Performance Comparison of Optimized Resource Allocation in CoMP LTE-A using Iterative Subgradient Method and PSO

Inter-cell interference (ICI) is a primary factor that limits the capacity of any wireless cellular system. Recently, base stations cooperation (i.e., CoMP LTEAdvanced) is seen as a promising way to reduce ICI through coordination. In CoMP, the allocation of crucial resources such as subcarrier and power should be carefully determined because the resource allocation strategies of cells in CoMP affect each other’s performance. Our previous work proposed an optimized resource allocation (ORA) scheme that maximizes proportional fairness utility subject to user minimum rate requirement and available base station (BS) power constraints. Selecting appropriate tool to solve any optimization problem is very important because different tools may results in different level of complexity, convergence rate and searching capabilities. In this paper, we carry out simulation study to investigate the performance of our ORA scheme by using different optimization tools. The first tool is iterative subgradient method and the second tool is metaheuristics optimization algorithm, namely Particle Swarm Optimization (PSO). Based on numerical results, PSO gives the best performance in terms of mean values, standard deviations and processing time compared to iterative subgradient algorithm

Using a Hybrid Evolutionary Algorithm for Solving Signal Transmission Station Location and Allocation Problem with Different Regional Communication Quality Restriction

This study aims to investigate the signal transmission station location-allocation problems with the various restricted regional constraints. In each constraint, the types of signal transmission stations and the corresponding numbers and locations are to be decided at the same time. Inappropriate set up of stations is not only causing the unnecessary cost but also making the poor service quality. In this study, we proposed a hybrid evolutionary approach integrating the immune algorithm with particle swarm optimization (IAPSO) to solve this problem where each of the regions is with different maximum failure rate restrictions. We compared the performance of the proposed method with commercial optimization software LINGO®. According to the experimental results, solutions obtained by our IAPSO are better than or as well as the best solutions obtained by LINGO®. It is expected that our research can provide the telecommunication enterprise the optimal/near-optimal strategies for the setup of signal transmission stations

Distributed drone base station positioning for emergency cellular networks using reinforcement learning

Due to the unpredictability of natural disasters, whenever a catastrophe happens, it is vital that not only emergency rescue teams are prepared, but also that there is a functional communication network infrastructure. Hence, in order to prevent additional losses of human lives, it is crucial that network operators are able to deploy an emergency infrastructure as fast as possible. In this sense, the deployment of an intelligent, mobile, and adaptable network, through the usage of drones—unmanned aerial vehicles—is being considered as one possible alternative for emergency situations. In this paper, an intelligent solution based on reinforcement learning is proposed in order to find the best position of multiple drone small cells (DSCs) in an emergency scenario. The proposed solution’s main goal is to maximize the amount of users covered by the system, while drones are limited by both backhaul and radio access network constraints. Results show that the proposed Q-learning solution largely outperforms all other approaches with respect to all metrics considered. Hence, intelligent DSCs are considered a good alternative in order to enable the rapid and efficient deployment of an emergency communication network

Dynamically Energy-Efficient Resource Allocation in 5G CRAN Using Intelligence Algorithm

5G network is the next generation for cellular networks to overcome the challenges and limitations of the 4G network.  Cloud Radio Access Network(C-RAN) is providing solutions for cost-efficient and power-efficient solutions for the 5G network.   The aim of this paper proposed an energy-efficient C-RAN to minimize the cost of the network by dynamically allocating BBU resources to RRHs as per facing traffic, and also minimize the energy consumption of centralized BBU resources that affect dynamically allocate of RRHs.  Particle Swarm Optimization (PSO) algorithm is a Swarm Intelligence algorithm for optimization of mapping between BBU-RRH for resource allocation in C-RAN.  The main objective of the paper is as per resource usage in C-RAN the BBU is put in the active or in-active mode to minimize energy consumption in C-RAN of 5G technology. As per our proposed C-RANapplication, the proposed PSO algorithm 90% minimizes energy consumption and maximizes energy efficiency compared with existing work

Reduction of Power Losses in the Distribution System by Controlling Tap Changing Transformer using the PSO Algorithm

Energy is an essential commodity for everyone, with electrical energy being the most preferred form. Unfortunately, non-renewable energy resources are gradually depleting, and renewable energy sources take several years to establish. To mitigate this problem, technology has shifted from non-renewable energy sources to electrical devices and machines, including household appliances like washing machines and air conditioners. However, the generation of electricity is still inadequate to meet the growing demand. This leads to two critical issues: Excessive power loss and inadequate voltage stability, making it difficult for power distribution companies to ensure a consistent and reliable power supply. The objective of this study is to tackle the issue of reduction and minimization of power dissipation By employing the PSO technique, adjusting the transformer tap settings. The proposed approach uses the 14-bus system as a reference and calculates losses for this system using the backward-forward sweeping technique

Validation of optimal electric vehicle charging station allotment on IEEE 15-bus system

Introduction. The diminishing conventional energy resources and their adverse environmental impacts compelled the researchers and industries to move towards the nonconventional energy resources. Consequently, a drastic paradigm shift is observed in the power and transportation sectors from the traditional fossil fuel based to the renewable energy-based technologies. Considering the proliferation of electric vehicles, the energy companies have been working continuously to extend electric vehicle charging facilities. Problem. Down the line, the inclusion of electric vehicle charging stations to the electric grid upsurges the complication as charging demands are random in nature all over the grid, and in turn, an unplanned electric vehicle charging station installation may cause for the system profile degradation. Purpose. To mitigate the problem, optimum allocation of the charging stations in existing power distribution system in a strategic manner is a matter of pronounced importance in maintaining the system stability and power quality. In this paper, optimum allocation of electric vehicle charging stations in IEEE 15-bus system is studied in order to minimize the highest over and under voltage deviations. Methodology. Primarily, voltage stability analysis is carried out for identification of the suitable system nodes for the integration. Voltage sensitivity indices of all the system nodes are calculated by introducing an incremental change in reactive power injection and noting down the corresponding change in node voltage for all nodes. Henceforth, dynamic load-flow analysis is performed using a fast and efficient power flow analysis technique while using particle swarm optimization method in finding the optimal locations. Results. The results obtained by the application of the mentioned techniques on IEEE 15-bus system not only give the optimum feasible locations of the electric vehicle charging stations, but also provide the maximum number of such charging stations of stipulated sizes which can be incorporated while maintaining the voltage profile. Originality. The originality of the proposed work is the development of the objective function; voltage stability analysis; power flow analysis and optimization algorithms. Practical value. The proposed work demonstrates the detailed procedure of optimum electric vehicle charging station allotment. The experimental results can be used for the subsequent execution in real field.Вступ. Зменшення традиційних енергетичних ресурсів та їх несприятливий вплив на навколишнє середовище змусили дослідників і галузі промисловості перейти до нетрадиційних енергетичних ресурсів. Отже, в енергетичному та транспортному секторах спостерігається кардинальна зміна парадигми від традиційного викопного палива до технологій, що базуються на відновлюваних джерелах енергії. Беручи до уваги розповсюдження електромобілів, енергетичні компанії постійно працюють над розширенням потужностей для зарядки електромобілів. Проблема. Включення зарядних станцій для електромобілів до електричної мережі викликає ускладнення, оскільки вимоги до зарядки мають випадковий характер по всій електромережі, і, в свою чергу, незапланована установка зарядної станції для електромобілів може призвести до погіршення профілю системи. Мета. Щоб полегшити проблему, оптимальне розміщення зарядних станцій в існуючій системі розподілу електроенергії стратегічним чином є питанням надзвичайно важливого значення для підтримки стабільності системи та якості електроенергії. У цій роботі вивчається оптимальне розміщення зарядних станцій для електричних транспортних засобів в 15-шинній системі IEEE з метою мінімізації найвищих відхилень напруги вгору та донизу. Методологія. В першу чергу, проводиться аналіз стабільності напруги для ідентифікації відповідних вузлів системи для інтеграції. Показники чутливості до напруги всіх вузлів системи обчислюються шляхом введення поступової зміни подачі реактивної потужності та відмітки відповідної зміни вузлової напруги для всіх вузлів. Надалі динамічний аналіз потоку навантаження виконується за допомогою швидкого та ефективного методу аналізу потоку потужності, використовуючи метод оптимізації рою частинок для пошуку оптимальних місць розташування. Результати. Результати, отримані при застосуванні зазначених методів на 15-шинній системі IEEE, не тільки дають оптимально можливе розташування зарядних станцій електромобілів, але також забезпечують максимальну кількість таких зарядних станцій встановлених розмірів, які можна включити, зберігаючи профіль напруги. Оригінальність. Оригінальність запропонованої роботи полягає у розвитку цільової функції; у аналізі стабільності напруги; у алгоритмах аналізу та оптимізації потоку потужності. Практичне значення. Запропонована робота демонструє детальну процедуру оптимального розподілу станцій зарядки електромобілів. Результати експериментів можуть бути використані для подальшої реалізації в реальних умовах

Electric Vehicle Charging Load Allocation at Residential Locations Utilizing the Energy Savings Gained by Optimal Network Reconductoring

In this study, a two-stage methodology based on the energy savings gained by optimal network reconductoring was developed for the sizing and allocation of electric vehicle (EV) charging load at the residential locations in urban distribution systems. During the first stage, the Flower Pollination Algorithm (FPA) was applied to minimize the annual energy losses of the radial distribution system through optimum network reconductoring. A multi-objective function was formulated to minimize investment, peak loss, and annual energy loss costs at different load factors. The results obtained with the flower pollination algorithm were compared with the particle swarm optimization algorithm. In the second stage, a simple heuristic procedure was developed for the sizing and allocation of EV charging load at every node of the distribution system utilizing part of the annual energy savings obtained by optimal network reconductoring. The number of electric cars, electric bikes, and electric scooters that can be charged at every node was computed while maintaining the voltage and branch current constraints. The simulation results were demonstrated on 123 bus and 51 bus radial distribution networks to validate the effectiveness of the proposed methodology