Showing papers by "Kennedy Chinedu Okafor published in 2019"

Towards Locational Marginal Pricing Model for Nigerian Electricity Tariff Structure using Optimal Power Flow Computation

Abstract: In the Nigerian deregulated electricity market, competition and economic pricing of electricity in the long term stage is fundamental. A transparent and predictable pricing structure of electricity is also needed to provide useful information to market participants (such as generation companies, transmission companies and customers). The existing methodology for setting electricity tariff using the Multi-year tariff order (MYTO) is perceived to be less efficient in reflecting true cost of electricity and in sending correct price signals. It also relies on long run marginal cost method. Motivated by these concerns, this paper employed a locational marginal pricing (LMP) model that is based on minimizing the cost of supply of electricity to a location when there is an increment in the load clusters. A linear programming approach incorporating DC and AC optimal power flow (OPF) model is carried out. Model implementation was done using the 330kV Nigerian grid network within three-tier cases viz: Case 1- LMP value under normal conditions (No constraint enforcement), Case 2: LMP value considering Congestion/Transmission Limit and Case 3: LMP value considering losses. The result from the three cases considered shows that the LMP varies at various locations owing to transmission congestion constraint and transmission losses while the final total cost of supply of electricity varies. With the removal of fixed charges in the electricity tariff, electricity should be priced based on locations to reflect correct price list to the market stakeholders.

Towards Cooperative Power Pooling for Heterogeneous Load Clusters

Abstract: In this paper, cooperative power management (CPM) for heterogeneous load clusters is presented. Load flow (LF) and continuation power flow (CPF) characterization are introduced to describe the behavior of the network in static form. The essence is to guide on size of generator to consider and to determine excess power remaining without collapsing the network after meeting satisfactory supply demand to the cooperative members. The considered entities of the proposed cooperative power pooling system (CPPS) include: (i) electricity consumer cooperatives, (ii) CPPS criteria (iii) load flow model iv) continuation power flow (CPF); (v) power dispatch model and costing (PDMC), (vi) consumer cooperative with pool of generators model, vii) consumer cooperative with purchase of central generator, (viii) Algorithm for the pool of generator and the consumer cooperative (with pool of generators) and willing to sell out excess power model (CWSPM). These are employed in modeling demand response needed to tackle price distribution of the network using metering system. Consequently, the system provides fairness on charges based on load consumed while managing the members effectively.

Intelligent Symbiotic Relay Selection Technique for 5G Networks

Abstract: The growing demand for bandwidth and spectrum has inspired the ongoing efforts to establish the future 5G network supporting vertical sectors such as cyber-physical systems (CPS). Cooperative communication is one of the requisite techniques to improve coverage, network capacity and reduce power consumption in the network. In this paper, a symbiotic two-phase intelligent transmission is considered. The first phase occurs between the source and the candidate relays, and involves the selection of a set of “reliable relays”. The second phase occurs between the reliable relays and the destination, and involves the selection of the “best relay” for transmission. Dynamic relay selection using k-means clustering is used to detect the most significant correlation between all the channel state information (CSI) attributes in the system. The work identified the reliable relays while reducing the number of relay nodes for the second transmission phase. Contextual scenarios are created with typical network configuration using three geographical locations Coventry, Birmingham and London. An experimental validation is done with Omnet++ environment for the scenarios of three geographical locations. A natural grouping of mobile users is carried out leveraging the relay capabilities. The results are validated using support vector machine (SVM) classification algorithm. Considering urban environment deployment of relay nodes, metrics such as signal-to-noise-plus-interference ratio (SINR), attenuation, signal to noise ratio (SNR), link quality, k-means clustering, accuracy, and root mean square error (RMSE) are investigated for the Direct-2-Direct (D2D) capable relays. It was observed that the proposed technique both outperforms the other fixed-parameter relay selection techniques and improves with larger datasets unlike the other techniques.

Localisation of Zones of Cancer Detection in Prostate Gland Using Ratio Matrix and Radial Scanning of 2D Trans-rectal Ultrasound Images;

Abstract: Researchers have continued to proffer various solutions to the challenge of delineating from Trans-rectal ultrasound (TRUS) 2D-images of the prostate the regions of desired property. This paper presents an algorithm that categorises the detected regions suspected to be cancerous, hyper-echoic pixels, in the prostate gland from a 2D Trans-rectal Ultrasound images into three zones. The developed algorithm uses radial scanning of the pixels of the prostate gland image from common seed point both to detect and delineate the suspected cancerous pixels into zones, namely peripheral, transition and central, by applying ratios of the anatomical zones of the prostate gland. Expert knowledge, intensity and gradient features were implemented to delineate regions of interest. MATLAB programming tool was used for creating the codes that implemented the algorithms. Samples of TRUS 2D-images of the prostate for patients with raised PSA values (>10 ng/ml) used in a previous work by Award (2007) were used for testing the algorithm. The test results showed that the algorithm could detect zones of the prostate boundary exhibit image properties for cancer cells and also the percentage of malignancy detected in zones agreed with existing research findings. Comparison of detection results with that of an expert radiologist yielded the following performance parameters; accuracy of 88.55% and sensitivity of 71.65%.

Towards Smart Green Energy Metering Design for OpenStack/Amazon Elastic Cloud Integration

Abstract: Energy demand in most African countries is insufficient thereby hindering economic development. By leveraging renewable technologies, the control and management of renewable micro-grids requires a robust/reliable design that supports smart integration via Cloud for dynamic service delivery. Motivated by this concern, this paper developed smart green energy management system (SGEMS) for optimizing demand side management (DSM) in renewable micro-grids. The architecture employs solar photovoltaic (PV) to generate energy and meters user consumption pattern. SGEMS global model for solar PV metering platform is introduced including structured OpenStack Cloud application with active load-balancers, Trove/Hadoop Bigdata application program interface (API), OpenFlow firewall, and dynamic network scaling. These features manage user access with little computational overhead. An experimental demonstration of OpenStack/Amazon EC2 instance scenarios for transactional workload is briefly highlighted. The work discussed the usefulness of OpenStack engine as a distributed API middleware for SGEMS-energy application running on AWS EC2.