Data Mining - Concepts and Techniques.

Introduction to stochastic programming

A systematic literature review of blockchain-based applications: Current status, classification and open issues

Trends in extreme learning machines

Differential Evolution (DE) is arguably one of the most powerful and versatile evolutionary optimizers for the continuous parameter spaces in recent times. Almost 5 years have passed since the first comprehensive survey article was published on DE by Das and Suganthan in 2011. Several developments have been reported on various aspects of the algorithm in these 5 years and the research on and with DE have now reached an impressive state. Considering the huge progress of research with DE and its applications in diverse domains of science and technology, we find that it is a high time to provide a critical review of the latest literatures published and also to point out some important future avenues of research. The purpose of this paper is to summarize and organize the information on these current developments on DE. Beginning with a comprehensive foundation of the basic DE family of algorithms, we proceed through the recent proposals on parameter adaptation of DE, DE-based single-objective global optimizers, DE adopted for various optimization scenarios including constrained, large-scale, multi-objective, multi-modal and dynamic optimization, hybridization of DE with other optimizers, and also the multi-faceted literature on applications of DE. The paper also presents a dozen of interesting open problems and future research issues on DE.

Recent advances in differential evolution – An updated survey

Smart grid has become the most complicated interconnected system in human society. With the advanced sensor infrastructure, the smart grid will continuously generate unprecedented data volume. This paper gives an overview discussion of the big data management & analysis in the smart grid. Firstly, this paper identifies the basic requirements of the big data analysis in the smart grid. Secondly, this paper introduces some emerging technologies in multiple domains which can be utilized to develop the big data applications in the smart grid. Thirdly, this paper summarizes some promisingly big data application scenarios in the smart grid.

Enabling the big data analysis in the smart grid

Demand side management techniques have drawn significant attentions along with the development of smart grid. This paper proposes a new direct load control (DLC) model for scheduling interruptible air conditioner loads. The model is coordinated with the unit commitment and economic dispatch to minimize the total operation cost over the whole dispatch horizon. The network constraints are also considered in the model. To ensure the thermal comfort of the occupants, we are among the first to incorporate the advanced two-parameter thermal inertia dynamical model of customer houses into the DLC model to calculate the indoor temperature variation. This paper also proposes a distributed imperialist competitive algorithm to effectively solve the model. The simulation studies prove the efficiency of the proposed methodology.

/pdf/direct-load-control-by-distributed-imperialist-competitive-14xqla3tmx.pdf

Direct load control by distributed imperialist competitive algorithm

Wind power is one of the promising renewable energy resources to achieve energy sustainability. Its growth poses increasing financial and technical challenges to power systems, mainly due to the wind intermittency. Existing transmission networks may require infrastructure investment, in order to maintain the economical, secure and reliable operations of power systems with increasing wind power penetration. This study proposes a stochastic transmission expansion planning (TEP) framework to assess the impacts of wind power penetration and demand response incorporation. A risk constraint on load curtailment is introduced and its effect on TEP solutions is investigated. Also, to reduce the complexity and size the formulated TEP problem, a decomposition-based approach is adopted. According to the numerical results on the Garver's six-bus and the modified IEEE 30-bus systems, the solutions of TEP are subject to the variation of wind power characteristics and cannot be determined straightforward. Hence risk-analysis should be carried out to guide transmission investment with increased flexibility. The incorporation of wind power uncertainty increases the total cost and expected energy not supplied, which can be mitigated by the proposed TEP approach.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-rpg.2016.0794

Probabilistic transmission expansion planning for increasing wind power penetration

The distribution automation system (DAS) is vulnerable to cyber-attacks due to the widespread use of terminal devices and standard communication protocols. On account of the cost of defense, it is impossible to ensure the security of every device in the DAS. Given this background, a novel quantitative vulnerability assessment model of cyber security for DAS is developed in this paper. In the assessment model, the potential physical consequences of cyber-attacks are analyzed from two levels: terminal device level and control center server level. Then, the attack process is modeled based on game theory and the relationships among different vulnerabilities are analyzed by introducing a vulnerability adjacency matrix. Finally, the application process of the proposed methodology is illustrated through a case study based on bus 2 of the Roy Billinton Test System (RBTS). The results demonstrate the reasonability and effectiveness of the proposed methodology.

https://www.mdpi.com/1996-1073/8/6/5266/pdf

Quantitative vulnerability assessment of cyber security for distribution automation systems

Vehicle routing problem is a classic combinational optimization problem, which has been attracting research attentions in logistics and optimization area. Conventional static vehicle routing problem assumes the logistics information is accurate and timely, and does not take into account the uncertainties, which is therefore inadequate during practical applications. In this paper, a vehicle initial routing optimization model considering uncertainties is proposed, the vehicle capacity, customer time-window, and the maximum travelling distance as well as the road capacity are considered. In the cyber-physical logistics system background, a routing adjustment model is proposed to minimize the total distribution cost considering the road congestion, and the static and dynamic models are proposed for traffic information transmission network to quantitatively analyse the impact of the traffic information transmission delay on the vehicle routing optimization. The learnable genetic algorithm is adopted to solve the initial routing optimization model and the routing adjustment model. The simulation results have verified its effectiveness.

Junhua Zhao

Papers

Enabling the big data analysis in the smart grid

Direct load control by distributed imperialist competitive algorithm

Probabilistic transmission expansion planning for increasing wind power penetration

Quantitative vulnerability assessment of cyber security for distribution automation systems

Cyber-physical logistics system-based vehicle routing optimization