Due to its high short-term variability, solar-photovoltaic power in isolated industrial grids faces a challenge of grid reliability. Storage systems can provide grid support but come at a high cost that requires carefully evaluating power capacity needs. Battery sizing methodologies are now the focus of many studies, with a global upward trend in detailed modelling and complex optimization. However, although solar variability can be the source of uncertainties and battery oversizing, it rarely features as an input in scenarios. This study proposes several solar variability scenarios thanks to the wavelet-variability model and two variability metrics. These scenarios are employed as inputs in two sizing methodologies to compare the resulting battery capacity and draw conclusions on the role of modelling complexity and scenario identification. Results show that neglecting the photovoltaic power plant smoothing effect leads to an overestimation of the battery power support of 51%. In the other hand, complex dynamic modelling may reduce the battery power capacity by 25%. The economic analysis shows that a proper combination of variability scenario and battery sizing methodology may reduce the levelized costs of electricity by 3%. Highlights• Modelling photovoltaic plant geographical smoothing avoids over-investments.• Identifying variability scenarios is crucial to ensure continuity of supply.• Combining ramp-detection and variability index spares the use of day-long timeseries.

Reducing carbon emissions and electricity costs in industry is a major challenge to ensure competitiveness and compliance with new climate policies. Photovoltaic power offers a promising solution but also brings considerable uncertainties and risks that may endanger the continuity and quality of supply. From an operational point of view, large-scale integration of solar power could result in unmet demand, electrical instabilities and equipment damage. The performance and lifetime of conventional fossil equipment are likely to be altered by repeated transient operations, making it necessary to adopt specific modeling tools. Control strategies and sizing methodologies must be adapted to account for the strong reliability constraint while dealing with significant production uncertainties. In addition, conventional mitigation technologies, such as storage and load flexibility, have limited potential in these applications and may result in high investments or penalties if they are not properly assessed. This study provides an overview of these challenges by providing a transversal analysis of the scientific literature from fossil engine thermodynamics to control system theory applied to industrial systems. The main characteristics of reliability-constrained microgrids are identified and a conceptual definition is proposed by analyzing state-of-the art studies of various industrial applications and taking oil-and gas microgrids as an enlightening example. Then follows a review of the challenges of accounting for dynamical behavior of fossil equipment, PV and storage systems, ending with the identification of several research gaps. Finally, applicable control strategies and sizing techniques are presented.

Low-inertia, isolated power systems face the problem of resiliency to active power variations. The integration of variable renewable energy sources, such as wind and solar photovoltaic, pushes the boundaries of this issue further. Higher shares of renewables requires better evaluations of electrical system stability, to avoid severe safety and economic consequences. Accounting for frequency stability requirements and allocating proper spinning reserves, therefore becomes a topic of pivotal importance in the planning and operational management of power systems. In this paper, dynamic frequency constraints are proposed to ensure resiliency during short-term power variations due to, for example, wind gusts or cloud passage. The use of the proposed constraints is exemplified in a case study, the constraints being integrated into a mixed-integer linear programming algorithm for sizing the optimal capacities of solar photovoltaic and battery energy storage resources in an isolated industrial plant. Outcomes of this case study show that reductions in the levelized cost of energy and carbon emissions can be overestimated by 8.0% and 10.8% respectively, where frequency constraints are neglected. The proposed optimal sizing is validated using time-domain simulations of the case study. The results indicate that this optimal system is frequency stable under the worst-case contingency.