Multi-Objective Comprehensive Charging/Discharging Scheduling Strategy for Electric Vehicles Based on the Improved Particle Swarm Optimization Algorithm

To solve the problems that a large number of random and uncontrolled electric vehicles (EVs) connecting to the distribution network, resulting in a decrease in the performance and stability of the grid and high user costs, in this study, a multi-objective comprehensive charging/discharging scheduling strategy for EVs based on improved particle swarm optimization (IPSO) is proposed. In the distribution network, the minimum root-mean-square error and the minimum peak valley difference of system load are first designed as objective functions; on the user side, the lowest charge and discharge cost of electric vehicle users and the lowest battery loss cost are used as objective functions, then a multi-objective optimization scheduling model for EVs is established, and finally, the optimization through IPSO is performed. The simulation results show that the proposed method is effective, which enhances the peak regulating capacity of the power grid, and it optimizes the system load and reduces the user cost compared with the conventional methods.

Resource Management and 3D Deployment in Power-Efficient Multi-UAV Networks

In this paper, the scenario in which multiple unmanned aerial vehicles (UAVs) provide service to ground users is considered. Under the condition of satisfying the minimum rate per user and system load balance, the user association, bandwidth allocation and three dimensional (3D) deployment of multi-UAV networks are optimized jointly to minimize the total downlink transmit power of UAVs. Since the problem is hard to solve directly, it is decomposed into three sub-problems, and then the problem is solved by alternating iteration algorithm. First, when the UAV’s location is determined, a modified K-means algorithm is used to obtain balanced user clustering. Then, when the user association and UAV’s 3D deployment are determined, the convex optimization method is used to obtain the optimal bandwidth allocation. Finally, when the user association and optimal bandwidth allocation are determined, a modified differential evolution algorithm is proposed to optimize the 3D location of the UAVs. Simulation results show that the proposed algorithm can effectively reduce the transmit power of UAVs compared with the existing algorithms under the conditions of satisfying the minimum rate of ground users and system load balance.

Reducing System Load of Effective Video Using a Network Model

Recently, as non-face-to-face work has become more common, the development of streaming services has become a significant issue. As these services are applied in increasingly diverse fields, various problems are caused by the overloading of systems when users try to transmit high-quality images. In this paper, SRGAN (Super Resolution Generative Adversarial Network) and DAIN (Depth-Aware Video Frame Interpolation) deep learning were used to reduce the overload that occurs during real-time video transmission. Images were divided into a FoV (Field of view) region and a non-FoV (Non-Field of view) region, and SRGAN was applied to the former, DAIN to the latter. Through this process, image quality was improved and system load was reduced.

On the Impact of Treatment Restrictions for the Indigent Suffering from a Chronic Disease: The Case of Compassionate Dialysis

We analyze a congested healthcare delivery setting resulting from emergency treatment of a chronic disease on a regular basis. A prominent example of the problem of interest is congestion in the emergency room (ER) at a publicly funded safety net hospital resulting from recurrent arrivals of uninsured end-stage renal disease patients needing dialysis (a.k.a. compassionate dialysis). Unfortunately, this is the only treatment option for un/under-funded patients (e.g., undocumented immigrants) with ESRD, and it is available only when the patient’s clinical condition is deemed as life-threatening after a mandatory protocol, including an initial screening assessment in the ER as dictated and communicated by hospital administration and county policy. After the screening assessment, the so-called treatment restrictions are in place, and a certain percentage of patients are sent back home; the ER, thus, serves as a screening stage. The intention here is to control system load and, hence, overcrowding via restricting service (i.e., dialysis) for recurrent arrivals as a result of the chronic nature of the underlying disease. In order to develop a deeper understanding of potential unintended consequences, we model the problem setting as a stylized queueing network with recurrent arrivals and restricted service subject to the mandatory screening assessment in the ER. We obtain analytical expressions of fundamental quantitative metrics related to network characteristics along with more sophisticated performance measures. The performance measures of interest include both traditional and new problem-specific metrics, such as those that are indicative of deterioration in patient welfare because of rejections and treatment delays. We identify cases for which treatment restrictions alone may alleviate or lead to severe congestion and treatment delays, thereby impacting both the system operation and patient welfare. The fundamental insight we offer is centered around the finding that the impact of mandatory protocol on network characteristics as well as traditional and problem-specific performance measures is nontrivial and counterintuitive. However, impact is analytically and/or numerically quantifiable via our approach. Overall, our quantitative results demonstrate that the thinking behind the mandatory protocol is potentially naive. This is because the approach does not necessarily serve its intended purpose of controlling system-load and overcrowding.

A new method to incorporate three-phase power transformer model into distribution system load flow analysis

This paper proposes a new and simple method to incorporate three-phase power transformer model into distribution system load flow (DSLF) analysis. The objective of the present work is to find a robust and efficient technique for modeling and integrating power transformer in the DSLF analysis. The proposed transformer model is derived based on nodal admittance matrix and formulated by using the symmetrical component theory. Load flow formulation in terms of branch currents and nodal voltages is also proposed in this paper to enable integrating the model into the DSLF analysis. Singularity that makes the calculations in forward/backward sweep (FBS) algorithm is difficult to be carried out. It can be avoided in the method. The proposed model is verified by using the standard IEEE test system.