The purpose of this paper is to analyse the concept of supply chain resilience (SCRES) using a concept mapping framework to seek conceptual clarity, with an emphasis on SCRES definitions, essential capabilities, elements and managerial practices. Design/methodology/approach: A systematic literature review was conducted of 103 peerreviewed journal articles covering the period from 2000 to 2015, with the aim to identify supply chain resilience concept. Findings: Through analysis and synthesis of the literature, the study revealed three major constructs used to define resilience in supply chain: SCRES phases, strategies, and capabilities. The study has addressed five core resilience capabilities: the ability to anticipate, to adapt, to respond, to recover, and to learn. The study has also identified 13 essential elements and several managerial practices that support firms to acquire the five capabilities. The studied capabilities are then linked with supply chain resilient phases and strategies in order to establish an integrated view of the concept. Research limitations/implications: The explorative nature of this study and the role of the concept mapping framework, which does not empirically test the relationships in the model, are considered as limitations, to be addressed by the authors in future research. Originality/value: The originality of this paper lies in the classification of different features of SCRES through a comprehensive concept mapping framework that establishes relationships and interactions between them. This study, therefore, lays a foundation for testing these connections in future empirical studies. The article brings together fragmented literature from multiple studies to create a solid body of knowledge that addresses the need for conceptual clarity in SCRES literature.

In terms of energy production, combining conventional and renewable energy sources prove to be more sustainable and cost-effective. Nevertheless, efficient planning and designing of such systems are extremely complex due to the intermittency of renewable sources. Many existing studies fail to capture the stochasticity and/or avoid detailed reliability analysis. This research proposes a practical stochastic multi-objective optimization tool for optimally laying out and sizing the components of a grid-linked system to optimize system power at a low cost. A comparative analysis of four state-of-the-art algorithms using the hypervolume measure, execution time, and nonparametric statistical analysis revealed that the nondominated sorting genetic algorithm III (NSGA-III) was more promising, despite its significantly longer execution time. According to the NSGA-III calculations, given solar irradiance and energy profiles, the household would need to install a 5.5 (kWh) solar panel tilted at 26.3° and orientated at 0.52° to produce 65.6 (kWh) of power. The best battery size needed to store enough excess power to improve reliability was 2.3 (kWh). The cost for the design was $73520. In comparison, the stochastic technique allows for the construction of a grid-linked system that is far more cost-effective and reliable.

Avoiding over-dependency on the oil-fired energy supply systems motivates many countries to integrate renewable energy into the existing energy supply systems. Solar Photovoltaic technology forms the most promising option for developing such a cost-effective and sustainable energy supply system. Generally, the current-voltage curve is used in the performance assessment and analysis of the Photovoltaic module. The accuracy of the equations for the curve depends on accurate cell parameters. However, the extraction of these parameters remains a complex stochastic nonlinear optimization problem. Many studies have been carried out to deal with such problem but still more researches need to be carried out to achieve a minimum error and a high accuracy. The existing researches ignored the variation in the meteorological data though it has a significant impact on the problem design. In this study, the Sample Average Approximation was employed to deal with the uncertainty and the hybrid optimization method was used to get the optimal parameters. The results showed that the Hybrid PSO-GWO produced the most optimal solution: Series resistance (1.4623), Shunt resistance(215.0000), Ideal diode factors (n1 = 0.9500, n2 = 1.6500) with a maximum PV power of 59.850W. The methodology produced realistic results since the variability is dealt with and the Hybrid PSO-GWO finds the optimal solution at a higher convergence rate.

Renewable Energy Resources have been identified among the most promising sources of harnessing power for industrial and household consumption but their power generations highly uctuate so building renewable power systems without critical reliability analysis might result in frequent blackouts in the power system. Therefore, in this paper, a robust, effective and ecient design approach is proposed to handle the reliability issues. The study involves a Mathematical modelling strategy of the PV system to estimate the total PV power produced and the Bottom-Up approach for predicting the household load demand. The reliability is defined in terms of Loss of Load Probability. The design methodology was validated with a University Household. The data used for the analysis consists of daily average global solar irradiance and load profiles. The results revealed that throughout the year, November-February is where the system seems to be more reliable. Also, the results indicated that without buck-up systems, the system would experience an average annual power loss of 17.8753% and thus, it is recommended that either solar batteries or the grid are used as backup system to achieve a complete level of reliability.

Generally, the main focus of the grid-linked photovoltaic systems is to scale up the photovoltaic penetration level to ensure full electricity consumption coverage. However, due to the stochasticity and nondispatchable nature of its generation, significant adverse impacts such as power overloading, voltage, harmonics, current, and frequency instabilities on the utility grid arise. These impacts vary in severity as a function of the degree of penetration level of the photovoltaic system. Thus, the design problem involves optimizing the two conflicting objectives in the presence of uncertainty without violating the grid’s operational limitations. Nevertheless, existing studies avoid the technical impact and scalarize the conflicting stochastic objectives into a single stochastic objective to lessen the degree of complexity of the problem. This study proposes a stochastic multiobjective methodology to decide on the optimum allowable photovoltaic penetration level for an electricity grid system at an optimum cost without violating the system’s operational constraints. Five cutting-edge multiobjective optimization algorithms were implemented and compared using hypervolume metric, execution time, and nonparametric statistical analysis to obtain a quality solution. The results indicated that a Hybrid NSGAII-MOPSO had better convergence, diversity, and execution time capacity to handle the complex problem. The analysis of the obtained optimal solution shows that a practical design methodology could accurately decide the maximum allowable photovoltaic penetration level to match up the energy demand of any grid-linked system at a minimum cost without collapsing the grid’s operational limitations even under fluctuating weather conditions. Comparatively, the stochastic approach enables the development of a more sustainable and affordable grid-connected system.