This paper presents a robust control scheme for DISTRIBUTED GENERATIONS (DGs) in islanded mode operation of a microgrid (MG). In this strategy, assuming a dynamic slack bus with constant voltage magnitude and phase angle, nonlinear equations of the MG are solved in the slack-voltageoriented synchronous reference frame, and the instantaneous active and reactive power reference for the slack bus is obtained at each time step, based on Y_bus equation of the MG. The slack bus power references are robustly tracked by the proposed adaptive sliding mode based power controller. In addition, a hyper-plan sliding controller is suggested for other DGs that provides three regulators including active power, reactive power and voltage regulator for DG units and ensures protection of the power electronic interfaces to the faults assumed to have occurred in the MG. At each step time, DGs are modeled as positive and negative current sources that are controlled by their adaptive sliding mode controllers in the normal and abnormal operating conditions. All the parameters of controllers are derived via particle swarm optimization (PSO) algorithm in order to minimize an appropriate cost function. Performance of the proposed control strategy is compared to the performance of the conventional master-slave based control strategy. The validity and effectiveness of the presented method are supported by time domain simulation of a test microgrid in MATLAB.

This paper presents a new fault location method for radial distribution networks with DISTRIBUTED GENERATIONS.The proposed fault location algorithm uses the voltage and current data obtained by digital fault recorder installed at the head of the network main feeder, with or without the data from any digital fault recorders installed at DG connection points. The algorithm is based on fundamental frequency calculations and includes novel subroutines of current calculation of DG contained laterals and average loading factor and power factor estimation of the network transformers to solve the fault location problem. The method has been tested by simulation studies using EMTP on a 205-node 20kV radial distribution network containing three DG's for different data acquisition scenarios. The results verify accuracy of the method under different fault conditions even with only one measuring point at the head of the main feeder.

This paper has developed a novel multiobjective function for optimal sizing and sitting of DISTRIBUTED Generation (DG) units and capacitor banks in simultaneous mode to improve reliability and reduce energy losses. The proposed function consists of four objectives: Cost of Energy Not Supplied (CENS), System Average Interruption Duration Index (SAIDI), costs of energy loss and investment. A novel structure has been suggested for Differential Evolutionary Algorithm (DEA) to solve this nonlinear complex problem and its results compared with related values of genetic algorithm and simple DEA. In addition to the novel objective function, the other contribution of this work is proposing a new model for load and energy cost. Three types of DGs, i.e., wind turbine, solar cell and diesel generator have been employed in placement process. To verify the comprehensiveness of the proposed function, three scenarios have been introduced: Scenario i) First, placement of DGs, then capacitor banks, Scenario ii) First, placement of capacitor banks, and then DGs, and Scenario iii) simultaneous placement of DGs and capacitor banks. Simulations have been carried out on one part of practical distribution network in Metropolitan Tabriz in North West of Iran. The results of simulations have been discussed and analyzed by using of the five novel indices. The obtained simulation results using proposed function shows that the simultaneous placement of DISTRIBUTED GENERATIONS and capacitor banks results in more reduction of the energy losses, and increase improvements of reliability indices as well as voltage profile.

Due to the widespread adoption of DC source-based DISTRIBUTED energy resources (DERs) and loads, alongside advancements in power electronics technology, DC microgrids (DC MGs) have recently gained significant attention. To effectively implement DC MGs, it is crucial to employ a suitable control strategy that maintains the bus voltage at the desired level and ensures appropriate power sharing among the integrated DERs. To address these objectives, a two-layer control scheme is proposed in this paper. In the primary layer, a customized droop control scheme is introduced, which applies lower voltage drop in comparison to the conventional droop strategies. Simultaneously, in the secondary layer, a modified voltage controller  which is supplemented by a term to enhance power sharing in a DISTRIBUTED manner is employed,. The proposed control strategies are characterized by their simplicity and low communication infrastructure requirements. To assess the efficacy of the proposed control architecture, several case studies, including plug and play integration, load variations, and communication challenges, including link disconnection and noise effects, are conducted. Additionally, the performance of the proposed strategies is benchmarked against an architecture featuring conventional primary droop control and cooperative DISTRIBUTED secondary control approaches. The simulation studies conducted in MATLAB/SIMULINK software demonstrate that the proposed control methods outperform the alternative approaches, confirming their effectiveness in maintaining voltage regulation and power sharing objectives.

This paper presents sub-transmission and distribution network expansion planning (S&DEP) including DISTRIBUTED generation (DG) and distribution automation (DA) considering reliability indexes. The objective function is to minimize investment, operation, maintenance and reliability costs subjected to AC power flow, system operation and generating units and DG limits, reliability, and distribution automation constraints (including the constraints of protection devices and volt/VAr control mechanism). The proposed model is a mixed integer non-linear programming (MINLP) model which is hard to solve. For this reason, a MINLP problem is transformed to mixed integer linear programming (MILP) model. The validity of the proposed method is investigated in the two synthetic test networks.

In this paper, to implement the reconfiguration of the intelligent distribution network and sizing the DISTRIBUTED GENERATIONS (DGs), simultaneously, a stochastic multi-objective framework is presented in which the goals of reducing the energy losses, operating costs and emissions of environmental pollutants are followed. In most of the researches carried out for the network reconfiguration, the customer and distribution substation loads have been assumed to be fixed, while in reality these loads are changing. Generally, in the power system planning, the worst case which is the maximum possible load is considered, but the obtained configuration with this assumption is certainly not the optimal configuration. Therefore, in this paper, in order to cover the uncertainties associated with the load, the framework for the reconfiguration and DGs sizing problem is presented as a chance constrained programming problem and the Monte Carlo simulation is used in it. The problem of reconfiguration and DGs sizing at the same time in order to achieve predetermined goals is a multi-objective optimization problem. In order to solve it, this paper uses a newly introduced powerful optimization method called human-behavior based optimization approach. Finally, the proposed stochastic multi-objective framework is implemented on an IEEE 33-bus network, and the results are analyzed.

This paper presents a long-term dynamic multi-objective model for DISTRIBUTED generation investment. The proposed model optimizes three objectives, namely active losses, costs and environmental emissions and determines the optimal schemes of sizing, sitting of DG units and specially the dynamics of investment over the planning period. The Pareto optimal solutions of the problem are found using a GA algorithm and finally a fuzzy satisfying method is applied to select the optimal solution considering the desires of the planner. The solutions of Pareto optimal front are analyzed to extract general useful information for planners about the appropriate DG technologies and placement schemes. The effectiveness of the proposed model is demonstrated by applying it on a test distribution system and the results are presented, discussed and compared to other methods.

In this paper, a new method for pricing of DISTRIBUTED GENERATIONS (DG) based on contribution of each zone of distribution network to reliability enhancement is proposed. In the proposed method, some flaws such as zero reliability price allocation to DGs and non-zero merchandising surplus are eliminated. In order to determine the share and price of each zone, cooperation game theory is utilized. For reliability evaluation of distribution network, Markov chain in used in which the operation of DGs in islanded mode is considered as well.