The rise of renewables coincides with the shift towards Electrical Vehicles
(EVs) posing technical and operational challenges for the energy balance of the
local grid. Nowadays, the energy grid cannot deal with a spike in EVs usage
leading to a need for more coordinated and grid aware EVs charging and
discharging strategies. However, coordinating power flow from multiple EVs into
the grid requires sophisticated algorithms and load-balancing strategies as the
complexity increases with more control variables and EVs, necessitating large
optimization and decision search spaces. In this paper, we propose an EVs fleet
coordination model for the day ahead aiming to ensure a reliable energy supply
and maintain a stable local grid, by utilizing EVs to store surplus energy and
discharge it during periods of energy deficit. The optimization problem is
addressed using Harris Hawks Optimization (HHO) considering criteria related to
energy grid balancing, time usage preference, and the location of EV drivers.
The EVs schedules, associated with the position of individuals from the
population, are adjusted through exploration and exploitation operations, and
their technical and operational feasibility is ensured, while the rabbit
individual is updated with a non-dominated EV schedule selected per iteration
using a roulette wheel algorithm. The solution is evaluated within the
framework of an e-mobility service in Terni city. The results indicate that
coordinated charging and discharging of EVs not only meet balancing service
requirements but also align with user preferences with minimal deviations.

本文提出了一种适用于日前期望保证可靠能量供应和维持稳定局部电网的 EV 车队协调模型，利用 EV 来储存多余能量，并在能量不足时释放，结果表明，EV 的协调充电和放电不仅满足平衡服务要求，还与用户偏好基本吻合。

利用多目标哈里斯雀优化进行电动车协调以实现电网平衡

Electric Vehicles coordination for grid balancing using multi-objective  Harris Hawks Optimization

As the integration of unmanned aerial vehicles (UAVs) into visible light
communications (VLC) can offer many benefits for massive-connectivity
applications and services in 5G and beyond, this work considers a UAV-assisted
VLC using non-orthogonal multiple-access. More specifically, we formulate a
joint problem of power allocation and UAV's placement to maximize the sum rate
of all users, subject to constraints on power allocation, quality of service of
users, and UAV's position. Since the problem is non-convex and NP-hard in
general, it is difficult to be solved optimally. Moreover, the problem is not
easy to be solved by conventional approaches, e.g., coordinate descent
algorithms, due to channel modeling in VLC. Therefore, we propose using harris
hawks optimization (HHO) algorithm to solve the formulated problem and obtain
an efficient solution. We then use the HHO algorithm together with artificial
neural networks to propose a design which can be used in real-time applications
and avoid falling into the "local minima" trap in conventional trainers.
Numerical results are provided to verify the effectiveness of the proposed
algorithm and further demonstrate that the proposed algorithm/HHO trainer is
superior to several alternative schemes and existing metaheuristic algorithms.

本文提出了一种利用无人机的可见光通信辅助非正交多址接入的方法，通过使用 Harris 鹰优化算法和人工神经网络，实现了实时应用并避免了落入传统训练器的 “局部最小值” 陷阱，证明了该算法 / HHO 训练器优于其他方案和现有元启发式算法。