This paper proposes a dynamic model of a solar-based micro-cogeneration system called photovoltaic-thermal (PVT) collector to perform a design optimization of the multi-stage PVT system. The parametric study reveals t...
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This paper proposes a dynamic model of a solar-based micro-cogeneration system called photovoltaic-thermal (PVT) collector to perform a design optimization of the multi-stage PVT system. The parametric study reveals the most important design parameters influencing the water-based flat-plate PVT system performance. The analysis also shows an existing trade-off between thermal and electrical efficiencies during the PVT operation. A novel exergy-based multi-objective design optimization method is demonstrated to find a trade-off design solution of the multi-stage PVT collector which compromises between the electrical and thermal exergy efficiencies under different weather conditions. The electrical and thermal exergy efficiencies are defined to be the objective functions of the Matlab gamultiobj-function, which is a multi-objective evolutionary algorithm using non-dominated sorting genetic algorithm-ii (NSGA-ii). As a result of the algorithm, the Pareto optimal sets were derived that revealed the optimal solutions taking into account the trade-off nature of the optimization problem. The decision-making method called an ideal point method was used in the decision-making process to find the final optimal solutions for different weather conditions. The results revealed that the optimal number of the PVT collectors in series depended on the weather conditions and decreased from 3 to 2 if the conditions got cooler.
In railway traction power supply, co-phase system with hybrid power quality conditioner (HPQC) is capable of tackling the power quality issues caused by single-phase traction loads. To further reduce the overall carbo...
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In railway traction power supply, co-phase system with hybrid power quality conditioner (HPQC) is capable of tackling the power quality issues caused by single-phase traction loads. To further reduce the overall carbon emissions in railway systems, this paper considers to integrate the renewable energy with railway power supply, which however leads to a more complicated system to model, design and control. This paper first investigates its modelling aspect. To reduce the operating capacity of HPQC while addressing the power unbalance, optimal design of the compensation scheme for co-phase system is formulated as a multi-objective optimization problem which is then solved by the nondominated sorting genetic algorithm-ii (NSGA-ii). To eliminate the impact of errors arising from imperfect predictions of the loads and renewable power, a hybrid optimal compensation control is proposed, yielding full and optimal compensations. Comprehensive simulation studies, considering three operation modes covering variable traction loads, renewable and regenerative braking power, are conducted. The simulation results confirm the validity of the proposed optimal compensation scheme, achieving an average of more than 20% reduction in HPQC capacity compared to the full compensation scheme. Meanwhile, the power quality requirement is satisfied, even in the presence of real-time prediction errors.
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