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Optimal Active Energy Loss with Feeder Routing and Renewable Energy for Smart Grid Distribution

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  • Narongdech Keeratipranon

    (College of Innovative Technology and Engineering, Dhurakij Pundit University, Bangkok, Thailand)

  • Phatcharasak Phawanaphinyo

Abstract

Electric power is the main energy source for a modern society. Good management of electric power cycle is essential for a sustainable society. The electric power cycle is composed of Generation, Transmission, Distribution, and Consumption. Smart Grid (SG) is a system that integrated traditional grids with Information and Communication Technology (ICT). In addition, SG has the ability to integrate electrical power supply from both to main power substation and Distributed Generation (DG), which compensates for the power demand during peak times. However, SG still has a similar problem to the original grid in terms of active power loss, from electric current injecting through the transmission line. This paper solves the active power loss problem by feeder routing using the Adjusting Dijkstra’s Cost Method, follow by deciding the allocation position and sizing of DG by the use of Evolutionary Computing, namely Harmony Search (HS), Artificial Bee Colony (ABC), and Particle Swarm Optimization (PSO). The experiments evaluate the performance of the algorithm using power flow analysis, Backward / Forward Sweep Method, on the IEEE 33 bus system. From the experimental results, PSO provides the best performance. The overall active power loss in the cases of 3 DGs was reduced from 202.67 to 52.29 kW, representing a reduction of 74.20%..

Suggested Citation

  • Narongdech Keeratipranon & Phatcharasak Phawanaphinyo, 2017. "Optimal Active Energy Loss with Feeder Routing and Renewable Energy for Smart Grid Distribution," Journal of Reviews on Global Economics, Lifescience Global, vol. 6, pages 269-278.
  • Handle: RePEc:lif:jrgelg:v:6:y:2017:p:269-278
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    Keywords

    Minimal active power loss; Backward / Forward Sweep Method; Harmony Search; Artificial Bee Colony; Particle Swarm Optimization.;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • Q21 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation - - - Demand and Supply; Prices

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