GM Shafiullah

A standalone microgrid in a remote area may frequently experience overloading due to lack of sufficient power generation and/or renewable-based over generation causing unacceptable voltage and frequency deviation, which in turn lead the microgrid to operate with less resiliency and reliability. Conventionally, such problems are alleviated by load shedding or renewable curtailment. Alternatively, such autonomously operating microgrid clusters in a certain geographical area can be provisionally connected to each other to enable power exchange among them to address the problems of overloading or overgeneration more efficiently and cost-effective way. The power exchange link among the microgrids can be of different types such as a three-phase ac, a single-phase ac, or a dc-link. Power electronic converters are required to interconnect such power exchange networks to the three-phase ac microgrids and control the power-sharing amongst them. Such arrangement is also essential to interconnect microgrid clusters to each other with proper isolation while maintaining autonomy if they are operating in different standards. In this chapter, the topologies, and structures of various forms of power exchange links are investigated and an appropriate framework is established under which power exchange will take place. This approach is a decentralized control mechanism to facilitate power-sharing amongst the converters of the neighboring microgrids without any data communication, that can be implemented at the primary level based on the localized measurements. The dynamic performance of the control mechanism for all the topologies is illustrated through simulation studies in PSIM® to verify that such overloading or overgeneration situations can be effectively alleviated through proper frequency regulation. The chapter also presents a comparative analysis of the topologies in terms of stability and sensitivity.

This study present a data-driven optimization framework for microgrid energy management (MEM). The framework considered demand response (DR) and uncertainties in power generation. The framework aims to improve the energy efficiency and reliability of microgrid (MG) operation by optimizing the use of energy resources. To assess the efficacy of the proposed energy management (EM) framework, several performance metrics are used. The obtained results are compared between scenarios with and without the EM strategy implementation. The findings indicate a significant decrease in fuel usage while implementing the EM approach, without encountering any unfulfilled load or capacity shortage. Investigation demonstrates that MG integrated with EM has a fuel consumption rate of 80,594 L/yr, in contrast to the absence of EM, the MG consumes 94,673 L/yr. The findings further verify the efficient utilization of excess electricity through the implementation of the EM strategy, which resulted in minimal wastage of only 0.293% compared to 2.383% in the MG lacking EM. Results indicate that the utilisation of EM leads to increased incorporation of renewable energy sources (RESs) within the MG. The proposed EM framework shows potential as a viable solution for improving the energy efficiency and reliability in MGs.

A 100% renewable energy (RE)-based stand-alone power system can be achieved using a resilient renewable energy storage system RESS to provide a sufficient and stable power supply. This study conducted a feasibility study for a 100% renewable energy hydrogen-enabled microgrid in the Pilbara region, North of Western Australia (WA). In this feasibility study, two different sites were studied to techno-economically evaluate the transition to a 100% RE-based stand-alone microgrid (SAM) using a hybrid hydrogen-battery storage system. The first selected site was a grid-connected small-scale aboriginal community power system (150 kWh/day) to be expanded to a large-scale 100% RE SAM power system to avoid the cost of expansion of their grid connection and become a pilot zero-emission project. The second selected site was a diesel-based power system in medium-scale aboriginal communities (1.5 MWh/day). A diesel power station transition to a 100% RE SAM power system has been modelled as a pilot project using a hybrid hydrogen-battery storage system. This study demonstrated the techno-economic viability of using hybrid hydrogen-battery RESS to provide lower energy cost with sufficient autonomy and reduce the carbon footprint. Several scenarios were considered in the modelling for the most optimal option for the locality in terms of the cost of energy and GHG emissions. Both sites were modelled with the principle of a battery bank following the load fluctuations while the hydrogen fuel cell generator covers the baseload.

Simulation analyses for the grid-connected site revealed that a larger capacity of hydrogen system adds more energy autonomy at a price. An additional scenario of utilising one of the existing diesel generators for site 2 as a backup was evaluated. It is found that having the diesel generator backup is the most robust and cost-effective option. However, this option comes at the cost of having a tiny percentage (1.5%) of fossil fuel penetration, therefore incurring a 1.5% carbon footprint compared to the base case scenario.

Today's power system network is more complex with enhanced responsibility to maintain reliable, stable and quality supply of power at transmission and distribution level. Maintaining grid balance is a bigger issue, in case of any unexpected generation shortage or grid disturbance or any participation of an intermittent nature of renewable energy sources like wind and solar power in the energy mix. In order to compensate such imbalance and improve reliability, and stability of power system, an energy storage system (ESS) can be considered as a vital solution. Also ESS can be used to mitigate associated issues of renewable energy sources while integration into the power system network. Thus ESS supports to get a reduction in greenhouse gas (GHG) emissions by means of integrating more renewable energy sources to the grid effectively. There are various types of Energy Storage (ES) technologies which are being used in power systems network for large scale (MW) to small scale (KW) level. Based on the type and characteristics, each storage technology is suitable for a particular role of applications. This paper presents an extensive review study on various types of ES technologies in characteristics and applications point of view. It also demonstrates various applications of ESS in detail. Finally, with the aid of ES-selectTM tool software, a feasibility analysis has been carried out to identify a suitable ES technology for appropriate applications at different grid locations and also helps to develop a smart hybrid storage system for grid applications in future.

Renewable energy (RE) is staring to be used as the panacea for solving current climate change or global warming threats. Therefore, government, utilities and research communities are working together to integrate large-scale RE into the power grid. However, there are a number of potential challenges in integrating RE with the existing grid. The major potential challenges are as follows: unpredictable power generation, week grid system and impacts on power quality (PQ) and reliability. This chapter investigates the potential challenges in integrating RE as well as distributed energy resources (DERs) with the smart power grid including the possible deployment issues for a sustainable future both nationally and internationally. Initially, the prospects of RE with their possible deployment issues were investigated. Later, a prediction model was proposed that informs the typical variation in energy generation as well as effect on grid integration using regression algorithms. This chapter also investigates the potential challenges in integrating RE into the grid through experimental and simulation analyses.

Growing concern about climate change and global warming has resulted in an increasing emphasis on reducing carbon emissions. Renewable energy (RE) is emerging as a universal remedy to these problems. However, due to the congestion and heavy load on distribution networks in metropolitan areas, large scale RE facilities are unlikely to be built in urban settings. By contrast, the ‘wide open spaces’ and low population densities of regional areas are a considerable advantage for siting new RE installations. The large-scale deployment of RE plants in the regional Australia is a need and requirement for both environmental sustainability and energy efficiency. Regional Australia has enormous potentialities for RE, particularly in wind, solar and geothermal. However, due to the intermittent nature of RE sources and cost of energy generation, integration of large-scale RE with the grid introduces potential challenges that include power quality (PQ), energy-efficiency, cost-economic analysis with respect to greenhouse gas emission (GHG) and other socio-environmental factors. This chapter explores the benefits as well as the possible deployment and integration issues of renewable energy into the grid, when considering the development of a clean-energy system for a sustainable regional Australia. A hybrid model is presented to investigate the prospects of renewable energy, in particular, wind and solar energy in different locations across regional Australia. From simulation analysis, it is clearly evident that regional Australia has huge potential for large scale renewable energy which could feed energy into the national grid.

Emerging wireless sensor networking (WSN) and modern machine learning techniques have encouraged interest in the development of vehicle health monitoring (VHM) systems that ensure secure and reliable operation of the rail vehicle. The performance of rail vehicles running on railway tracks is governed by the dynamic behaviours of railway bogies especially in the cases of lateral instability and track irregularities. In order to ensure safety and reliability of railway in this chapter, a forecasting model has been developed to investigate vertical acceleration behaviour of railway wagons attached to a moving locomotive using modern machine learning techniques. Initially, an energy-efficient data acquisition model has been proposed for WSN applications using popular learning algorithms. Later, a prediction model has been developed to investigate both front and rear body vertical acceleration behaviour. Different types of models can be built using a uniform platform to evaluate their performances and estimate different attributes’ correlation coefficient (CC), root mean square error (RMSE), mean absolute error (MAE), root relative squared error (RRSE), relative absolute error (RAE) and computation complexity for each of the algorithm. Finally, spectral analysis of front and rear body vertical condition is produced from the predicted data using Fast Fourier Transform (FFT) and used to generate precautionary signals and system status which can be used by the locomotive driver for deciding upon necessary actions.

Recent developments in wireless communications have enabled the development of low-cost, low-power wireless sensor networks (WSN) with wide applicability, including environment and vehicle-health monitoring. Minimizing energy consumption and hence maximizing the life time of the network are key requirements in the design of optimum sensor networking protocols and algorithms. Several routing protocols with different objectives have already been proposed for energy-efficient WSN applications. This paper surveys a sample of existing energy-efficient cluster-based and QoS-aware routing protocols and highlights their key features, including strengths and weaknesses