In this paper, an attempt has been made to introduce a new control strategy including Plug-in Hybrid Electric Vehicle (PHEV) and Diesel engine generator to control the voltage and frequency of autonomous microgrids. The proposed control strategy has multiple advantages over the recent control methods in microgrids. The proposed method applies the primary and secondary frequency control strategy, simultaneously. However the secondary voltage control scheme is obligatory. In present study, Artificial Neural Networks (ANN) has been implemented for training and validating the advanced droop control (ADC). After ADC unit training, the inverter based DG can be installed in complex microgrids consist of several DGs and loads. Simulation results indicate the effectiveness and applicability of proposed method in controlling the voltage and frequency in autonomous microgrids.

Traditionally, conventional generation units were used to provide ancillary services e. g. reactive power support, and spinning reserve. Nonetheless, with the emergence of highly penetrated Distributed Energy Resources (DERs) systems and considering how beneficial they can be; it appears reasonable to use them as reactive power providers. Therefore, this paper introduces a new procedure for DERs to participate in the reactive power market. To do that, an algorithm is proposed to calculate the deliverable reactive power capability of DERs from distribution networks to transmission systems. Furthermore, a reactive power procurement model is introduced to consider DERs participation in the reactive power market. Finally, to show the effectiveness, and validity of this method many case studies are carried out on 33-bus distribution and CIGRE 32-bus transmission test systems

The integration of Distributed Energy Resources (DER) in power systems can provide the opportunity to supply electricity to customers more efficiently and effectively. The DER includes Distributed Generation (DG) and Demand Side Management (DSM). In a smart grid incorporating automated control and Distributed Energy systems an effective DSM can alleviate the peak load and shift part of the demand to off-peak hours. The aim of this research is to assess the impact of the DG and the DSM on reliability of the smart distribution system by analyzing different case studies. Roy Billinton test system RBTS Bus2 is used to validate case studies which are performed as a part of this paper. For more practical considerations, a modification of the RBTS Bus2 model is developed to assess the system reliability

This paper presents a multi-stage planning framework for analysis of stochastic Distributed Energy Resources (DERs) comprising of solar, wind, and battery storage. The existing models do not consider penetration level analysis in conjunction with sizing, placement, and economic assessment. The main objective of this research is to embed all these dimensions of system planning in one structure. The first stage involves reliability constrained component sizing. The second stage pertains to placement of DERs based on loss minimization and voltage profile. The third stage is the main thrust of this work which provides exhaustive economic evaluation and cost-benefit analysis. The novelty of this work lies in the consideration of penetration level in backdrop of all three stages. The proposed formulation is implemented on a 33-Bus radial distribution feeder located in Jaisalmer, Rajasthan, India. Four penetration levels viz. 10, 20, 40, and 60 percent have been investigated and analyzed under different planning scenarios. The results facilitate the determination of optimum penetration level.

In addition to the great benefits of Distributed generation (DG) Resources to power systems, there are some disadvantages. In spite of some benefits like decrease of received power from transmission grid, increment of DGs penetration can lead to voltage increase in distribution networks during off peak. Hence the recent efforts have been made to handle problems to increase the penetration of these Resources. The use of Energy storage systems (ESSs) is one of the ways to prevent defects may arise from use of DGs in power systems and can help to increase penetration of DGs in power systems. ESSs can save Energy during off peak and deliver it to the network in peak hours; hence, these equipment can reduce power losses and prevent voltage deviation during off peak by increasing load due to ESS charging.In this paper, first DG allocation and ESS placement are introduced then simultaneous placement of DG and ESS to reduce power losses in the distribution network are described. The proposed models are solved using genetic algorithm as optimization tool. The obtained results show that simultaneous placement could increase DG penetration compared to the separate allocation of these devices and provide further reduce of the losses in the distribution network.