Static and Dynamic Distribution Network Reconfiguration with the Integration of Renewable Energy Sources

To satisfy the growing energy demand, renewable distributed generation units are being widely incorporated to the power system. Several optimisation studies have been performed to find the most appropriate size and placement of these sources using a fixed distribution network structure. However, the results produced may work for only one network configuration while producing unsatisfactory results for other probable network structures. Therefore, this paper explores the optimisation of the renewable energy sources namely photovoltaic systems and wind turbines along with distribution network reconfiguration. It investigates on both static and dynamic reconfiguration. For the static reconfiguration, three optimisation algorithms namely non-dominated sorting genetic algorithm II, strength pareto evolutionary algorithm 2 and multi-objective evolutionary algorithm based on decomposition were utilised in the IEEE-123 Node Test Feeder System. Artificial neural network, group method of data handling and adaptive network based fuzzy inference system models were adopted to predict the load demand one-day ahead for the implementation of dynamic network reconfiguration. The load curve was then partitioned over 24-hour intervals to perform static reconfiguration on an hourly basis. The simulations revealed that the incorporation of renewable distributed generation units along with the change in the network configuration helps to reduce the active power loss. It was found that dynamic reconfiguration reduces the energy loss by almost 60 % while static reconfiguration results in only a slight reduction in the energy loss.