• Energy Research

Magnesium hydride owns the largest share of publications on solid materials for hydrogen storage. The Magnesium group of international experts contributing to IEA Task 32 Hydrogen Based Energy Storage recently published two review papers presenting the activities of the group focused on magnesium hydride based materials and on Mg based compounds for hydrogen and energy storage. This review article not only overviews the latest activities on both fundamental aspects of Mg-based hydrides and their applications, but also presents a historic overview on the topic and outlines projected future developments. Particular attention is paid to the theoretical and experimental studies of Mg-H system at extreme pressures, kinetics and thermodynamics of the systems based on MgH2,nanostructuring, new Mg-based compounds and novel composites, and catalysis in the Mg based H storage systems. Finally, thermal energy storage and upscaled H storage systems accommodating MgH2 are presented.

Abstract Large photovoltaic arrays are becoming common as the world moves to replace fossil-fuelled electricity generators. As the array size and project cost increase, it becomes increasingly important to know accurately what the array’s performance will be before it is built. Large arrays inevitably contain modules with a spread of performance characteristics such as short-circuit current and open-circuit voltage, and suffer from temperature differences between modules. In this first study of these problems, a model has been developed that accurately predicts the behaviour of a photovoltaic array subject to variability between modules and inhomogeneity of cell temperature across the array. The model was applied to a real rooftop array consisting of 912 modules (298 kW nominal peak power). Based on measured string currents, the predicted average string temperature was compared the temperature measured by a radiometric survey using a drone-mounted IR camera and matched very well. The five-parameter model of cell I - V characteristics was fitted to manufacturer’s data, with highest weighting given to the region around the maximum-power point (MPP) where a real array should operate via active MPP tracking. The model was used to explore separately the effects of a spread in module characteristics arising in the manufacturing process and of temperature inhomogeneity across the array. The current in each module of a string was constrained to be the same, and the voltage of every parallel-connected string was also constrained to be the same. These constraints lead to greater power loss than is predicted based on an average module at an average temperature. Compared to a hypothetical array assembled from identical average modules at the same average temperature, variability caused a loss of power of about 2%, depending on the detailed form of the distribution function chosen to represent the spread of characteristics in the manufacturer’s tolerance band. As a rule of thumb, de-rating the maximum power to the lower end of the manufacturer’s tolerance band is recommended to account for module variability during the design phase. The effect of temperature inhomogeneity is more serious, because temperature affects V oc strongly, causing parallel-connected strings to be pulled away from their ideal operating points to obey the constraint of equal voltage. A modest 10 °C temperature gradient across the studied array was predicted to cause about a 4% loss of power at the MPP. Much higher real temperature differences could be expected in summer and were observed. The study confirmed that temperature inhomogeneity poses a serious design problem for large arrays, requiring careful thermal design to achieve not only acceptably low average array temperature, but also the least possible temperature spread.

Abstract Electricity generation presents the biggest opportunity to lower CO 2 emissions and it is foreseen that hydrogen energy technology will play an important role in realising the scenario to cap global warming at 2 °C through replacement of fossil fuels with renewables. The transition to electric power for transport in battery- and fuel-cell-electric vehicles will further increase the need for low-carbon electricity generation. For a successful transition to a renewable energy economy the traditional approach of designing energy systems to meet only goals related to the technology (capacity, availability, reliability) and economics (return on investment, cost to the consumer) must evolve to take on a more holistic viewpoint and be able to take into account other goals addressing environmental and social considerations. This paper reviews approaches for integrating hydrogen energy technology into hybrid energy systems, emphasising electricity generation using a hydrogen fuel cell. Integration of energy storage, sizing methodologies, energy flow management and their associated optimization algorithms and software implementation are addressed. Few published case studies go beyond technical considerations. This reality is discussed in the light of available software packages. A four-dimensional multi-objective meta-heuristic function is proposed with weighting of technical, economic, environmental and socio-political factors to suit the design goals for the energy system.

This paper presents a battery capacity optimisation method with the aim of investment and operational cost reduction for grid-connected microgrids consisting of dispatchable generators, renewable energy resources and battery energy storage. The operating cost of grid-connected commercial Microgrids is mainly associated with the purchased energy from the grid and monthly peak demand. Hence, mitigating the peak value by the means of battery energy storage and dispatchable generators during the peak period can effectively reduce the operating cost. However, due to the high cost and short life span of the battery energy storage systems, the optimum design of energy storages is of the utmost importance to the Microgrids. This paper proposes an efficient iterative method with an inner unit commitment optimisation layer to achieve the optimised battery capacity. In order to implement the inner unit commitment optimisation, the Mixed Integer Quadratic Programming (MIQP) optimisation algorithm is applied and CPLEX solver is chosen to solve the optimisation problem. This approach is applicable and beneficial when dealing with high demands as it economically distributes the load requirement between the battery and dispatchable generators. Finally, the proposed method is applied to determine the battery capacity of the experimental Microgrid at Griffith University. The simulation results for the understudy case verified the efficiency and effectiveness of the proposed approach.

Abstract High penetration of renewable energy sources (RES) in distributed Microgrid (MG) systems provides significant economic and environmental benefits. Nevertheless, the intermittent nature of RESs is the greatest challenge in the efficient utilisation of these resources. Using Battery Energy Storage Systems (BESS) in MGs is proposed as an effective solution to mitigate the fluctuations of RESs power generation. However, BESSs are expensive components with a short lifetime and therefore, they should be optimally sized according to the characteristics and requirements of MGs. This paper proposes a BESS sizing approach to determine the capacity and power rating of the BESS in a Grid-Connected MG, taking economic and technical criteria as well as the uncertainty of dispatchable generators (DG) and RESs into consideration. The technical requirements include the degree of reliability, BESS degradation, and operating constraints of the DGs. On the other hand, the cost factors associated with the utility grid including peak cost, valley cost, and Time of Use tariff (TOU) are taken into account. In this paper, a multi-objective mixed-integer quadratic model is developed to include the aforementioned parameters in the BESS capacity optimisation algorithm. Inspecting the impact of the considered parameters on the optimal size of the BESS and total cost of the MG, four case scenarios are studied. The first two scenarios evaluate the effect of the BESS depreciation, whereas the third and fourth scenarios investigate the impact of grid volatility (GV) and reliability indices on the optimal solution. The obtained results from the case studies verify the effectiveness of the proposed BESS sizing method.

An advanced PEM fuel cell mathematical model is described and realised in four ancillaries in the Matlab–Simulink environment. Where possible, the model is based on parameters with direct physical meaning, with the aim of going beyond empirically describing the characteristics of the fuel cell. The model can therefore be used to predict enhanced performance owing to, for instance, improved electrode materials, and to relate changes in the measured performance to internal changes affecting influential physical parameters. Some simplifying assumptions make the model fairly light in computational demand and therefore amenable to extension to simulate an entire fuel-cell stack as part of an energy system. Despite these assumptions, the model emulates experimental data well, especially at high current density. The influences of pressure, temperature, humidification and reactant partial pressure on cell performance are explored. The dominating effect of membrane hydration is clearly revealed.