(无人驾驶航空飞行器)群群往往需要在离网情景下出现,如灾害爆发、战乱或农村地区,无人驾驶航空飞行器无法进入电网,而是依赖可再生能源。考虑到由风能和太阳能两种可再生能源组成的主要电池,我们根据财政预算、环境特点和季节差异而扩大这种系统的规模。有趣的是,能源来源与无人驾驶飞行器的能源支出相关,因为强风导致UAV盘旋日益成为能源饥饿。目的是在特定地点实现覆盖成本效率最大化,因为那里无人驾驶航空飞行器无法进入电网,而是依赖可再生能源。在非电流标准下,多变量能源发电系统的尺寸是一个组合优化问题。我们设计了一个定制的算法,通过降低处理复杂程度和通过取样减少溶液空间。在供应商提供的价格驱动下,对每个单位地区的风力、太阳能和交通负荷的冷却数据进行了评估。实施工作在四个地点进行了测试,风能或太阳能强度各不相同。最佳效果是在温低风度和高度分配的情况下,在太阳能密集度高空域进行。