Grid reliability enhancement by peak load forecasting with a PSO hybridized ANN model | IEEE Conference Publication | IEEE Xplore

Grid reliability enhancement by peak load forecasting with a PSO hybridized ANN model


Abstract:

Raised complexity levels of conventional grids, ever increasing load demands, elevated reliability issues, limitations of conventional power generating units and quality ...Show More

Abstract:

Raised complexity levels of conventional grids, ever increasing load demands, elevated reliability issues, limitations of conventional power generating units and quality issues associated with sustainable energy resources all highlight the need to adapt specific demand management techniques to augment grid reliability. The stability of grids is hampered by varying loads, intermittent supply of the renewable resources and the inability of conventional plants to deal with them making the grids extremely vulnerable during peak load times. In this study we propose the utilization of a hybrid load forecasting tool based on Artificial Neural Network and Particle Swarm Optimization (PSO) as the solution. The tool would forecast futuristic load and would be specifically effective for the peak load times enabling utilities to optimize grid performance.
Date of Conference: 02-04 September 2015
Date Added to IEEE Xplore: 17 December 2015
ISBN Information:
Conference Location: Noida, India

I. Introduction

A power grid involves different power systems that together serve as the source to the grid which in turn supplies power to the customers. The performance and availability of the generated power varies from system to system. For example, steam power systems with fuel sources like nuclear or coal provides for the baseload, which requires continuous operation with approximately constant power output. Moreover, a steam powered system requires prolonged time for responding to the load change or varying its output. Whereas a natural gas power system responds faster to load changes.

References

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