Typification of load curves for DSM in Brazil for a smart grid environment Maria N.Q. Macedo ⇑ , Joaquim J.M. Galo, Luiz A.L. Almeida, Antonio C.C. Lima Department of Industrial Engineering, Federal University of Bahia, Federal Institute of Bahia, Salvador, Brazil article info Article history: Received 7 September 2014 Received in revised form 17 November 2014 Accepted 26 November 2014 Keywords: Smart grid Demand side management Load curve abstract The deployment of a smart grid environment is a worldwide trend and generates of a large volume of data. The load curve for each consumer in real time is an example of this. The challenge is the transfor- mation of these data into useful information that may help to improve efficiency in the management, planning and operation of the power grid. The implementation of demand side management (DSM) requires an analysis of the data generated in a smart grid environment to determine which policies are most appropriate for each type of consumer. Because of the large number of customers, the application of these policies involves the selection of patterns for the load curve. This study discusses the use of DSM in a smart grid environment in Brazil and presents the simulation for creating load curve patterns using the k-means technique from the consumer data of a concessionaire for the Brazilian electric system. The result obtained in this research is the creation of the load curve patterns for selecting the policies of DSM. Ó 2014 Elsevier Ltd. All rights reserved. Introduction The increased complexity of electric power systems in recent years has contributed significantly to the search for greater efficiency in their management. This implies the need for a deeper knowledge of the behaviour of the load for their networks and their customers. The use of digital technology associated with telecom- munications has provided major breakthroughs by providing systems that can supply information to improve the management of electrical systems [1–3]. A smart grid is based on the integrated use of information tech- nology, automation, telecommunications and control of the power grid, which involves smart metres, sensors and digital network management devices that are bi-directional and allow the implementation of strategies to control and optimize the electric network with real-time data processing [4]. This convergence of technologies offers a volume of data with high reliability, encompassing the values at the points of consump- tion and scheduling the evaluations of voltage, current and power losses. Thus, the power grid can be controlled with more autonomy for the consumer units, and energy management can be imple- mented in a more decentralised manner, requiring the development of new methods of control and optimization for the operation of the electric system [5]. In addition, these new devices may exhibit multiple features, such as differentiated charging, dynamic pricing and direct control load, enabling the use of techniques for demand side management (DSM) to optimize the planning and management of the electrical system [6,7]. The challenge is the transformation of data into information that is relevant to the management of the electrical system, in addition to resolving the issues of sustainability and energy conservation. Processing these data with the use of tools that include statisti- cal methods or artificial intelligence allows a greater insight into the consumer’s habits of consumption, and it contributes to the deployment of power management policies that are best suited to each case, such as DSM programs. This study discusses the use of DSM techniques in a smart grid environment and presents the simulation for creating load curve patterns that will be used to select which DSM techniques are best suited to each consumer. Section ‘Aspects of demand side management in Brazil’ of the article presents the characteristics and the policies associated with the DSM programs. Section ‘Load characterization in Brazil’ presents aspects of the characterization of the load. Section ‘Typification of the load curve in Brazil’ presents techniques for creating the patterns of the load curve. Section ‘Simulation and results’ presents the simulation and results, and Section ‘Conclusion’ presents the conclusion. http://dx.doi.org/10.1016/j.ijepes.2014.11.029 0142-0615/Ó 2014 Elsevier Ltd. All rights reserved. ⇑ Corresponding author. E-mail address: mnevesmacedo@uol.com.br (M.N.Q. Macedo). Electrical Power and Energy Systems 67 (2015) 216–221 Contents lists available at ScienceDirect Electrical Power and Energy Systems journal homepage: www.elsevier.com/locate/ijepes