Two-Step Many-Objective Optimal Power Flow Based on Knee Point-Driven Evolutionary Algorithm

To coordinate the economy, security and environment protection in the power system operation, a two-step many-objective optimal power flow (MaOPF) solution method is proposed. In step 1, it is the first time that knee point-driven evolutionary algorithm (KnEA) is introduced to address the MaOPF problem, and thereby the Pareto-optimal solutions can be obtained. In step 2, an integrated decision analysis technique is utilized to provide decision makers with decision supports by combining fuzzy c-means (FCM) clustering and grey relational projection (GRP) method together. In this way, the best compromise solutions (BCSs) that represent decision makers' different, even conflicting, preferences can be automatically determined from the set of Pareto-optimal solutions. The primary contribution of the proposal is the innovative application of many-objective optimization together with decision analysis for addressing MaOPF problems. Through examining the two-step method via the IEEE 118-bus system and the real-world Hebei provincial power system, it is verified that our approach is suitable for addressing the MaOPF problem of power systems.Comment: Accepted by Journal Processe

Non-noble metal-catalysed carbonylative transformations

The dissertation is mainly concerned with non-noble metal-catalyzed carbonylative reactions. More specifically, copper, iron and manganese catalysed carbonylation reactions are presented. In all the above mentioned areas systematic catalyst optimization studies were performed and the scope and limitations of the respective protocol were presented

Online Static Security Assessment of Power Systems Based on Lasso Algorithm

As one important means of ensuring secure operation in a power system, the contingency selection and ranking methods need to be more rapid and accurate. A novel method-based least absolute shrinkage and selection operator (Lasso) algorithm is proposed in this paper to apply to online static security assessment (OSSA). The assessment is based on a security index, which is applied to select and screen contingencies. Firstly, the multi-step adaptive Lasso (MSA-Lasso) regression algorithm is introduced based on the regression algorithm, whose predictive performance has an advantage. Then, an OSSA module is proposed to evaluate and select contingencies in different load conditions. In addition, the Lasso algorithm is employed to predict the security index of each power system operation state with the consideration of bus voltages and power flows, according to Newton-Raphson load flow (NRLF) analysis in post-contingency states. Finally, the numerical results of applying the proposed approach to the IEEE 14-bus, 118-bus, and 300-bus test systems demonstrate the accuracy and rapidity of OSSA.Comment: Accepted by Applied Science

The Asymmetric Overnight Return Anomaly in the Chinese Stock Market

Traditional asset pricing theory suggests that to compensate for the uncertainty that investors bear, risky assets should generate considerably higher rates of return than the risk-free rate. However, the overnight return anomaly in the Chinese stock market, which refers to the anomaly that overnight return is significantly negative, contradicts the risk–return trade-off. We find that this anomaly is asymmetrical, as the overnight return is significantly negative after a negative daytime return, whereas the anomaly does not occur following a positive daytime return. We explain this anomaly from the perspective of investor attention. We show that the attention of individual investors behaves asymmetrically such that they draw more attention on negative daytime returns, and play an essential role in explaining the overnight return puzzle