Reliability study under the smart grid paradigm using computational intelligent techniques and renewable energy sources.
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Date
2022
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Abstract
The increase in the demand for a reliable electricity supply by the utilities and consumers has
necessitated the evaluation of the reliability of power systems. A reliable electricity supply is
characterized by no or minimal duration and frequency of supply outages. Current power systems
are changing due to increasing power demand and depletion of fossil fuel deposits. These
changes are related to smart grids which are intelligent electric networks that are capable of using
demand management methods, supporting communication devices and monitoring of consumer
energy consumption. They can also integrate renewable energy sources thereby reducing reliance
on fossils fuel sources. The main objective of this study is to optimize power systems operations
and improve reliability. Different optimization methods are proposed in this study to address the
issues of power systems operations. These optimization problems consider different constraints for
maximum operations of the power systems. Case studies are used to confirm the proposed methods
using the historical and climatic data for the City of Pietermaritzburg (29.37°S and 30.23°E), and
Newcastle (27.71°S, 29.99°E) South Africa. Firstly, the implementation of the back-propagation
algorithm method of the artificial neural networks (ANNs) for designing a predictive model for
power system outage is proposed. The results obtained are found to be satisfactory. In situations
where there is the problem of accessibility to large system data and presence of multiple
system constraints, another method is proposed. This second technique proposes the application
of a maximum entropy function-based multi-constrained event-driven outage prediction model,
using the collaborative neural network (CONN) algorithm. The outcome is better than the conventional
event-driven methods. Lastly, an adaptive model predictive control (AMPC) method with
the integration of renewable energy sources (RESs) and a battery energy storage system (BESS)
is proposed to further improve the reliability of the power system. The developed method uses
a modified Roy Billinton Test System (RBTS) to implement the reliability improvement of the
power system. The proposed computational intelligent techniques fulfil the necessities of operation
robustness, implementation simplicity and reliability improvement of the power systems.
Description
Doctoral Degree. University of KwaZulu-Natal, Durban.