Lagrangian relaxation hybrid with evolutionary algorithm for short-term generation scheduling

Short-term generation scheduling is an important function in daily operational planning of power systems. It is defined as optimal scheduling of power generators over a scheduling period while respecting various generator constraints and system constraints. Objective of the problem includes costs associated with energy production, start-up cost and shut-down cost along with profits. The resulting problem is a large scale nonlinear mixed-integer optimization problem for which there is no exact solution technique available. The solution to the problem can be obtained only by complete enumeration, often at the cost of a prohibitively computation time requirement for realistic power systems. This paper presents a hybrid algorithm which combines Lagrangian Relaxation (LR) together with Evolutionary Algorithm (EA) to solve the problem in cooperative and competitive energy environments. Simulation studies were carried out on different systems containing various numbers of units. The outcomes from different algorithms are compared with that from the proposed hybrid algorithm and the advantages of the proposed algorithm are briefly discussed.     

Locally Weighted Learning

This paper surveys locally weighted learning, a form of lazy learning and memory-based learning, and focuses on locally weighted linear regression. The survey discusses distance functions, smoothing parameters, weighting functions, local model structures, regularization of the estimates and bias, assessing predictions, handling noisy data and outliers, improving the quality of predictions by tuning fit parameters, interference between old and new data, implementing locally weighted learning efficiently, and applications of locally weighted learning. A companion paper surveys how locally weighted learning can be used in robot learning and control.     

Methodological Challenges in Union Commitment Studies.

Methodological problems in studies of union commitment were identified and illustrated with data from 4,641 members and 479 stewards in 297 local teachers' unions. Using a 20-item union commitment scale, results confirmed the existence of 3 substantive factors and 1 method factor at the individual level of analysis: loyalty to the union, responsibility to the union, willingness to work for the union, and a factor of negatively worded items. Tests of measurement invariance showed that the scale captured commitment for rank-and-file members but not for union stewards. The authors also found partial measurement invariance between long-time and newer members and full measurement invariance between men and women. Finally, the authors found that violation of the statistical assumption of independence reduced model fit when individual commitment scores were analyzed without attention to the hierarchical nature of the data. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Nodal ant colony optimization for solving profit based unit commitment problem for GENCOs

This paper proposes a nodal ant colony optimization (NACO) technique to solve profit based unit commitment problem (PBUCP). Generation companies (GENCOs) in a competitive restructured power market, schedule their generators with an objective to maximize their own profit without any regard for system social benefit. Power and reserve prices become important factors in decision process. Ant colony optimization that mimics the behavior of ants foraging activities is suitably implemented to search the UCP search space. Here a search space consisting of optimal combination of binary nodes for unit ON/OFF status is represented for the movement of the ants to maintain good exploration and exploitation search capabilities. The proposed model help GENCOs to make decisions on the quantity of power and reserve that must be put up for sale in the markets and also to schedule generators in order to receive the maximum profit. The effectiveness of the proposed technique for PBUCP is validated on 10 and 36 generating unit systems available in the literature. NACO yields an increase of profit, greater than 1.5%, in comparison with the basic ACO, Muller method and hybrid LR-GA.     

考虑风电不确定性和机组故障停运风险的两阶段鲁棒机组组合

发电机故障停运是电力系统运行中的不确定事件。目前在含风电鲁棒机组组合中考虑机组故障停运的方法,不同程度地忽略了机组故障停运的概率特征,难以较好描述机组故障停运风险。在含风电鲁棒机组组合模型的基础上,引入电量不足期望以量化机组故障停运风险,基于成本收益分析建立了考虑风电不确定性和机组故障停运风险的两阶段鲁棒机组组合模型,并提出计算电量不足期望的简化模型以提升计算效率。基于改进的IEEE-RTS 26机系统验证所提模型和方法,结果表明该方法能较好考虑风电不确定性和机组故障停运风险的影响,该简化模型能显著减小问题求解规模、缩短计算时间。     

Paving the way for CRM success: The mediating role of knowledge management and organizational commitment

Customer relationship management (CRM) has become one of the most influential technologies in the world, and companies are increasingly implementing it to create value. However, despite significant investment in CRM technology infrastructure, empirical research offers inconsistent support for its positive impact on performance. This study develops and tests a research model analyzing the process through which CRM technology infrastructure translates into organizational performance, drawing on the resource-based view (RBV) and the knowledge-based view (KBV) of the firm. Based on an international sample of 125 hotels, the results suggest that organizational commitment and knowledge management fully mediate this process.     

知识型员工工作态度与离职倾向关系的实证研究

采用实证法研究了知识型员工工作态度总体水平及各维度与离职倾向的关系,并建立了关系模型。研究结果表明：工作态度及其维度均与离职倾向显著负相关,且组织承诺对离职倾向的影响大于工作满意度;规范承诺、内在满意度、情感承诺、外在满意度、持续承诺依次成为影响知识型员工离职倾向的主要因素,均负相关。     

ID-based trapdoor mercurial commitment under one-way functions assumptions

The notion of identity-based trapdoor mercurial commitment incorporates the advantages of both identity-based trapdoor commitment and trapdoor mercurial commitment. So far, the existing identity-based trapdoor mercurial commitment schemes are based on the strong Diffie-Hellman assumption or computational Diffie-Hellman assumption. One-way function existence is one of the fundamental assumptions in the cryptography. In this paper, we first propose a general construction for identity-based trapdoor mercurial commitment based on the one-way function. Meanwhile, we give a concrete instantiation based on the Boneh-Boyen signature scheme.     

计及风电与电动汽车随机性的两阶段机组组合研究

风电出力的随机性以及电动汽车(electric vehicle,EV)充电需求的不确定性给电力系统调度带来了挑战。在传统确定性机组组合模型的基础上,针对电力系统日前调度面临的不确定问题,提出了充分考虑风电与电动汽车双重不确定性的随机优化调度及备用计算模型。首先,对于风电出力不确定性,采用基于场景分析的两阶段随机优化方法,并使用生成对抗网络(generative adversarial network, GAN)来生成风电场景。其次,对于电动汽车充电需求的不确定性,将其分为可调度与不可调度两类。可调度电动汽车根据其出行规律采用随机模拟的方法,并建立了EV充电聚集商模型;不可调度电动汽车通过K-means聚类分析得到其典型负荷曲线,并将其并入系统常规负荷中。最终建立了基于多场景分析考虑EV充电聚集商的两阶段随机机组组合模型,并通过算例分析证明了所提模型的有效性。