Linear MW-only ldquodcrdquo network power flow models are in widespread and even increasing use, particularly in congestion-constrained market applications. Many versions of these approximate models are possible. When their MW flows are reasonably correct (and this is by no means assured), they can often offer compelling advantages. Given their considerable importance in today's electric power industry, dc models merit closer scrutiny. This paper attempts such a re-examination.

DC Power Flow Revisited

Series Editor's Introduction Preface Acknowledgments 1. The Idea of Agent-Based Modeling 1.1 Agent-Based Modeling 1.2 Some Examples 1.3 The Features of Agent-Based Modeling 1.4 Other Related Modeling Approaches 2. Agents, Environments, and Timescales 2.1 Agents 2.2 Environments 2.3 Randomness 2.4 Time 3. Using Agent-Based Models in Social Science Research 3.1 An Example of Developing an Agent-Based Model 3.2 Verification: Getting Rid of the Bugs 3.3 Validation 3.4 Techniques for Validation 3.5 Summary 4. Designing and Developing Agent-Based Models 4.1 Modeling Toolkits, Libraries, Languages, Frameworks, and Environments 4.2 Using NetLogo to Build Models 4.3 Building the Collectivities Model Step by Step 4.4 Planning an Agent-Based Model Project 4.5 Reporting Agent-Based Model Research 4.6 Summary 5. Advances in Agent-Based Modeling 5.1 Geographical Information Systems 5.2 Learning 5.3 Simulating Language Resources Glossary References Index About the Author

What are agent-based models?

Short-term electrical load forecasting using the Support Vector Regression (SVR) model to calculate the demand response baseline for office buildings

State-of-the-art, challenges, and future trends in security constrained optimal power flow

Advances in information and communication technologies (ICT) enable a great opportunity to develop the residential demand response that is relevant in smart grid applications. Demand response (DR) aims to manage the required demand to match the available energy resources without adding new generation capacity. Expanding the DR to cover the residential sector in addition to the industrial and commercial sectors gives rise to a wide range of challenges. This study presents an overview of the literature on residential DR systems, load-scheduling techniques, and the latest ICT that supports residential DR applications. Furthermore, challenges are highlighted and analyzed, which are likely to become relevant research topics with regard to the residential DR of smart grid. The literature review shows that most DR schemes suffer from an externality problem that involves the effect of high-level customer consumption on the price rates of other customers, especially during peak period. A recommendation for using adaptive multi-consumption level pricing scheme is presented to overcome this challenge. *Corresponding author at: Department of Electronics and Communication Engineering, Tenaga Nasional Universiti, P13-B-07-06, Sri Cempaka, Jalan Sepakat Indah 2/2 Taman Sep, Kajang 43000 Selangor, Malaysia. Tel.: +60 183262643.

A review of residential demand response of smart grid

The method classifies more than one PQ event within the same temporal window.The algorithm was based on Wavelet Transform and Support Vector Machine (SVM).The classification method uses One vs. One multiclass SVM.The algorithm was tested using several real PQ events obtained from the field.More than 92% of the measured PQ events were successfully detected and classified. In this paper we propose a method based on a combination of binary classifiers which are optimized for those special cases where the real signals contain a multitude of events within the analyzed temporal window. These type of events are known as complex events. The proposed Power Quality (PQ) classifier is based on Wavelet Transforms (WT) and Support Vector Machines (SVM). The method uses a One vs. One multiclass SVM. We propose a novel method which is simple, easy to train, and can be implemented with low computational cost. The proposed algorithm consists of a set of simple binary SVM classifiers. Each SVM node is trained separately allowing them to be parallelized. The training stage is performed using single events, however due to the structure of the SVM methodology selected, it allows the system to detect complex events. Tests and training were performed using real complex signals and the results show the proposed methodology to be highly efficient.

An effective Power Quality classifier using Wavelet Transform and Support Vector Machines

Impact of demand response resources on unit commitment and dispatch in a day-ahead electricity market

Security constrained unit commitment (SCUC) is becoming a key calculation in modern electric energy markets. This calculation extends conventional unit commitment (UC) to include the pre- and post-contingency constraints of the power transmission network. Such constraints complicate the problem considerably, and much work remains to be done to implement them satisfactorily. This paper offers a discussion of the analytical and computational challenges that they pose

Fernando Magnago

Papers

An effective Power Quality classifier using Wavelet Transform and Support Vector Machines

Impact of demand response resources on unit commitment and dispatch in a day-ahead electricity market

Security Constrained Unit Commitment: Network Modeling and Solution Issues

Symmetry issues in mixed integer programming based Unit Commitment

Hardware and software architecture for power quality analysis