Optimal sizing of a hybrid photovoltaic/fuel cell grid-connected power system including hydrogen storage

The global energy demand is enormous, yet nonrenewable resources such as fossil fuels, and nuclear power are insufficient to satisfy it. Renewable energy will eventually be the better option. This study investigates the design and optimization of a hybrid photovoltaic / fuel cell (PV/FC) energy system with an H2 tank linked to the grid. The primary objective of this research is to design and size a PV/FC energy system with an H2 storage tank to supply the energy needs of a university ICT center that is connected to an inconsistent grid. HOMER's energy-balance algorithms were used to determine the best design architecture. Using mean solar radiation data (22 years) obtained for the University of Benin ICT Center, hourly simulations were performed to determine the optimum configuration in terms of size, cost, and performance of the energy system. Findings revealed that a hybrid PV/FC power system with a 400 kW solar array, a 250 kW FC, a 240 kW inverter, and a 150 kW electrolyzer with an H2 tank of 700 kg will reliably supplement the inconsistent grid with a high proportion (92%) of renewable resources at $0.1052/kWh. An energy cost reduction of approximately 88 percent and a return on investment of 200 percent with a present value of$98,251,110 could be obtained in less than 2 years over the traditional grid/diesel systems. Using an ideally sized PV/FC hybrid system will alleviate Nigeria's electrical challenges, impeding the country's economic growth. Furthermore, hybrid PV/FC power systems can reduce CO2 emissions, resulting in a more environmentally friendly and sustainable environment

Optimal sizing of a hybrid photovoltaic/fuel cell grid-connected power system including hydrogen storage

The global energy demand is enormous, yet nonrenewable resources such as fossil fuels, and nuclear power are insufficient to satisfy it. Renewable energy will eventually be the better option. This study investigates the design and optimization of a hybrid photovoltaic / fuel cell (PV/FC) energy system with an H2 tank linked to the grid. The primary objective of this research is to design and size a PV/FC energy system with an H2 storage tank to supply the energy needs of a university ICT center that is connected to an inconsistent grid. HOMER's energy-balance algorithms were used to determine the best design architecture. Using mean solar radiation data (22 years) obtained for the University of Benin ICT Center, hourly simulations were performed to determine the optimum configuration in terms of size, cost, and performance of the energy system. Findings revealed that a hybrid PV/FC power system with a 400 kW solar array, a 250 kW FC, a 240 kW inverter, and a 150 kW electrolyzer with an H2 tank of 700 kg will reliably supplement the inconsistent grid with a high proportion (92%) of renewable resources at $0.1052/kWh. An energy cost reduction of approximately 88 percent and a return on investment of 200 percent with a present value of$98,251,110 could be obtained in less than 2 years over the traditional grid/diesel systems. Using an ideally sized PV/FC hybrid system will alleviate Nigeria's electrical challenges, impeding the country's economic growth. Furthermore, hybrid PV/FC power systems can reduce CO2 emissions, resulting in a more environmentally friendly and sustainable environment

Microgrid, Its Control and Stability: The State of The Art

Some of the challenges facing the power industries globally include power quality and stability, diminishing fossil fuel, climate change amongst others. The use of distributed generators however is growing at a steady pace to address these challenges. When interconnected and integrated with storage devices and controllable load, these generators operate together in a grid, which has incidental stability and control issues. The focus of this paper, therefore, is on the review and discussion of the different control approaches and the hierarchical control on a microgrid, the current practice in the literature concerning stability and the control techniques deployed for microgrid control; the weakness and strength of the different control strategies were discussed in this work and some of the areas that require further research are highlighted

Model predictive control based on mixed H2/H∞ control approach for active vibration control of railway vehicles

This paper investigates the application of Model Predictive Control (MPC) technology based on mixed H2/H1 control approach for active suspension control of a railway vehicle, the aim being to improve the ride quality of the railway vehicle. Comparisons are made with more conventional control approaches, and the applicability of the linear matrix inequality approach is illustrated via the railway vehicle example

Model Predictive Control of Uncertain Constrained Linear System Based on Mixed ℋ 2

Uncertain constrained discrete-time linear system is addressed using linear matrix inequality based optimization techniques. The constraints on the inputs and states are specified as quadratic constraints but are formulated to capture hyperplane constraints as well. The control action is of state feedback and satisfies the constraints. Uncertainty in the system is represented by unknown bounded disturbances and system perturbations in a linear fractional transform (LFT) representation. Mixed ℋ2/ℋ∞ method is applied in a model predictive control strategy. The control law takes account of disturbances and uncertainty naturally. The validity of this approach is illustrated with two examples

Design and implementation of a deep neural network approach for intrusion detection systems

Due to the increased reliance of many a human system on cyber infrastructures, there is the continuous need for intelligent protection schemes to be developed as the spate of cyber-attacks is on the rise. Such intelligent systems that could easily detect both existing and zero-day attacks are highly sought in the complex and fast evolving cyberspace. In this regard, machine learning as well as deep learning models have been very effective. However, there is the need for better performing models than the existing ones. This work is aimed at the design and implementation of a Deep Neural Network model for detecting intrusions in computer networks. Techniques such as SMOTE and Random Sampling were applied to handle data imbalance in the CICIDS 2017 dataset. The entire experiment was carried out on a single Jupyter notebook in the Google Colaboratory environment where relevant software libraries such as seaborn, pandas, matplotlib, keras, and Tensor Flow were imported and thereafter implemented as required. Performance metrics of accuracy and loss at both training and validation of the model were considered. Results show that the deep learning model was excellent at predicting attacks with the CICIDS 2017 dataset, achieving accuracy score of 99.68 % and loss of 0.0102

Model predictive control based on mixed ℋ2/ℋ∞control approach for active vibration control of railway vehicles

This paper investigates the application of model predictive control technology based on mixed H2/H-inf control approach for active suspension control of a railway vehicle, the aim being to improve the ride quality of the railway vehicle. Comparisons are made with more conventional control approaches, and the applicability of the linear matrix inequality approach is illustrated via the railway vehicle exampl