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The outbreak of SARS-COV-2 disease 2019 (COVID-19) abruptly changed the patterns in electricity consumption, challenging the system operations of forecasting and balancing supply and demand. This is due to the mitigation measures that include lockdown and Work from Home (WFH), which decreased the aggregated demand and remarkably altered its profile. Here, we characterise these changes with various quantitative markers and compare it with pre-COVID-19 business-as-usual data using Great Britain (GB) as a case study. The ripple effects on the generation portfolio, system frequency, forecasting accuracy and imbalance pricing are also analysed. An energy data extraction and pre-processing pipeline that can be used in a variety of similar studies is also presented. Analysis of the GB demand data during the March 2020 lockdown indicates that a shift to WFH will result to a net benefit for flexible stakeholders, such as consumer on variable tariffs. Furthermore, the analysis illustrates a need for faster and more frequent balancing actions, as a result of the increased share of renewable energy in the generation mix. This new equilibrium of energy demand and supply will require a redesign of the existing balancing mechanisms as well as the longer-term power system planning strategies.

The accurate prediction of the performance output of photovoltaic (PV) installations is becoming ever more prominent. Its success can provide a considerable economic benefit, which can be adopted in maintenance, installation, and when calculating levelized cost. However, modelling the long-term performance output of PV modules is quite complex, particularly because multiple factors are involved. This article investigates the available literature relevant to the modelling of PV module performance drop and failure. A particular focus is placed on cracks and hotspots, as these are deemed to be the most influential. Thus, the key aspects affecting the accuracy of performance simulations were identified and the perceived relevant gaps in the literature were outlined. One of the findings demonstrates that microcrack position, orientation, and the severity of a microcrack determines its impact on the PV cell’s performance. Therefore, this aspect needs to be categorized and considered accordingly, for achieving accurate predictions. Additionally, it has been identified that physical modelling of microcracks is currently a considerable challenge that can provide beneficial results if executed appropriately. As a result, suggestions have been made towards achieving this, through the use of methods and software such as XFEM and Griddler.

In this paper, an experimental fuel cell/battery/supercapacitor hybrid system is investigated in terms of modeling and power management design and optimization. The power management strategy is designed based on the role that should be played by each component of the hybrid power source. The supercapacitor is responsible for the peak power demands. The battery assists the supercapacitor in fulfilling the transient power demand by controlling its state-of-energy, whereas the fuel cell system, with its slow dynamics, controls the state-of-charge of the battery. The parameters of the power management strategy are optimized by a genetic algorithm and Pareto front analysis in a framework of multi-objective optimization, taking into account the hydrogen consumption, the battery loading and the acceleration performance. The optimization results are validated on a test bench composed of a fuel cell system (1.2 kW, 26 V), lithium polymer battery (30 Ah, 37 V), and a supercapacitor (167 F, 48 V).

To date, only a few studies have examined the execution of the actuator disc approximation for a full-size turbine. Small-scale models have fewer constraints than large-scale models because the range of time-scale and length-scale is narrower. Hence, this article presents the methodology in implementing the actuator disc approach via the Reynolds-Averaged Navier-Stokes (RANS) momentum source term for a 20-m diameter turbine in an idealised channel. A structured grid, which varied from 0.5 m to 4 m across rotor diameter and width was used at the turbine location to allow for better representation of the disc. The model was tuned to match known coefficient of thrust and operational profiles for a set of validation cases based on published experimental data. Predictions of velocity deficit and turbulent intensity became almost independent of the grid density beyond 11 diameters downstream of the disc. However, in several instances the finer meshes showed larger errors than coarser meshes when compared to the measurements data. This observation was attributed to the way nodes were distributed across the disc swept area. The results demonstrate that the accuracy of the actuator disc was highly influenced by the vertical resolutions, as well as the grid density of the disc enclosure.

In this paper, a novel concept to integrate High Voltage Direct Current (HVDC)-connected offshore wind power plants with the onshore grid is presented. The concept makes use of a holistic wind farm controller along with a fully instrumented conventional synchronous generator at the point of common coupling. In our approach, the wind farm is able to replicate the natural response of the generator to a system, even enabling the wind farm to reproduce, in a scaled up manner, a range of ancillary services without having to rely on indirect frequency measurements which are prone to noise and delays. Simulation results are presented to validate the proposed solution.

This paper presents a numerical method to address function estimation problems in inverse heat transfer problems using parameter estimation approach without prior information on the functional form of the variable to be estimated. Using an inverse analysis, the functional form of a time-dependent heat transfer coefficient is estimated efficiently and accurately. The functional form of the heat transfer coefficient is assumed unknown and the inverse heat transfer problem should be treated using a function estimation approach by solving sensitivity and adjoint problems during the minimization process. Based on proposing a new sensitivity matrix, however, the functional form can be estimated in an accurate and very efficient manner using a parameter estimation approach without the need for solving the sensitivity and adjoint problems and imposing extra computational cost, mathematical complexity, and implementation efforts. In the proposed sensitivity analysis scheme, all sensitivity coefficients can be computed in only one direct problem solution at each iteration. In this inverse heat transfer problem, the body shape is irregular and meshed using a body-fitted grid generation method. The direct heat conduction problem is solved using the finite-difference method. The steepest-descent method is used as a minimization algorithm to minimize the defined objective function and the termination of the minimization process is carried out based on the discrepancy principle. A test case with three different functional forms and two different measurement errors is considered to show the accuracy and efficiency of the used inverse analysis.

The conventional energy source in cement industries is fossil fuels, mainly coal, which has a high environmental footprint. On average, energy expenditures account for 40% of the overall production costs per ton of cement. Reducing both the environmental impact and economic expenditure involves incorporating alternative energy sources (fuels) such as biomass, solid-derived fuel (SDF), refuse-derived fuel (RDF) etc. However, within cement plants, the substitution of conventional fossil fuels with alternative fuels poses several challenges due to the difficulty in incorporating additional fuel-saving techniques. Typically, an additional 3000 MJ of electricity per ton of clinker is required. One of the most effective solutions to this is thermal optimization through co-processing and pre-processing, which makes it possible to implement additional fossil-fuel-saving techniques. In developing nations such as Togo, waste-management systems rely on co-processing in cement factories through a waste-to-energy relationship. Also, there are some old cement plants with low-efficiency, multi-stage preheaters without pre-calciners, reciprocating huge coolers, low-efficiency motors etc., which still operate and need to be made environmentally sustainable. However, compared to modern kilns which can have up to 95% of energy recovery from waste, an old suspension preheater kiln can recover only up to 60% of its heat energy depending on the cooler type, and due to the lack of a bypass and combustion chamber (pre-calciner). This research paper evaluated the performance of a cement plant incorporating AF and presents the procedures and recommendations to optimize AF substitution in cement plants. To achieve this, a comparative performance study was carried out by assessing the alternative fuel characteristics and the equipment performance before and after the incorporation of the alternative fuel. Data were collected on the optimum substitution ratio, pre-processing and co-processing performance, raw-meal design and economic analysis. Results indicated that the cost to be covered per ton of waste input is €10.9 for solid-derived fuel (SDF), €15 for refuse-derived fuel (RDF), and that the co-processing cost optimization for the cement plant could have a cost saving of up to 7.81€/GJ. In conclusion, it is recommended that appropriate kiln and alternative-fuel models be created for forecasting production based on various AF.