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Optimal integration location and sizing of renewable energy systems in AC railways for cost optimisation
(2026-01-01)
Chinomi, Nutthaka
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Tian, Zhongbei
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Kano, Nakaret
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Chow, Chompoo inwai
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Jiang, Lin
This study presents a bi-level optimisation framework for the optimal integration of Photovoltaic (PV) systems and Energy Storage Systems (ESS) in AC railway traction power supply networks. The framework addresses two key objectives: determining the optimal capacities and selecting effective integration locations for PV and ESS to enhance energy efficiency and reduce operational costs. At the master level, a grid search explores combinations of PV and ESS sizes and locations, while the slave level employs a mixed-integer linear programming model solved with CPLEX to optimise ESS charge/discharge operations under variable energy conditions. Case studies demonstrate that integrating a 10 MW PV system with a 1 MWh, 2 MW ESS can reduce daily operational costs by 30 % (from 20152 £/d to 14007 £/d). The system shows resilience to short-term fluctuations in solar irradiance but exhibits higher sensitivity during extended low-irradiance periods. These findings highlight the importance of carefully balancing system configuration, operational strategies, and renewable energy variability. Overall, the proposed approach provides a structured methodology for achieving cost-effective and sustainable PV-ESS integration in railway electrification systems, supporting both economic and operational performance objectives.
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Design of DC/AC Unidirectional Inverter Based on DSP with Second-Order Switching Sequence Control in Photovoltaic DC Nano-Grids
(2025-01-01)
Bi, Chenxing
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Jiriwibhakorn, Somchat
Conventional inverters predominantly utilize Pulse-Width Modulation (PWM) control, operating in an intermediate state between the averaged and actual switching models. However, this approach often results in suboptimal output power quality and limited dynamic performance, which can compromise the operational stability of AC load systems. To address these limitations, this paper proposes a second-order Switching Sequence Control (SSC) strategy based on sliding mode theory, offering discrete-time control with enhanced robustness and precision. A comprehensive mathematical model of a single-phase LC inverter is developed and discretized. The SSC control algorithm is implemented using a TMS320F28335 Digital Signal Processor (DSP), with code generated via MATLAB/Simulink to streamline development and ensure real-time execution. Extensive simulations under various conditions demonstrate that the proposed inverter achieves excellent tracking accuracy, strong dynamic response, and reduced Total Harmonic Distortion (THD) is only 0.48%. Compared with conventional PWM, Selective Harmonic Elimination (SHE), and FPGA-based control methods, the SSC strategy significantly improves waveform fidelity and transient performance. These results confirm its suitability for high-performance applications in photovoltaic DC nanogrids, smart energy systems, and critical load support scenarios.
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Optimum Generated Power for a Hybrid DG/PV/Battery Radial Network Using Meta-Heuristic Algorithms Based DG Allocation
(2023-07-01)
Abdelwareth, Mohamed Els S.
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Riawan, Dedet Candra
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Chompoo-inwai, Chow
This paper presents four optimization outcomes for a diesel generator (DG), photovoltaic (PV), and battery hybrid generating radial system, to reduce the network losses and achieve optimum generated power with minimum costs. The effectiveness of the four utilized meta-heuristic algorithms in this paper (firefly algorithm, particle swarm optimization, genetic algorithm, and surrogate optimization) was compared, considering factors such as Cost of Energy (COE), the Loss of Power Supply Probability (LPSP), and the coefficient of determination (R<sup>2</sup>). The multi-objective function approach was adopted to find the optimal DG allocation sizing and location using the four utilized algorithms separately to achieve the optimal solution. The forward-backward sweep method (FBSM) was employed in this research to compute the network’s power flow. Based on the computed outcomes of the algorithms, the inclusion of an additional 300 kW DG in bus 2 was concluded to be an effective strategy for optimizing the system, resulting in maximizing the generated power with minimum network losses and costs. Results reveal that DG allocation using the firefly algorithm outperforms the other three algorithms, reducing the burden on the main DG and batteries by 30.48% and 19.24%, respectively. This research presents an optimization of an existing electricity network case study located on Tomia Island, Southeast Sulawesi, Indonesia.
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Optimum Operation and Cost Scenarios of a Hybrid Wind/PV/Battery in a Radial Network using Genetic Algorithm and Particle Swarm Optimization
(2022-01-01)
Abdelwareth, Mohamed Els S.
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Riawan, Dedet Candra
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Chompoo-Inwai, Chow
Hybrid generation systems took the attention of many researchers searching for the best energy source that can be optimum, reliable, and scalable instead of dependency on the traditional fossil fuels sources; researchers are developing Artificial Intelligence (AI) algorithms to optimize those systems. This paper will study the optimum generating power from a Wind turbine, PV, and Battery linked to the radial network in Tomia Island, considering the optimum cost using Particle Swarm Optimization (PSO) and Genetic Algorithm (GA).

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