Abstract
Electric vehicles (EVs) promote many advantages for distribution systems such as increasing efficiency and reliability, decreasing dependence on non-endogenous resources, and reducing pollutant emissions. Due to increased proliferation of EVs and their integration in power systems, management and operation of distribution systems (ODS) is becoming more important. Recent studies have shown that EV can increase power grid flexibility since EV owners do not use them for 93-96% of the daytime. Therefore, it is important to exploit parking time, during which EVs can act either as a load or distributed storage device, to maximize the benefit for the power system. Following a survey of the current state-of-the-art, this work studies the impact of EV charging on the load profile. Since renewable energy resources (RES) play a critical role in future distribution systems the current case study considered the presence of RES and their stochastic nature has been modeled. The study proceeds with analyzing EV owners' driving habits, enabling prediction of the network load profile. The impact of: EV charging modes (i.e., controlled and uncontrolled charging), magnitude of wind and photovoltaic (PV) generation, number of EVs (penetration), and driving patterns on the ODS is analyzed.

Abstract
The ionizing radiation is used in the nuclear medicine field during the execution of diagnosis exams. The administration of nuclear radio pharmaceutical components to the patient contaminates the environment. The main contribution of this work is to propose a path planning method for scanning the nuclear contaminated environment with a mobile robot optimizing the traveled distance. The Genetic Algorithm methodology is proposed and compared with other approaches and the final solution is validated in simulated and real environment in order to achieve a closer approximation to reality. © 2018, Springer International Publishing AG, part of Springer Nature.

Abstract
Develop cost-effective ground robots for crop monitoring in steep slope vineyards is a complex challenge. The terrain presents harsh conditions for mobile robots and most of the time there is no one available to give support to the robots. So, a fully autonomous steep-slope robot requires a robust automatic recharging system. This work proposes a multilevel system that monitors a vineyard robot autonomy, to plan off-line the trajectory to the nearest recharging point and dock the robot on that recharging point considering visual tags. The proposed system called VineRecharge was developed to be deployed into a cost-effective robot with low computational power. Besides, this paper benchmarks several visual tags and detectors and integrates the best one into the VineRecharge system. © Springer International Publishing AG 2018.

Abstract
The optimized operation of a building energy management system (BEMS) is of great significance to its operation security, economy, and efficiency. This paper proposes a day-ahead multiobjective optimization model for the BEMS under time-of-use price-based demand response (DR), which integrates building integrated photovoltaic with other generations to optimize the economy and occupants' comfort by the synergetic dispatch of source-load-storage. The occupants' comfort contains three aspects of the indoor environment: visual comfort; thermal comfort; and indoor air quality comfort. With the consideration of controllable load that could participate in DR programs, the balances among different energy styles, electric, thermal, and cooling loads are guaranteed during the optimized operation. YALMIP toolbox in MATLAB was applied to solve the optimization problem. Finally, a case study was conducted to validate the effectiveness and adaptability of the proposed model.

Abstract

Abstract
Increasing penetration of distributed energy resources in power distribution systems and appearing the flexible behavior of end-users based on demand response programs make the distribution layer of the power systems more active. In this way, energy transaction management through a decentralized manner could be an appropriate solution to improve the efficiency of energy trading in the distribution power networks. This paper proposes a decentralized method to manage energy flexibility by consumers based on a bottom-up approach in distributed power systems. Also, a 33-bus distribution network is considered to assess the performance of our proposed decentralized energy flexibility management model based on impacts of flexible behaviors of end-user and uncertainty of distribution lines to flow energy in the power network.