Open AccessPower System Market Planning
Author(s) -
Isa S. Qamber,
Mohamed Y. Al-Hamad
Publication year - 2019
Publication title -
european journal of engineering research and science
Language(s) - English
Resource type - Journals
ISSN - 2506-8016
DOI - 10.24018/ejers.2019.4.11.1602
Subject(s) - margin (machine learning) , electricity market , electricity , control (management) , capacity planning , electric power system , operations research , nameplate capacity , environmental economics , reliability engineering , resource (disambiguation) , electricity generation , engineering , computer science , power (physics) , operations management , economics , electrical engineering , physics , quantum mechanics , computer network , machine learning , artificial intelligence
The loss of load expectation (LOLE) considered an acceptable security standard across most of the national electricity systems. In the present study, the LOLE is calculated. This means that the LOLE needs developing a model. The developed model serving to assesses the security of supply risks associated with the different electricity capacity margin levels, where the capacity margin is the level by which available electricity generation capacity exceeds the maximum expected level of demand. Then, the developed model updated on an annual basis. The annual update is helping to fulfill the Electricity Authority for planning, operation and control. The developed model will assess the system by calculating the capacity margin probability by combining both the generation and load models. The calculation of LOLE is an internationally accepted criterion in capacity adequacy. The results for the model of each region vary due to several factors, such as generation resource, load forecast, and forced outage rates (FOR). The estimation of the risk is economically optimal reserve margin to a number of case studies assumptions considered in the present study. The results help in future planning for the electric system operation and control. Furthermore, the study is helping to evaluate the implications of the obtained results for the electricity policy market to determine the best model for market design in the future.