This article presents a simple model to identify the proper location and size of distributed generations (DGs) in radial distribution networks. The proposed model is a combination of a 2-stage framework that determines a suitable location for DG and appropriate size at obtained location. An index is formulated that combines aspects of the real power losses in the network and the voltage stability condition of the network. The index is defined as the DG selection index, and it is used to determine the proper bus location for the DG. On the basis of a DG sizing formula presented in the article, the optimal generation capacity of a DG unit is determined. The proposed methodology is tested on 12-bus, 33-bus, and 69-bus radial distribution network models that consider different operational power factor operation modes of DGs. The simulation results are compared with existing analytical approaches to demonstrate the effectiveness of the proposed methodology in radial network systems.
Voltage stability condition, reliability and service quality are the important parameters of a distribution system which need to be satisfied from the customer point of view.To measure voltage stability of distribution network a voltage stability index is presented in this paper. Voltage stability improvement of network is facilated by network reconfiguration and voltage stability level is quantified by voltage stability index. Network reconfiguration is a process which alters the feeder topological structure by changing the open/close status of the sectionalizing (normally closed) and ties switches (normally open) in the system. In this work, a two stage search of switching option i.e. local search and global search is implemented to achieve desired network configuration. A multilayer ANN model with Error Back Propagation Learning (EBPL) algorithm is simulated for global search to obtained optimal set of candidate switching. The proposed scheme is tested on an 11 kV practical radial distribution system consisting of 52 buses. After reconfiguration, better voltage stable condition of the system is attained. Active power loss of the system is reduced from 0.854 MW to 0.485 MW and reactive power loss of the system is reduced from 0.355MVar to 0.199MVar after achieving optimal configuration.
Reversible solid oxide cells can provide efficient and cost-effective scheme for electrical-energy storage applications. However, this technology faces many challenges from material development to system-level operational parameters , which should be tackle for practical purposes. Accordingly, this study focuses on developing novel robust artificial intelligence-based blackbox models to optimize operational variables of the system. A genetic-programming algorithm is used for Pareto modeling of reversible solid oxide cells in a multi-objective fashion based on experimental input-output data. The robustness of the obtained optimal model evaluated using Monte Carlo simulations technique. An optimization study adopted to optimize the operating parameters, such as temperature and fuel composition using a differential evolution algorithm. The objective functions that have been considered for Pareto multi-objective modeling process are training error and model complexity. In addition, the discrepancy between maximum and minimum output voltage in the whole operation of the system is chosen as the optimization process objective function. The robustness of the optimal trade-off model is shown in terms of statistical indices for varied uncertainty levels from 1 to 10%. The optimized operational condition based on the suggested model reveals optimal intermediate temperature of 762 °C and fuel mixture of about 29% H 2 , 25% H 2 O, and 14% CO.
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