Distribution system planners usually provide dedicated feeders to its different class of customers, each of whom has its own characteristic load pattern which varies hourly and seasonally. A more realistic modeling should be devised by considering the daily and seasonal variations in the aggregate load patterns of different class of customers. This paper addresses a new methodology to provide integrated solution for the optimal allocation of distributed generations and network reconfiguration considering load patterns of customers. The objectives considered are to maximize annual energy loss reduction and to maintain a better node voltage profile. Bat algorithm (BA) is a new bio-inspired search algorithm which has shown an advance capability to reach into the promising region, but its exploration is inadequate. The problem is solved by proposing the improved BA (IBA). The proposed method is investigated on the benchmark IEEE 33-bus test distribution system and the results are very promising.

Technologies such as the integration of distributed generations (DGs) and shunt capacitors (SCs) are key for realising smart distribution systems. These technologies may be coordinated together to get better solutions so that distribution systems can achieve optimum performance. This study proposes a long‐term distribution system planning methodology to determine the optimal sizing and siting of SCs and mix of dispatchable and intermittent DGs. More practical formulations are suggested while considering realities of modern distribution systems with reference to the stochastic nature of load demand and power generation among distribution buses on account of diversity, variability and uncertainty. The long‐term costs in the proposed planning model include several techno‐economic and social objectives pertaining to investment, operation and maintenance of these distributed resources and the revenue generated by sale of electricity to customers and the utility grid. The proposed planning model is more practical as it fully considers the system uncertainties and variability in load and power generation which are efficiently handled by introducing deterministic self‐adaptive uncertainty model. The case study on a standard test distribution system being modified by wind turbines, photovoltaic generators and microturbines demonstrates the effectiveness of the proposed methodology. The results of study are investigated and presented.

This paper addresses an application of Teaching-Learning-Based Optimization method for the optimal allocation of Distributed Generations (DGs) in radial distribution systems. The problem is formulated to maximize annual energy loss reduction while maintaining better node voltage profiles using penalty function approach. A piecewise linear multi-level load pattern is considered, and the distribution network is reconfigured after optimal placement of DGs in the distribution network. A probability-based heuristic intelligent search (IS) is suggested to enhance the accuracy and convergence of the optimization techniques. IS directs optimization techniques to efficiently scan the problem search space in such a way that a fair candidature is available to all decision variables of the problem. It virtually squeezes the search space while maintaining adequate diversity in population. The proposed method is investigated on the benchmark IEEE 33-bus, 69-bus test distribution systems, and 83-bus real distribution system. The application results show that the proposed optimization methodology provides substantial improvement in convergence characteristics and quality of solutions.