Optimal distributed generation allocation and sizing in radial distribution networks by Cuckoo search algorithm

Modern smart grid implementations bring several advantages in terms of operational controls. In the last decade, the smart grid has been introduced to accommodate high penetration of such renewable resources and make the power grid more efficient, reliable and resilient. The main idea behind the smart integration is locating, sizing and operating renewable-based Distributed Generation (DG) resources which are typically located in distribution systems and near load centers by considering various technical, economical and environmental issues. Distributed generation and storage are probably the most important concepts that are used to modify the quality and the reliability of the consumed energy. Allocating DG units in distribution systems may inflict unwanted challenges in traditional power systems, which have been designed radial and unidirectional. DG sizing and sitting problems have been introduced. Numerous advantages such as decreasing power losses and improving voltage profiles were attained by integrating Distributed Generation (DG) in distribution systems. Such benefits can be achieved and enhanced if DGs are optimally sized and located in the systems. Numerous researches have studied several aspects of the problem using different optimization methods such as heuristic methods, analytical approaches and computational artificial intelligence. However, both the problem formulations and the applied methods still suffer from several drawbacks. In modern load growth scenario, uncertainty of the load and the generation model shows that the reduction of power loss in distribution system is possible and all node voltage magnitudes can be maintained within specified limits without violating the thermal limit of the system. Power losses surely have important effect on the revenues of electric power companies, greenhouse gases emission, and the cost of the energy supplied. This thesis presents an optimal DG allocation and sizing approach in radial distribution networks taking seasonal load variations effects into account. Continuous nonlinear meta-heuristic Cuckoo search algorithm is used to solve the resulting constrained optimization problem. The proposed approach is applied to an IEEE 12-bus radial distribution test system in MATLAB environment and the results are discussed to validate the performance of the proposed approach. Forward-Backward Sweep Method (FBSM) is chosen to solve the load flow equations. The optimal DG placement and sizing problem is formulated for two basic objectives. First, optimization problem is treated as two separated single-objective optimization problem, where the percentage daily energy losses are considered as the first objective to be minimized and total daily voltage variation is considered as the second objective function. Secondly, the problem is tackled as a multi-objective one, focusing on percentage daily energy losses as well as total voltage violations of the networks.