Polynomial optimization techniques for activity scheduling. Optimization based prototype scheduler

Polynomial optimization techniques for activity scheduling (optimization based prototype scheduler) are presented in the form of the viewgraphs. The following subject areas are covered: agenda; need and viability of polynomial time techniques for SNC (Space Network Control); an intrinsic characteristic of SN scheduling problem; expected characteristics of the schedule; optimization based scheduling approach; single resource algorithms; decomposition of multiple resource problems; prototype capabilities, characteristics, and test results; computational characteristics; some features of prototyped algorithms; and some related GSFC references

Network syslog monitor

The purpose of this project to develope a network syslog monitoring system to monitor network devices like printers, switches, etc. and to help the network technical help desk personnel to handle issues promptly and effectively based on the syslog messages captured by the syslog server

A survey of big data and machine learning

This paper presents a detailed analysis of big data and machine learning (ML) in the electrical power and energy sector. Big data analytics for smart energy operations, applications, impact, measurement and control, and challenges are presented in this paper. Big data and machine learning approaches need to be applied after analyzing the power system problem carefully. Determining the match between the strengths of big data and machine learning for solving the power system problem is of utmost important. They can be of great help to plan and operate the traditional grid/smart grid (SG). The basics of big data and machine learning are described in detailed manner along with their applications in various fields such as electrical power and energy, health care and life sciences, government, telecommunications, web and digital media, retailers, finance, e-commerce and customer service, etc. Finally, the challenges and opportunities of big data and machine learning are presented in this paper

Optimal Reactive Power Scheduling Using Cuckoo Search Algorithm

This paper solves an optimal reactive power scheduling problem in the deregulated power system using the evolutionary based Cuckoo Search Algorithm (CSA). Reactive power scheduling is a very important problem in the power system operation, which is a nonlinear and mixed integer programming problem. It optimizes a specific objective function while satisfying all the equality and inequality constraints. In this paper, CSA is used to determine the optimal settings of control variables such as generator voltages, transformer tap positions and the amount of reactive compensation required to optimize the certain objective functions. The CSA algorithm has been developed from the inspiration that the obligate brood parasitism of some Cuckoo species lay their eggs in nests of other host birds which are of other species. The performance of CSA for solving the proposed optimal reactive power scheduling problem is examined on standard Ward Hale 6 bus, IEEE 30 bus, 57 bus, 118 bus and 300 bus test systems. The simulation results show that the proposed approach is more suitable, effective and efficient compared to other optimization techniques presented in the literature

Comparative analysis of electrochemical energy storage technologies for smart grid

This paper presents a comparative analysis of different forms of electrochemical energy storage technologies for use in the smart grid. This paper addresses various energy storage techniques that are used in the renewable energy sources connected to the smart grid. Energy storage technologies will most likely improve the penetrations of renewable energy on the electricity network. Consequently, energy storage systems could be the key to finally replacing the need for fossil fuel with renewable energy. It is hard to evaluate the different types of energy storage techniques between themselves due to the fact that each technology could be used in a different way and are more like compliments. Subsequently, for the purposes of this paper, it is seen that the use of energy storage technologies will increase the supply, and balances out the demand for energy

Transient stability enhancement using thyristor controlled series compensator

Stability is an important issue which determines the stable operation of power system. At present, the most practical available method of transient stability analysis is time domain simulation, in which the non-linear differential equations are solved by step by step method or network reduction techniques. In this paper, FACTS devices are used  in the existing system for effective utilization of existing transmission resources. In this paper, the studies have been carried out in order to improve the transient stability of 5 bus system, and Western System Coordinating Council (WSCC) 9 bus system with fixed compensation on various lines, and the optimal location has been investigated for better results. To improve the transient stability margin further, a Thyristor Controlled Series Compensator (TCSC) has been used, and the results shows the effectiveness of the application of TCSC in improving the transient stability of power system

Power system state estimation using teaching learning-based optimization algorithm

The main goal of this paper is to formulate power system state estimation (SE) problem as a constrained nonlinear programming problem with various constraints and boundary limits on the state variables. SE forms the heart of entire real time control of any power system. In real time environment, the state estimator consists of various modules like observability analysis, network topology processing, SE and bad data processing. The SE problem formulated in this work is solved using teaching leaning-based optimization (TLBO) technique. Difference between the proposed TLBO and the conventional optimization algorithms is that TLBO gives global optimum solution for the present problem. To show the suitability of TLBO for solving SE problem, IEEE 14 bus test system has been selected in this work. The results obtained with TLBO are also compared with conventional weighted least square (WLS) technique and evolutionary based particle swarm optimization (PSO) technique

Challenges, issues and opportunities for the development of smart grid

The development smart grids have made the power systems planning and operation more efficient by the application of renewable energy resources, electric vehicles, two-way communication, self-healing, consumer engagement, distribution intelligence, etc. The objective of this paper is to present a detailed comprehensive review of challenges, issues and opportunities for the development of smart grid. Smart grids are transforming the traditional way of meeting the electricity demand and providing the way towards an environmentally friendly, reliable and resilient power grid. This paper presents various challenges of smart grid development including interoperability, network communications, demand response, energy storage and distribution grid management. This paper also reviews various issues associated with the development of smart grid. Local, regional, national and global opportunities for the development of smart grid are also reported in this paper

Short-term optimal hydro-thermal scheduling using clustered adaptive teaching learning based optimization

In this paper, Clustered Adaptive Teaching Learning Based Optimization (CATLBO) algorithm is proposed for determining the optimal hourly schedule of power generation in a hydro-thermal power system. In the proposed approach, a multi-reservoir cascaded hydro-electric system with a non-linear relationship between water discharge rate, net head and power generation is considered. Constraints such as power balance, water balance, reservoir volume limits and operation limits of hydro and thermal plants are considered. The feasibility and effectiveness of the proposed algorithm is demonstrated through a test system, and the results are compared with existing conventional and evolutionary algorithms. Simulation results reveals that the proposed CATLBO algorithm appears to be the best in terms of convergence speed and optimal cost compared with other techniques

Smart cities: Understanding policies, standards, applications and case studies

This paper presents the integration of required basic facilities of living such as healthcare, education, and infrastructure for building the smart cities. The administrations of smart cities should have the smart governance, safety measures with cultural and social stimulus. Four building blocks of smart cities, i.e., people and environment, smart utilities, smart technology and smart administration are described in the present paper. The aim of this paper is to give a clearer perspective of the key decisions with spatial reference that may assume a key part in the plan of a smart city technique. Application of various technologies, for examples big data, artificial intelligence, machine learning, internet of things (IoT), cloud computing, block chain technology to the smart cities are discussed in this paper. Various challenges of smart cities such as information technology (IT) infrastructure, cost, privacy, security, efficiency, fossil fuel dependency and congested commutes with proposed solutions are also presented in this paper