Celso Cavellucci

Energy Losses Estimation in Power Distribution Systems

Estimating technical losses is fundamental to the planning and economics of electric power networks. This paper surveys the evolution of the ideas behind energy loss estimation and focuses on the development of the concepts of the loss factor and equivalent hours. The paper next identifies difficulties in using maximum demands and the loss factor to estimate energy losses. Based on this analysis, this study proposes an alternative loss estimation approach that relies on the “loss coefficient” as the fundamental parameter for describing load variations in loss estimation. A large load-curve data bank from Brazilian utilities is used to characterize load-curve parameters and provide perspective on the old and new concepts. Practical applications put the proposed ideas into perspective, showing how the use of average demands and loss coefficient can help to make better cable choices, increase accuracy in loss estimation for distribution transformers, and enhance the quality of information in loss estimation analysis.

Switch Allocation Problems in Power Distribution Systems

Reliability analysis of power systems has been attracting increasing attention. Regulatory agencies establish reliability standards that, if infringed, result in costly fines for the utility suppliers. A special concern pertains to the distribution networks on which most failures occur. The allocation of switches is a possible strategy to improve reliability, by allowing network reconfiguration to isolate contingencies and restore power to dark areas. This paper proposes an optimization methodology to allocate switches on radially operated distribution networks. The solution framework considers sectionalizing and tie switches of different capacities, with manual or automatic operation schemes. The approach minimizes the costs of allocation and energy not supplied, under reliability and flow capacity constraints. The solution framework is based on memetic algorithm concepts with a structured population. Case studies with a large network and real-world scenarios were used to evaluate the methodology. The results indicate that significant cost reductions can be achieved using the proposed solutions.

Minimization of energy losses in electric power distribution systems by intelligent search strategies

Energy is continuously dissipated in electric power systems due to electrical resistance in transmission and distribution lines. This paper addresses the problem of obtaining a network topology with minimum energy losses for electric power distribution systems. As distribution networks must operate radially, the problem can be formulated as a generalization of the minimum spanning tree problem. The generalization is due to variation in costs as network configuration changes. Nonlinear network flow techniques are teamed with search strategies borrowed from the field of artificial intelligence to overcome computation intractability.

Simultaneous capacitor placement and reconfiguration for loss reduction in distribution networks by a hybrid genetic algorithm

There are two common strategies for technical loss reduction in electric power distribution networks: (a) the installation of capacitor banks to compensate the losses produced by reactive currents; and (b) the redefinition of the topology of electric distribution networks by changing the state of some sectionalizing switches to balance the load. Both strategies can be formulated as combinatorial optimization problems. The optimization problems for the first and the second strategies are usually known as Capacitor Placement Problem (CPP) and Network Reconfiguration Problem (NRP), respectively. In this paper, we propose a new approach based on Genetic Algorithm (GA) to solve both CPP and NRP simultaneously. The new approach makes use of two previously proposed and independent techniques for the CPP and the NRP. The performance of the new approach is compared with the performance of the two previously proposed techniques applied in a separate manner. The experiments show that the new method is more efficient regarding the metrics of power loss reduction and voltage profile enhancement.

Hierarchical multiple criteria optimization of maintenance activities on power distribution networks

Power distribution utilities must transport quality and reliable electric energy to all of the customers in a given network within specified targets. Failure of one of the network components is the main factor that compromises a system’s reliability. Maintenance actions, such as repairs and component replacements, are employed to avoid power interruptions or to resume the healthy network operation. This paper reports the relationship between maintenance activities and reliability as an optimization problem with two criteria: cost of maintenance activities and the maximum value for a system average interruption frequency index planning period. Solving these problems for each network provides local efficient solutions and associated trade-off curves. These solutions are optimally combined to solve a global-level multiple criteria optimization problem, revealing the local efficient solutions and associated trade-off curves for a group of networks or for a whole company. The procedure solves the hierarchical multiple criteria maintenance resources allocation problem and provides information to assess the decisions on maintenance activities at all decision levels in the network management. This procedure is applied to one illustrative example and real-life case studies to demonstrate its benefits.

Minimization of Energy Losses in Electric Power Distribution Systems by Intelligent Search Strategies

Abstract Energy is continuously dissipated in electric power systems due to electrical resistance in transmission and distribution lines. This paper addresses the problem of obtaining a network topology of minimum energy losses for electric power distribution systems. As distribution networks must operate radially, the problem can be formulated as a generalization of the minimum spanning tree problem. The generalization is due to variation in costs as the network configuration changes. The approach developed to find a global optimum merges nonlinear network flow techniques and search strategies borrowed from the field of artificial intelligence to overcome computation intractability.