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In a previous paper, we have introduced a simulation and optimization framework for the integration of renewable generators into an electrical distribution network. The framework searches for the optimal size and location of the distributed renewable generation units (DG). Uncertainties in renewable resources availability, components failure and repair events, loads and grid power supply are incorporated. A Monte Carlo simulation–optimal power flow (MCS–OPF) computational model is used to generate scenarios of the uncertain variables and evaluate the network electric performance with respect to the expected value of the global cost ( ECG ). The framework is quite general and complete, but at the expenses of large computational times for the analysis of real systems. In this respect, the work of the present paper addresses the issue and introduces a purposely tailored, original technique for reducing the computational efforts of the analysis. The originality of the proposed approach lies in the development of a new search engine for performing the minimization of the ECG , which embeds hierarchical clustering analysis (HCA) within a differential evolution (DE) search scheme to identify groups of similar individuals in the DE population and, then, ECG is calculated for selected representative individuals of the groups only, thus reducing the number of objective function evaluations. For exemplification, the framework is applied to a distribution network derived from the IEEE 13 nodes test feeder. The results show that the newly proposed hierarchical clustering differential evolution (HCDE) MCS–OPF framework is effective in finding optimal DG-integrated network configurations with reduced computational efforts. [ABSTRACT FROM AUTHOR]

Abstract Promoting renewable energy utilization is treated as a must in current energy market worldwide. This paper studies the layout optimization process for wind farms with more realistic mathematical models. It uses a recent evolutionary algorithm known as BBO (biogeographical based optimization), which produces better results for various benchmarks, especially in continuous problems. Single and multiple wake models are adopted from latest work, which gives the best approximation according to real-world experiments and exact computer-aided simulations. Cost function models have been designed to best simulate a detailed practical farm during its lifetime. Weibull distribution for wind speed over a year period with arbitrary factors is considered for the site under study; this model is applied to the farm with the uniformly distributed wind to be able to compare turbine micro sitting results. Utilization of the realistic and detailed models provides different results compared to those of generic models. The practicality of the obtained results forms the distinction between the present work and previous studies. Various tests are performed using the aforementioned wake model on the former results for wind farm layout optimization problem (WFLOP). In addition to the suggested layouts for different land types and wind models, the sensitivity analysis of financial parameters is also conducted in this work. [ABSTRACT FROM AUTHOR]