V. J. Garcia

Automatic reconfiguration of distribution networks using Smart Grid concepts

Topological reconfiguration is an important tool for the planning and operation of electric power distribution systems. Usually, the utilities can use multiple criteria regarding the observation of regulation policies and public awareness to drive the topological reconfiguration. Several researchers are looking for new optimization methods, as the complexity of this combinatorial issue is high in large systems and the classic optimization methods are failing to address the problem reasonably. Additionally, there is possibility to explore the automation equipment applying the concept of Smart Grid. This paper presents a new methodology and computer program for distribution network automatic reconfiguration. The topology is defined in real time and automatically performed by means of remote-controlled switches. The methodology includes a validation by computer simulations based on a multiple criteria decision making method. The developed tool has been applied in a pilot area of a power utility in Brazil.

Multitasked maintenance crews to serve emergency scenarios in electric distribution utilities

This paper proposes a mathematical model to handle emergency service requests in electric power distribution utilities. Given available maintenance crews and their already assigned service orders, a mathematical model based on mixed integer linear programming is developed to solve the dynamic vehicle routing problem related to the task of deciding which maintenance crew will complete each pending emergency service request. One key aspect refers to the real-time constraint, what means that computational system must be able to handle a solution to the emergency work order dispatch problem with response times in the order of milliseconds or even microseconds. First, the problem definition is presented, followed by preliminary results that have shown how suitable can be the proposed definition when addressing the referred problem and also by using Google Earth plataform. Finally, conclusions point out the main contributions of this work.

Smart grid concepts applied to self-healing in distribution system

In contingency situation, the ability to self-healing plays an important role on reducing the time to restore the energy supply. The increasing use of remote controlled equipment in power systems leads the development of more efficient techniques for automatic restoration of power supply, being particularly important in Smart Grid applications. This paper presents a methodology and system for automatic restoration of power supply in distribution network. The optimization of the network performance is based on a heuristic method and multicriterial analysis. The developed algorithms are integrated into a supervisory system, which allows real-time measurements and commands to the equipment. The proposed methodology is tested in a real network of a power utility and results are presented and discussed.

Distribution Network Reconfiguration Starting from Fuzzy Multicriteria Decision Making Algorithms

Topological reconfiguration corresponds to an important tool for planning and operation of electric power distribution systems. Considering the distribution operation planning, how much time one algorithm gets an alternative topological status for the system is not a matter of importance, since the main objective assumed involves: power losses reduction, enhancing the voltage profile and increasing the reliability levels. In this challenge, multiple criteria might be used according to the utility purpose in observation of regulation policies and public awareness. Despite the complexity of this combinatorial issue in large systems, researchers are making an effort to find effective optimization methods since the classic ones do not represent a reasonable alternative. This work has been especially motivated by these assumptions, implying the developing of a new fuzzy multicriteria decision making algorithm and software to distribution network reconfiguration, aiming at the adequate processing of the information sources available at the utilities. The effectiveness of the proposed algorithm is demonstrated with case studies involving actual distribution systems.

Introduction to Smart Operation Centers

This chapter presents an overview of technologies associated with smart grids, the automation and smart operation of Distribution Operation Centers. It describes the role and general structure of automation in important applications such as self-healing, demand response, and automatic reconfiguration of distribution networks. It also provides an overview of intelligent electronic devices and remotely controlled devices, communication protocols, supervisory systems, and information technology systems employed in distribution networks. The approach covers aspects of automation at the level of operation and planning, as well as at the substation, feeder and consumer level.

The Dispatch Problems in Power Distribution Systems

Planning the dispatch of distribution systems involves a variety of decision-making problems relating to the service crews and several equipment operations. These problems include the maintenance and repair of a service, through the routing, scheduling and assignment of vehicles and service orders. Also, in addition to the traditional offline dispatch, several technological advances have led to a renewed interest in online dispatch problems. This increases the opportunities for more optimized dispatch, but also raises the complexity of the problem. With a glance toward the power grid, the electric power distribution systems are being hugely transformed toward smart power distribution systems, integrating old and new energy players. In these systems, new energy transactions will become possible, bringing challenging problems to the system operators, in order to balance supply-demand-storage with the coordination among several players, such as smart controllable loads, distributed storage systems, intermittent power generators, reconfigurable networks, communication networks, and so on. Clearly, facing those problems will require a solid mathematical foundation for the understanding and solving of the problems at hand. Therefore, in this chapter, our goal is to introduce the reader to the study of an interesting problem that one could expect to face in the operation of a smart distribution system: the dispatch problem. To this end, along this chapter, we analyze the anatomy of the dispatch problem and study two instances which may be faced in the operation of the power distribution systems: (1) the economic dispatch problem, which deals with the (economic) dispatching of power generators and (2) the service dispatch problem, which deals with the dispatching of working personnel for attending customer, maintenance and emergency orders in the distribution system.

A computational intelligence approach to improve the efficiency of repair services in the smart grid context

Abstract In a smart grid context, self-healing is the capability of the system to perform fault location, fault isolation and service restoration in a fully automated process. Self-healing reduces the outage duration and can help improve the efficiency of the crews that must be dispatched in an emergency situation to repair the system and return it to its normal state. This work proposes an iterated local search algorithm to solve the Service Dispatch Problem (SDP) for assignment, scheduling and dispatching of those working crews to attend to emergency and regular orders. The main contribution involves simultaneously considering the working hour constraints related to the crews and the minimization of latency for both regular (off-line version) and emergency orders (on-line version). The computational results obtained from a test set of ten actual data instances of the problem highlight the effectiveness of the proposed algorithm when addressing the SDP.

Exponential smoothing for emergency service forecasting in electric power distribution utilities

The present study is aimed to estimate service time on the emergency service orders of an electric company. For this, a sequence of steps has been developed and described for the structuring of a demand forecasting system, which must be able to design patterns and tendencies of the analyzed data from past demands. Intending to meet these assumptions, the study sought to support the prediction method by exponential smoothing. The results obtained with this method, assist the process of service demand per hour and day of the week.

Strategy for the management of service orders in electrical energy concessionaires

The present study contributes to the service order management of electric concessionaires. The proposed methodology and policy of mixed prioritization, based on the deadline and the processing time, demonstrated to be pertinent to the problem characteristics. The results found in the conducted case study were shown to be promising for the significant reduction in processing time of the shortest solicitation requests and the amount of requests overdue at the end of the analyzed period.

Stochastic methodology for service management modeling in electric power utilities

This work proposes a methodology to modeling the probability of occurrences of emergency orders in certain regions covered by an electricity distribution utility based on the historical data, in order to promote a further pro-active routing of the commercial orders. The methodology proposed aims to reduce the average service time, which is defined as the sum of the travelling time plus the execution time. The other concern refers to the priority of this type of orders, being the emergency orders of high priority when compared to the commercial ones. By first stratifying the historical data over five week days, from Monday to Friday, one can conduct a work order forecasting related to emergency services, to further discount these amount of time from the total workday hours of all available teams. A given case study has shown how one could apply this methodology to predict emergency order.