http://bura.brunel.ac.uk/handle/2438/8622 2026-05-05T11:49:06Z http://bura.brunel.ac.uk/handle/2438/33215 Title: System Sizing of Hybrid Renewable Systems Under Inverter and Contracted Grid Power Constraints with Flexible Load Integration Authors: Abualshawareb, A; Pisica, I; König, C Abstract: Island microgrids face significant challenges, including seasonal load variations, weak interconnections, and high demand charges leading to oversized energy systems. This paper introduces a novel bi-level optimization approach, combining a genetic algorithm for capacity sizing and a rolling-horizon mixed-integer linear programming controller for daily dispatch scheduling. The method simultaneously optimizes photovoltaic arrays, battery storage, hydrogen tanks, inverter ratings, and contracted grid-import limits to minimize the net present cost. The approach was applied to a municipal energy community in Formentera, Spain, with an annual demand of approximately 203 megawatt-hours. Compared to the baseline scenario (661,677 euros), the proposed framework reduced the net present cost to 612,945 euros without load flexibility. Introducing load flexibility further decreased costs: 606,879 euros at 6 percent and 599,134 euros at 8 percent flexibility. Increased flexibility resulted in modest reductions in photovoltaic capacity from 155 to 150 kilowatt-peak, inverter size from 77 to 72 kilowatts, and contracted grid-import limits from 41 to 37 kilowatts. The findings underscore the significant economic and operational advantages of integrating demand-side flexibility into the co-optimization of component sizing, enhancing both the resilience and autonomy of islanded hybrid renewable energy systems. 2025-09-02T00:00:00Z http://bura.brunel.ac.uk/handle/2438/33211 Title: An Adaptive-Intelligent Distance-Aware Approach for Dynamic Network Connectivity Authors: Jasim, AM; Al-Anbagi, HN; Al-Azawi, S; Al-Raweshidy, H Abstract: Optimizing network connectivity characterizes a major challenge in different applications, e.g. IoT, where the efficiency of connectivity under constraints like distance and adaptability is essential. The key objective of the Minimum Spanning Tree (MST) methods is to construct a subset of a graph in which every vertex is connected with the least amount of edge weight and without any cycles. As a robust framework for constructing distance-constrained network connectivity, this research proposes a novel algorithm named Recursive Node Connectivity Algorithm (RNCA). RNCA forms a tree-like networking structure motivated by minimum spanning tree (MST) principles, while explicitly considering distance constraints to ensure feasible communications. The RNCA utilizes repeated pruning and reweighting procedures to create cost-effective and distance-compliant network structures that can ensure flexible and scalable connectivity in dynamic contexts. The performance of RNCA is evaluated through conducting simulations with different network scales. The results show that RNCA achieves high adaptability, scalability, and reduced redundancy. RNCA outperforms Kruskal’s and Prim’s MST algorithms by reducing recomputed links during node updates by up to 80%. It also ensures minimum change in network connections when nodes are added or removed from the original network topology. Therefore, it can be considered as a transformative solution for many modern network applications, such as transportation systems, WSNs, and IoT. 2026-03-15T00:00:00Z http://bura.brunel.ac.uk/handle/2438/33201 Title: Novel Forecasting for Photovoltaic Installation Output Using Transfer Learning Genetic Programming Authors: Liu, Y; Hulak, D; Huang, Z; Taylor, G Abstract: Accurately predicting photovoltaic (PV) power generation is crucial for optimizing energy allocation and integrating solar energy into smart grids. PV generation forecasting faces challenges due to varying forecasting horizons, seasonal changes, and location-specific environmental factors. Transfer learning can improve accuracy and reduce computation by using knowledge from related tasks. Genetic Programming (GP) offers transparent, interpretable symbolic regression models compared to black-box methods. In this paper, a transfer-learning-based GP is applied to the power generation forecasting model of PV installation across different seasons. In the proposed method, mutual information is employed to identify the useful knowledge from constructed models as the source domain, and captured shared generation patterns are then embedded into the initial population of the GP model for the target domain, guiding the search process. The proposed method is evaluated on a case study leveraging the homologous power generation patterns across different seasons for the same PV installation to assist in constructing forecasting models. Experiments on real-world seasonal data show the method outperforms state-of-the-art algorithms, improving both accuracy and model explainability. These results highlight the potential of transfer learning in GP for PV installation generation forecasting, offering both performance improvements and model transparency, which are crucial for real-world deployment and interpretability. Description: Acknowledgement: The weather data used was obtained from the NASA Langley Research Center (LaRC) POWER Project funded through the NASA Earth Science/Applied Science Program. The authors also thank the university for providing the data and for its support throughout the writing of this paper. 2025-09-02T00:00:00Z http://bura.brunel.ac.uk/handle/2438/33197 Title: Preventive Dispatch Against Attack of Adjustable Load on Power System Frequency Authors: Zhang, Y; Xiang, M; Huang, Z; Yang, Z Abstract: With the increase of adjustable loads connected to power systems, there is a growing risk of intentionally exploiting load adjustability to cause power balance deviations. This poses an obvious threat to power system frequency, which has been noticed by many system operators. However, the current dispatch method cannot consider the attack of load on frequency, let alone providing a preventive dispatch decision that reduces the frequency deviation. To address this challenge, a preventive dispatch method that mitigates the impact of adjustable load on power system frequency is presented for the first time. The relationship between the time-varying power mismatch caused by the attack of adjustable load and frequency deviation is established. This is achieved by theoretically deriving the transfer function between system frequency and power mismatch in time domain and discretizing the function into several segments according to the division of time horizons. A formal analysis of the error caused by the transformation is performed, which provides quantitative guidance on the accuracy of the frequency modeling. Based on this, the worst-case scenario of frequency deviation with adjustable load is determined through solving an optimization model. Then, a novel preventive dispatch method that guarantees the system frequency security under the worst-case scenario is presented. Particularly, the generator ramping behavior after receiving the AGC adjustment command is modeled by a group of linear constraints to distinguish the load tracking abilities of generators. Case studies based on the IEEE30-bus system and a 661-bus utility system show that the proposed preventive dispatch method can achieve a 5.8%-19.42% improvement of the maximum absolute frequency deviation. 2026-03-03T00:00:00Z