Variable-Speed Hydropower Plants (VSHP) are becoming more promising for stabilising power grids with the increasing integration of renewable energy sources. This research focuses on improving fault ride-through capabilities and delivering efficient ancillary services for VSHPs to support the grid by developing a comprehensive control strategy. The control system proposed integrates a machine-side controller, a Frequency Support Controller (FSC), a Virtual Synchronous Machine (VSM), a Vector Current Controller (VCC) for the grid-side converter, a turbine governor for regulating turbine speed, and a DC-link controller. PID with an anti-windup scheme and a Model Predictive Controller (MPC) were employed for the turbine governor. The MPC turbine governor results demonstrate the potential of advanced control methods for enhanced performance of the VSHP. A benchmarking between the MPC and the PID governor was made. The benchmarking results have reported that the MPC can achieve reference tracking improvements up to $99.42\%$. Tests on a diverse set of grid scenarios were conducted, and the graphical results showed significant improvements in mitigating the frequency drops through the effective governor response. The synthetic inertia provision is swift, completing within seconds of a frequency drop. Compared to the fixed-speed approach, the VSHP improves the grid’s overall stability by minimising frequency dipping and achieving steady-state recovery remarkably faster. The fixed-speed approach only begins to recover minutes after the VSHP reaches the settling time. By effectively providing critical ancillary services such as frequency support, synthetic inertia, and smooth fault ride-through capability, the VSHP can become a transformative solution for future power grids, which are estimated to be more reliant on renewable energy sources.

The goal of partitioning clustering analysis is to divide a dataset into a predetermined number of homogeneous clusters. The quality of final clusters from a prototype-based partitioning algorithm is highly affected by the initially chosen centroids. In this paper, we propose the InoFrep, a novel data-dependent initialization algorithm for improving computational efficiency and robustness in prototype-based hard and fuzzy clustering. The InoFrep is a single-pass algorithm using the frequency polygon data of the feature with the highest peaks count in a dataset. By using the Fuzzy C-means (FCM) clustering algorithm, we empirically compare the performance of the InoFrep on one synthetic and six real datasets to those of two common initialization methods: Random sampling of data points and K-means++. Our results show that the InoFrep algorithm significantly reduces the number of iterations and the computing time required by the FCM algorithm. Additionally, it can be applied to multidimensional large datasets because of its shorter initialization time and independence from dimensionality due to working with only one feature with the highest number of peaks.

The power-generation mix of future grids will be quite diversified with the ever-increasing share of renewable energy technologies. Therefore, the prediction of electricity demand will become crucial for resource optimization and grid stability. Machine learning- and artificial intelligence-based methods are widely studied by researchers to tackle the demand forecasting problem. However, since the COVID-19 pandemic broke out, new challenges have surfaced for forecasting research. In such a short amount of time, significant shifts have emerged in electricity demand trends, making it apparent that the pandemic and the possibility of similar crises in the future have escalated the complexity of energy management problems. Motivated by the circumstances, this research presents an hour-ahead and day-ahead electricity demand forecasting benchmark using Deep Polynomial Neural Networks (DNN) and Gene Expression Programming (GEP) methods. The DNN and GEP algorithms utilize on-site electricity consumption data collected from a university hospital for over two years with a temporal granularity of 15-minute intervals. Quarter-hourly meteorological, calendar, and daily COVID-19 data, including new cases and cumulative cases divided by four restriction levels, were also considered. These datasets are used not only to predict the electricity demand but also to investigate the impact of the COVID-19 pandemic on the electricity consumption of the hospital. The hour-ahead and day-ahead nRMSE results show that the DNN outperforms the GEP by $8.27\%$ and $14.32\%$, respectively. For the computational times, the DNN appears to be much faster than the GEP by $82.83\%$ and $78.56\%$ in the hour-ahead and day-ahead forecasting, respectively.

Moving towards a sustainable future, optimising energy systems and resources involves increasingly sophisticated decision-making processes that require balancing technical efficiency with societal, economic and environmental constraints. However, traditional decision-making methods often rely on unidimensional cost metrics and are prone to rank reversal and sensitive to normalisation, leading to unstable or biased outcomes. Driven by these methodological and domain-specific research gaps, this article proposes a novel algorithm, “Multi-Criteria Evaluation via Gradual-Weighting and Aggregation of Normalised Distance Matrices (MEGAN).” Rather than using fixed-weight vectors as in conventional methods, MEGAN adjusts the weight vectors gradually to aggregate rankings across scenarios. Moreover, it utilises dynamic thresholds based on dispersion measures to improve decision precision. The proposed algorithm was verified using a synthetic dataset of ten grid alternatives evaluated across fourteen criteria (economic, societal, environmental, and technical) based on real-world expert priorities. Experimental analyses demonstrated that MEGAN achieves superior ranking stability, remaining insensitive to variations in normalisation techniques compared to conventional decision-making methods. In addition, according to the Institute of Electrical and Electronics Engineers (IEEE) Congress on Evolutionary Computation (CEC) complexity protocol, it achieved a lower computational complexity score than complex outranking methods, thereby offering scalability for large-scale problems. In summary, MEGAN provides a robust, insensitive, and computationally efficient decision-support tool capable of managing the multidimensional trade-offs essential to sustainable energy planning.

Genetic Algorithms (GAs) are pillars of evolutionary computing and one of the most well-known population-based metaheuristic optimisation techniques. They are widely used in engineering and applied optimisation problems for their capabilities in finding global solutions. Standard GAs (SGAs) determine probabilities of crossover and mutation by computationally expensive trials. Adaptive Genetic Algorithms (AGAs), on the other hand, improve this process by adjusting the parameters throughout generations. This study proposes three deterministic parameter control functions, ACM1, ACM2 and ACM3, for the regulation of crossover and mutation probabilities. Using advanced test functions, comparisons between four deterministic GAs, an SGA, two fixed-parameter GAs, and an AGA have been made. The fixed-parameter configurations are called FCM1 and FCM2. The AGA is called LTA, and four deterministic methods are called HAM and ACM1–3. Results show that the SGA is mostly inadequate for complex optimisation problems. The LTA performs inconsistently by failing on some functions and succeeding on others. The methods, ACM2, HAM, and FCM2, are highly robust and effective. Unexpectedly, the FCM2 performs the best for smaller population sizes. However, in higher-dimensional problems, the proposed method, ACM2, is superior and shows less variability in finding optimal solutions. The methods are also evaluated using a boost converter implementation.

The argument k is a mandatory user-specified input argument for the number of clusters which is required to start all of the partitioning clustering algorithms. In unsupervised learning applications, an optimal value of this argument is generally determined by using any of the internal validity indexes. However, the determination of k with aid of these indexes are computationally very expensive because they compute a k value using the results after several runs of a clustering algorithm. On the contrary, the package `kpeaks' enables to estimate k before starting a clustering session. It is based on a simple novel technique using the descriptive statistics of peak counts of the features in datasets. In this paper, we introduce and illustrate the details of R package `kpeaks' as an implementation for quick selection of the number of clusters for starting cluster algorithms.