Optimal Sizing Of Distributed Energy Resources Research Articles

Two-stage ANN based intelligent technique for optimal positioning and sizing of DERs in distribution system

Recent years have examined large transformational implications due to integration of highly intermittent renewable sources of energy. Therefore, initiatives must be explored to enhance the operational flexibility of distributed energy resources (DER) for bringing a new viewpoint to the planning and operation of future distribution system. Also, despite the economic, environmental and societal advantages of DERs, their adoption is limited and markets are yet to realize its full potential to address the operational challenges of the system. The solution to these is integration of multiple DERs in the system of appropriate size at optimal location in the system. Currently, numerous optimization methods based on rigorous mathematical calculation are used to solve this issue. This inevitably increases the complexity of such algorithms. Therefore, this paper proposes a two-stage artificial neural network (ANN) based intelligent technique to solve the problem. In this work, first stage ANN is used for screening the critical buses rank wise based on voltage, line and power stability index to find the optimal location. In second stage ANN, screening the size of DER based on the location of critical bus is accomplished to find the optimal sizing of DER. The intelligent technique proposed in this work is applicable to both radial as well as networked system. The solution is extensively evaluated through simulation in MATLab for a set of operational scenarios and performance verification of the proposed technique is provided for IEEE 33 and 69 bus systems for radial and IEEE 14 and 118 bus systems for mesh networked systems. The results validate the efficacy of the proposed technique.

Bi-objective economic feasibility of hybrid micro-grid systems with multiple fuel options for islanded areas in Egypt

The main target of this research is to allow modern distributed energy resources (DERs) to contribute effectively in the economic feasibility of hybrid renewable power generation system. There are several factors such as the net present cost (NPC), levelized cost of energy (COE), amount of greenhouse gases (GHG) emissions, and the ability of the hybrid system to meet the load at different meteorological conditions to consider when evaluating the effectiveness of hybrid generation system within microgrids. A multi-objective based optimization algorithm to reduce cost, emissions, and a combined solution between cost and emissions is investigated in this research. This research presents an approach to optimize a hybrid microgrid (HMG) system with different fuel options. The power management approach determines the optimal sizing of DERs based on ant colony optimization (ACO) algorithm. In order to find the best configuration, the obtained results are compared with genetic algorithm (GA), particle swarm optimization (PSO), and HOMER. Three isolated areas in Egypt with different metrological conditions are selected for optimization of HMG system, namely: Kharga, Saint Katherine, and Qussair. The results show that the combined optimal configuration of HMG system is better in satisfying load demands without violating any restraints.