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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. This has triggered the necessity of using renewable energy sources (RESs) with optimization methods for reliability improvement of electricity systems and reduction of greenhouse gas (GHG) emissions. The main objective of this study is to optimize micro-grid systems operations, improve reliability, reduce emissions and balance the demand and supply of energy through RESs and battery energy storage system (BESS). The adaptive model predictive control (AMPC) method is used to address the issues of micro-grid operation. The AMPC algorithm solves the optimization problem of disturbance prediction in a micro-grid with different types of RESs and BESS integration. This optimization problem considers different constraints for minimum operating costs in different case scenarios. The financial viability of the proposed method is investigated. Solar photovoltaic (Solar PV), wind plant (WP) and BESSs are used with the AMPC method to investigate the impacts of annual real interest rates on cost and emission parameters, quantification of the emission oxides from different case scenarios, reduction of the cost of electricity (Ccoe), and power system reliability improvement. A modified Roy Billinton Test System (RBTS) is used to confirm the reliability enhancement and financial feasibility of the system. Case studies are used to confirm the proposed methods using climatic data for the city of Pietermaritzburg (29.37° S and 30.23° E), South Africa. The results obtained establish that the incorporation of RESs and BESSs using the AMPC method gives satisfactory outcomes.

The majority of faults on electric power systems are traceable to distribution networks due to the exposure of the networks to different adverse climatic conditions, among other factors. Power outages due to climatic factors are inevitable since a major part of a distribution network is made up of overhead lines and exposed to different unfavorable weather conditions. Climatic factors need to be fully considered when designing a power system model in order to achieve a meaningful system reliability improvement. The projection of power system outage requires a model with high accuracy, taking climatic conditions into consideration. Conventional models rely only on fault data and do not consider climatic factors, and so they have very low accuracy. This research proposes a computational intelligence model using historical fault and climatic data sets of Newcastle, South Africa. Three models are developed in this paper: the trend projection (TP) model, and two artificial neural network (ANN) models (Models 1 and 2). The performance of the models are examined using statistical parameters. The results of the ANN models were satisfactory unlike the trend projection model; thereby illustrating the efficacy of the computational methods.

Reliability of the electric grid structure for the transmission and distribution of power from the generating plants to the consumers, is an essential requirement for the reliability of electric supply. The components of the grid is exposed to weather events which cause faults to the grid’s structures. There is an expectation that as climate change alters the severity and number of weather events, electricity supplies and electricity grid reliability are expected to be affected. Computational intelligence models have been proven to be excellent predictive models for electricity reliability problems in previous studies. But there has not been sufficient literature reporting their applications in weather related electricity outage forecasting problems. In this study, weather related electricity outage was forecasted using artificial neural networks (ANNs) with back-propagation algorithm. Real-life data sets of the city of Pietermaritzburg, South Africa, was used to investigate the performance of the ANN model and was compared with a conventional model – exponential smoothing (ES). The ANN model gave satisfactory results as compared to the ES model. The result is a demonstration of the robustness of computation techniques.