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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: Distributed generation expansion planning (DGEP) has been frequently reported in the literature around the world. In this scope, renewable technologies which are considered as a kind of distributed generations are developing due to their environmental benefits. However, only a few renewable energies have proven to be competitive so far, while their economic viability is also limited to certain regions of the world. In this paper, an encouraging mechanism is proposed in favor of clean technologies in the planning process. This mechanism is defined based on a grant function of emission not polluted which is paid to DG owners to promote renewable and clean technologies. In the planning process, a multi-objective optimization algorithm is applied to produce a Pareto set of optimal planning schemes by taking into account different objective functions (cost and grant functions). The best planning scheme among the Pareto set is chosen based on a composite utility which are obtained through a Monte Carlo simulation of uncertain situations. Distributed generation technologies which are considered in this paper are conventional and renewable technologies, namely photovoltaic (PV), wind turbine (WT), fuel cell (FC), micro turbine (MT), gas turbine (GT), and reciprocal engine (RE). To assess the ability of the proposed method, a typical distribution system is used for expansion planning under two environmental scenarios. [Copyright &y& Elsevier]

A microgrid is obtained from the integration of several distributed energy resources such as fuel cells and storages. Droop control is a method for load sharing between sources or storages in a microgrid. In the conventional droop control method, one can only achieve one of the low voltage drops and accurate load sharing requirements by considering the line resistances. This paper proposes a new non-centralized control approach based on low-bandwidth communication (LBC) and the droop control method for the DC microgrid (DC-MG). The droop gain is calculated with respect to the output currents of the distributed generation (DG) units. Factors like different line resistances, different DG capacities, and the presence of local loads do not affect the controller performance. All calculations and control operations are performed locally and the LBC is used solely for transferring current information—this ensures high reliability. Simulations and experimental results show that the use of the proposed control method decreases voltage deviations and the effects of constant power loads (CPLs), while it increases system efficiency and the accuracy of load sharing. [ABSTRACT FROM AUTHOR]

This paper tests and analyzes the working capability of a resistive superconducting fault current limiter (SFCL) in a microgrid. The microgrid is a test-bed, consisting of a conventional power plant, two renewable energy sources in the form of wind-farm and solar-farm as two distributed generation (DG) units and five loads (i.e., industrial and domestic). Utilization of DG in mainstream is increasing and hence, various consequences that arise from DG penetration are to be considered accordingly. It is observed that DG penetration in existing distribution alters the fault current during a grid disturbance and hence imbalances the system. Based on this, the research work focuses on contribution of fault current from renewable energy sources in a microgrid, and how resistive SFCL offers positive result in minimizing the fault levels. The test-bed is modeled in MATLAB/Simulink, and simulation results are presented to show the effectiveness of resistive SFCL during various fault conditions. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents the analysis and comparison of the main active techniques for islanding detection used in grid-connected microinverters for power processing of renewable energy sources. These techniques can be classified into two classes: techniques introducing positive feedback in the control of the inverter and techniques based on harmonics injection. Accurate PSIMTM simulations have been carried out in order to perform a comparative analysis of the techniques under study and to establish their advantages and disadvantages according to IEEE standards. [ABSTRACT FROM AUTHOR]

Abstract: Renewable distributed generation introduced as an environmental friendly alternative energy supply while it provided the power system with ever-growing technical benefits such as loss reduction and feeder voltage improvement. The evaluation of the effects of small residential photovoltaic and wind DG systems on various system operating indices and the system net load is complicated by both the probabilistic nature of their output and the variety of their spatial allocations. The increasing penetration of renewable distributed generation in power systems necessitates the modeling of this stochastic structure in operation and planning studies. An advanced stochastic modeling of the system requires multivariate uncertainty analysis involving non-normal correlated random variables. Such an analysis is to epitomize the aggregate uncertainty corresponding to spatially spread stochastic variables. In this paper, an integration study of photovoltaics and wind turbines, distributed in a distribution network, is investigated based on the stochastic modeling using Archimedean copulas as a new efficient tool. The basic theory concerning the use of copulas for dependence modeling is presented and focus is given on an Archimedean algorithm. A comprehensive case study for Davarzan area in Iran is presented after reviewing Iran''s renewable energy status. This study shows an application of the presented technique when large datasets, assuming 10-min interval between data points of PV, wind and load profiles, are involved where a deterministic study is not trivial. [Copyright &y& Elsevier]