Optimal Allocation Of Distributed Renewable Energy Sources In Power Distribution Networks

In this dissertation study, various methods for optimum allocation of renewable distributed generators (DGs) in both balanced and unbalanced distribution networks have been proposed, developed, and tested. These methods were developed with an objective of maximizing several advantages of DG integration into the current distribution system infrastructure. The first method addressed the optimal sitting and sizing of DGs for minimum distribution power losses and maximum voltage profile improvement of distribution feeders. The proposed method was validated by comparing the results of a balanced distribution system with those reported in the literature. This method was then implemented in a co-simulation environment with Electric Power Research Institute\u27s (EPRI) OpenDSS program to solve a three phase optimal power flow (TOPF) problem for optimal location and sizing of multiple DGs in an unbalanced IEEE-123 node distribution network. The results from this work showed that the better loss reduction can be achieved in less computational time compared to the repeated load flow method. The second and third methods were developed with the goal of maximizing the reliability of distribution networks by optimally sitting and sizing DGs and reclosers in a distribution network. The second method focused on optimal allocation of DGs and reclosers with an objective of improving reliability indices while the third method demonstrated the cost based reliability evaluation. These methods were first verified by comparing the results obtained in a balanced network with those reported in literature and then implemented on a multi-phase unbalanced network. Results indicated that optimizing reclosers and DGs based on the reliability indices increases the total cost incurred by utilities. Likewise, when reclosers and DG were allocated to reduce the total cost, the reliability of the distribution system decreased. The fourth method was developed to reduce the total cost incurred by utilities while integrating DGs in a distribution network. Various significant issues like capital cost, operation and maintenance cost, customer service interruption cost, cost of the power purchased from fossil fuel based power plants, savings due to the reduction in distribution power losses, and savings on pollutant emissions were included in this method. Results indicated that integrating DGs to meet the projected growth in demand provides the maximum return on the investment. Additionally, during this project work an equivalent circuit model of a 1.2 kW PEM fuel cell was also developed and verified using electro impedance spectroscopy. The proposed model behaved similar to the actual fuel cell performance under similar loading conditions. Furthermore, an electrical interface between the geothermal power plant and an electric gird was also developed and simulated. The developed model successfully eliminated major issues that might cause instability in the power grid. Furthermore, a case study on the evaluation of geothermal potential has been presented

A Genetic Algorithm Approach to Optimal Sizing and Placement of Distributed Generation on Nigerian Radial Feeders

Mitigating power loss and voltage profile problems on radial distribution networks has been a major challenge to distribution system operators. While deployment of distributed generation, as compensators, has made a suitable solution option, optimum placement and sizing of the compensators has been a concern and it has thus been receiving great attention. Meta-heuristic algorithms have been found efficacious in this respect, yet the use of the algorithms in addressing problems of radial feeders is still comparatively low in Nigeria where analytical and numerical programming methods are common. Hence; the use of genetic algorithm to site and size distributed generator for real-time power loss reduction and voltage profile improvement on the Nigerian secondary distribution networks is presented. Backward-forward sweep load flow analysis, together with loss sensitivity factor, is deployed to identify the buses suitable for the installation of the distributed generation, while the algorithm is employed in estimating the optimum size. This approach is tested on the standard IEEE 15-bus system and validated using a Nigerian 11 kV feeder. The result obtained on the IEEE test system shows 183 kW loss using the compensator, as compared to 436 kW loss without the compensator; while on the Nigerian network the loss with the compensator was 4.99 kW, in comparison with no-compensation loss of 10.47kW. By the approach of this study, real power loss on the Nigerian feeder decreased by 52.3% together with energy cost reduction from N658,789.12 to N314,227.38. Likewise the minimum bus voltage magnitude and the voltage stability index of the network are improved to acceptable limits. This approach is therefore recommended as capable of strengthening the performance of the Nigerian radial distribution system

Hypervolume Sen Task Scheduilng and Multi Objective Deep Auto Encoder based Resource Allocation in Cloud

Cloud Computing (CC) environment has restructured the Information Age by empowering on demand dispensing of resources on a pay-per-use base. Resource Scheduling and allocation is an approach of ascertaining schedule on which tasks should be carried out. Owing to the heterogeneity nature of resources, scheduling of resources in CC environment is considered as an intricate task. Allocating best resource for a cloud request remains a complicated task and the issue of identifying the best resource – task pair according to user requirements is considered as an optimization issue. Therefore the main objective of the Cloud Server remains in scheduling the tasks and allocating the resources in an optimal manner. In this work an optimized task scheduled resource allocation model is designed to effectively address  large numbers of task request arriving from cloud users, while maintaining enhanced Quality of Service (QoS). The cloud user task requests are mapped in an optimal manner to cloud resources. The optimization process is carried out using the proposed Multi-objective Auto-encoder Deep Neural Network-based (MA-DNN) method which is a combination of Sen’s Multi-objective functions and Auto-encoder Deep Neural Network model. First tasks scheduling is performed by applying Hypervolume-based Sen’s Multi-objective programming model. With this, multi-objective optimization (i.e., optimization of cost and time during the scheduling of tasks) is performed by means of Hypervolume-based Sen’s Multi-objective programming. Second, Auto-encoder Deep Neural Network-based Resource allocation is performed with the scheduled tasks that in turn allocate the resources by utilizing Jensen–Shannon divergence function. The Jensen–Shannon divergence function has the advantage of minimizing the energy consumption that only with higher divergence results, mapping is performed, therefore improving the energy consumption to a greater extent. Finally, mapping tasks with the corresponding resources using Kronecker Delta function improves the makespan significantly. To show the efficiency of Multi-objective Auto-encoder Deep Neural Network-based (MA-DNN) cloud time scheduling and optimization between tasks and resources in the CC environment, we also perform thorough experiments on the basis of realistic traces derived from Personal Cloud Datasets. The experimental results show that compared with RAA-PI-NSGAII and DRL, MA-DNN not only significantly accelerates the task scheduling efficiency, task scheduling time but also reduces the energy usage and makespan considerably

EQUAL: Energy and QoS Aware Resource Allocation Approach for Clouds

The popularity of cloud computing is increasing by leaps and bounds. To cope with resource demands of increasing number of cloud users, the cloud market players establish large sized data centers. The huge energy consumption by the data centers and liability of fulfilling Quality of Service (QoS) requirements of the end users have made resource allocation a challenging task. In this paper, energy and QoS aware resource allocation approach which employs Antlion optimization for allocation of resources to virtual machines (VMs) is proposed. It can operate in three modes, namely power aware, performance aware, and balanced mode. The proposed approach enhances energy efficiency of the cloud infrastructure by improving the utilization of resources while fulfilling QoS requirements of the end users. The proposed approach is implemented in CloudSim. The simulation results have shown improvement in QoS and energy efficiency of the cloud

A theoretical and computational basis for CATNETS

The main content of this report is the identification and definition of market mechanisms for Application Layer Networks (ALNs). On basis of the structured Market Engineering process, the work comprises the identification of requirements which adequate market mechanisms for ALNs have to fulfill. Subsequently, two mechanisms for each, the centralized and the decentralized case are described in this document. These build the theoretical foundation for the work within the following two years of the CATNETS project. --Grid Computing