Multi-objective distributed generation integration in radial distribution system using modified neural network algorithm

This paper introduces a new approach based on a chaotic strategy and a neural network algorithm (NNA), called chaotic-based NNA (CNNA), to solve the optimal distributed generation allocation (ODGA), in the radial distribution system (RDS). This consists of determining the optimal locations and sizes of one or several distributed generations (DGs) to be inserted into the RDS to minimize one or multiple objectives while meeting a set of security limits. The robustness of the proposed method is demonstrated by applying it to two different typical RDSs, namely IEEE 33-bus and 69-bus. In this regard, simulations are performed for three DGs in the cases of unity power factor (UPF) and optimal power factor (OPF), considering single and multi-objective optimization, by minimizing the total active losses and improving the voltage profile, voltage deviation (VD) and voltage stability index (VSI). Compared to its original version and recently reported methods, the CNNA solutions are more competitive without increasing the complexity of the optimization algorithm, especially when the RDS size and problem dimension are extended

New typical power curves generation approach for accurate renewable distributed generation placement in the radial distribution system

This paper investigates, for the first time, the accuracy of normalized power curves (NPCs), often used to incorporate uncertainties related to wind and solar power generation, when integrating renewable distributed generation (RDG), in the radial distribution system (RDS). In this regard, the present study proposes a comprehensive, simple, and more accurate model, for estimating the expected hourly solar and wind power generation, by adopting a purely probabilistic approach. Actually, in the case of solar RDG, the proposed model allows the calculation of the expected power, without going through a specific probability density function (PDF). The validation of this model is performed through a case study comparing between the classical and the proposed model. The results show that the proposed model generates seasonal NPCs in a less complex and more relevant way compared to the discrete classical model. Furthermore, the margin of error of the classical model for estimating the expected supplied energy is about 12.6% for the photovoltaic (PV) system, and 9% for the wind turbine (WT) system. This introduces an offset of about 10% when calculating the total active losses of the RDS after two RDGs integration

Meta-heuristic optimization methods applied to renewable distributed generation planning: A review

Due to its proven efficiency and computational speed, the most recent developed meta-heuristic optimization methods are widely used to better integrate renewable distributed generation (RDG) into the electricity grid. The main objective of this paper is to obtain a better knowledge of current trends in meta-heuristics applied to optimally integrate RDGs to the distribution network. This is a review of well known meta-heuristic approaches, used to solve the problem of optimal renewable distributed generation allocation planning (ORDGAP). In this context, some research gaps were mentioned, and recommendations were proposed to expand the scope of research in this field