Lin Jiang

TL;DR: This paper presents a new method called Multi-objective Optimization by Reinforcement Learning (MORL), to solve the optimal power system dispatch and voltage stability problem and shows that MORL is superior over MOEA/D, as MORL can find wider and more evenly distributed Pareto fronts, obtain more accurate Pare to optimal solutions, and require less computation time.

TL;DR: In this paper, the chordal sparsity and symmetry of the graph related to LFC loops are exploited to improve the numerical tractability of delay-dependent stability analysis by exploiting the symmetry of LFC control loops.

TL;DR: An optimal distribution network operational model (ODNOM) is proposed to consider PQ problems caused by BES and DG and shows that a satisfactory PQ can be achieved by the proposed approach while optimizing the total branch active power losses.

TL;DR: A pseudo-gradient is proposed to improve the performance of evolutionary algorithms and its performance is compared with the standard evolutionary programming and standard genetic algorithm.

TL;DR: The results suggest that this analytical model is suitable for describing cases involving open ground surface beyond the foundation edge, serving as the primary oxygen source, and indicates that the major contribution of upward advection to indoor air concentration could be limited to the increase of soil gas entry rate.