Mitigating unbalance using distributed network reconfiguration techniques in distributed power generation grids with services for electric vehicles: A review

Solving optimal power flow problem with stochastic wind–solar–small hydro power using barnacles mating optimizer

This paper presents an energy management system for the microgrid present at Wroclaw University of Science and Technology. It has three components: a forecasting system, an optimizer and an optimized electrical vehicle charging station as a separate load for the system. The forecasting system is based on a deep learning model utilizing a Long Short-Term Memory (LSTM) – Autoencoder based architecture. The study provides a statistical analysis of its performance over several runs and addresses reliability and running time issues thereby building a case for its adoption. A MIDACO – MATPOWER combined optimization algorithm has been used as the optimization algorithm for energy management which intends to harness the speed of MATPOWER and the search capabilities of Mixed Integer Distributed Ant Colony Optimization (MIDACO) in finding an appropriate global minimum solution. The objective of the system is to minimize the import of power from the main grid resulting in improved self-sufficiency. Finally, an optimized electrical vehicle charging station model to maximize the renewable energy utilization within the facility is incorporated into the same.

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Microgrid Energy Management System With Embedded Deep Learning Forecaster and Combined Optimizer

Optimal location of an electrical vehicle charging station in a local microgrid using an embedded hybrid optimizer

An improved heap optimization algorithm for efficient energy management based optimal power flow model

An enhanced most valuable player algorithm based optimal power flow using Broyden's method

Soccer league optimization for load flow analysis of power systems

The problem of unbalanced load in an electrical distribution system can be solved by using a novel search method called as ant colony optimization This paper deals with the reconfiguration of a feeder system using ant colony optimization method The feeder system reconfiguration is done by opening/closing of the sectionalizing switches and tie switches This paper demonstrates the ant colony search method which is further used to solve a 3 feeder system and an optimal solution has been obtained

A novel ACO based search approach to enhance the distribution system load balance

The multi objectives in electrical distribution system are service restoration and minimization of Ohmic losses. These objectives can be achieved by artificial ant colony optimization through reconfiguration. Service restoration is also a major aspect for reliable service. If any fault occurs in the system, faulted zone is isolated from healthy system. This originates discontinuity in power supply. To maintain the continuity in power supply there is a necessity to provide service to these loads from adjacent healthy feeders through reconfiguration without infringement of radiality, load balance and critical parameters. In distribution system Ohmic losses are 30 to 40 percent. In order to minimize these losses, there is a need for reconfiguration of the system and perform the power flow analysis to get bus voltages, branch currents and power losses (i.e. real or Ohmic or copper loss and reactive power loss). A direct load flow analysis is adopted in the proposed work. In this paper four feeder test system used for service restoration and IEEE 33 bus, IEEE 69 bus test systems for minimization of Ohmic losses have been obtained.

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AACO technique for solving multiobjectives in electrical distribution system

This paper emphasizes on the optimal reconfiguration of distribution system in order to decrease the active power losses and in turn enhance the quality of power in the distribution system. The method also helps in improving voltage profile of the system as a whole. The optimization of the system is constrained by the radial configuration format, bus isolation issues and abnormal cases of combinations. The methodology is applied on to the 33 bus test system assuming constant loads and the study findings comprise of the optimal configuration of switches given by genetic algorithm.

P. Ravi Babu

Papers

An enhanced most valuable player algorithm based optimal power flow using Broyden's method

Soccer league optimization for load flow analysis of power systems

A novel ACO based search approach to enhance the distribution system load balance

AACO technique for solving multiobjectives in electrical distribution system

Genetic algorithm and reconfiguration for loss minimization in radial distribution system