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In order to combat climate change, many countries have promised to bolster Renewable Energy (RE) production following the Paris Agreement with some countries even setting a goal of 100% by 2025. The reasons are twofold: capitalizing on carbon emissions whilst concomitantly benefiting from reduced fossil fuel dependence and the fluctuations associated with imported fuel prices. However, numerous countries have not yet made preparations to increase RE production and integration. In many instances, this reluctance seems to be predominant in energy-rich countries, which typically provide heavy subsidies on electricity prices. With such subsidies, there is no incentive to invest in RE since the time taken to recoup such investments would be significant. We develop a model using a Neural Network (NN) regression algorithm to quantitatively illustrate this conjecture and also use it to predict the reduction in electricity price subsidies required to achieve a specified RE production target. The model was trained using 10 leading metrics from 53 countries. It is envisaged that policymakers and researchers can use this model to plan future RE targets to satisfy the Nationally Determined Contributions (NDC) and determine the required electricity subsidy reductions. The model can easily be modified to predict what changes in other country factors can be made to stimulate growth in RE production. We illustrate this approach with a sample use case. [ABSTRACT FROM AUTHOR]

Power generation companies have to meet consumer demand and reliability criteria. The maintenance of generators needs to be performed regularly to ensure a reliable supply of electricity. An increase in repair frequency will result in greater maintenance cost together with an expected decrease in capital loss due to generator failure. An optimal maintenance schedule is therefore needed to find the best suited trade-off and lower the overall operation cost. Maintenance scheduling is a combinatorial optimisation problem for which an exhaustive search is typically infeasible due to the large size of the solution space. Metaheuristic optimisation techniques are therefore used to approximate global optimum schedules in finite time. This paper compares the uses of the local search methods (hill climbing, tabu search, simulated annealing) against genetic and memetic algorithms in the solution of the generator maintenance scheduling problem. The solution methods improve the previously implemented solution of generator scheduling for the Power Generation Company of Trinidad and Tobago. [ABSTRACT FROM AUTHOR]