Search

Summary: Integration of renewable energy sources into a distribution system requires adjustments and improvement of the existing relay protection systems. Application of the three‐phase reclosing in radial distribution networks often leads to the island operation of the distributed power generation, which causes the generator's disconnection from the network. The application of the upgraded single‐phase reclosing technique, which is proposed in this paper, enables a partial transfer of energy into the electric power system during the temporary fault. Thus, it contributes to the maintenance of the distributed generator transient stability during temporary faults in the distribution network. Improvement of single‐phase reclosing technique is reflected in the adaptation to eliminate various types of faults that may occur. The analyses include different operation conditions and stability requirements of distributed generators. The proposed solution is presented in the paper by example of different types of distributed generation: a small hydropower plant with a synchronous generator that is directly connected to the distribution network, and a wind power plant with double fed induction generator and photovoltaic systems. The results of the analyses are presented in the form of dynamic simulations performed by DIgSILENT PowerFactory software using IEEE 33‐bus model of distribution network. [ABSTRACT FROM AUTHOR]

The global trends toward increasing clean and sustainable power generation have brought significant changes and developments in electrical distribution networks. The existing passive networks are being restructured to facilitate renewable distributed generations to ensure high degree of reliability of power supply to customers. A distribution system reliability assessment (DSRA) is crucial to analyze the risk and cost associated with power interruptions and take necessary preventive and corrective actions. This paper presents an approach to DSRA and carries out a comprehensive reliability assessment of an active distribution network (ADN) connected with solar, wind, and tidal energy sources. A multi‐state reliability model is developed, combining the Markov and well‐being frameworks to determine the reliability of a network energized by multiple renewable energy sources (RESs). The load point reliability and worth‐oriented indices, namely SAIDI, SAIFI, CAIDI, AENS, EENS, ECOST and IEAR, are evaluated to analyze the impacts of the three RESs on the reliability of the ADN. The paper also proposes two new metrics to estimate the benefits of adding RESs from the reliability worth and cost perspectives. The analyses have been carried out on the Roy Billinton Test System. The results show that the maximum reliability worth and cost benefits can be extracted from a combined operation of multiple RESs. A combination of solar‐wind‐tidal energy sources, therefore, can be an effective solution for countries having long coastlines to fulfill not only the sustainable energy goals but also to improve the grid's reliability and reduce the monetary losses incurred due to customer interruptions. [ABSTRACT FROM AUTHOR]

Summary: Renewable energy sources powered distributed generation (RES‐DG) is getting more indispensable to encounter the considerable increase in demand for electric energy owing to its techno‐economic benefits and eco‐friendly nature. An economic solution to this demand can only be obtained with the optimal placement and sizing of RES‐DGs. The optimal siting and sizing of RES‐DG, such as Photovoltaic (PV) and Wind Turbine (WT) is still a hot topic due to the uncertainties in solar irradiance (SI) and wind speed (WS). The main objective of this research paper is to develop a RES‐DG siting and sizing strategy for the discrete, nonlinear siting and sizing pattern of RES‐DGs using a novel hybrid Harris' Hawk optimizer (HHHO), considering the stochastic nature of SI and WS. The Weibull and Beta probability density functions (PDFs) are utilized for modeling the stochastic nature of WS and SI, respectively. The optimization of the multiobjective function comprises active power loss reduction, enhancement in voltage profile, and improvement in voltage stability index (VSI). Different scenarios of single and multiple RES‐DGs and capacitor banks (CB) are examined to validate the efficiency of the proposed novel HHHO based RES‐DGs siting and sizing strategy. The results show a considerable reduction in power loss, enhancement in the system voltage profile, and improvement in VSI. Evaluation of results by comparing withstate‐of‐art hybrid algorithms shows that the proposed solution using HHHO algorithm is globally optimum. [ABSTRACT FROM AUTHOR]

This paper proposes a solution methodology for optimal allocation and sizing of renewable based DG units in radial distribution systems. The objectives are to minimize total power losses, to minimize system's investment cost and to improve voltage stability. The multi-objective genetic optimization algorithm is used to generate the optimal Pareto front and a fuzzy decision making function is used, as a hybrid function, to obtain the best compromise solution. The proposed technique is applied to typical IEEE bus systems including 33-bus system, 69-bus system and 119-bus system. This technique is implemented using different scenarios including all possible combinations of renewable DG units; to prove the effectiveness of the proposed methodology. The effect of DG based renewable energy allocation on the most sensitive buses to collapse is also carried out considering different load models. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Summary: Determining the penetration level of the distributed generation (DG) is an effective tool to site and size DGs in distribution networks. A multiobjective function of optimizing the DG penetration level, subject to constraints including short circuit capacity, voltage limits, transformer capacity, reverse power flow, and congestion of lines, is introduced to minimize the real and reactive losses and to ensure the voltage stability considering the variations of loads, different locations and power factors of DGs, and statistical nature of DG powers especially renewable energy resources. This optimization is performed based on a combined probabilistic modeling of the chronological behavior of wind speed, solar irradiance, and load with different types. The dynamical behavior of the compensators and on‐load tap changers is modeled by optimization subfunctions. An improved bee algorithm is used through conducting searches in the neighboring areas based on a new nonlinear function for a higher fitness and coherency speed. [ABSTRACT FROM AUTHOR]