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.

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.

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In the light of the alarming impending energy scene, energy efficiency and exergy efficiency are unmistakably gathering momentum. Among efficient process design methodologies, literature suggests pinch analysis and exergy analysis as two powerful thermodynamic methods, each showing certain drawbacks, however. In this perspective, this article puts forward a methodology that couples pinch and exergy analysis in a way to surpass their individual limitations in the aim of generating optimal operating conditions and topology for industrial processes. Using new optimizing exergy‐based criteria, exergy analysis is used not only to assess the exergy but also to guide the potential improvements in industrial processes structure and operating conditions. And while pinch analysis considers only heat integration to satisfy existent needs, the proposed methodology allows including other forms of recoverable exergy and explores new synergy pathways through conversion systems. A simple case study is proposed to demonstrate the applicability and efficiency of the proposed method.

While reliability stays the most critical requirement for power generation in the Oil & Gas sector, energy effectiveness is becoming an increasingly important topic. Gas turbines are efficient and flexible tools both in electrical and mechanical drive applications as they cope with the multiple energy demand profiles of this sector. Aeroderivative gas turbines boast fast response and fast O&M procedures which fits with the exploitation of hydrocarbon fields. Heavy duty machines are robust and very popular in refinery utilities or as large mechanical drivers. However, it is sometimes desirable to boost GT performances: operators of the Oil & Gas sector may need to increase power output or wish to improve efficiency, especially during part load operation where the latter is degraded. While a number of commercial hardware, service and software products cover these needs, the operators may want to keep their own control on their production tools. To that end resorting to simple thermodynamic considerations enables rule-of-thumb evaluations of the impact of cycle changes on GT performances but this possibility is often unexploited. This joint paper is intended to provide the energy community, especially the engineers of the Oil & Gas sector, with some basic methods for the estimation/anticipation of gas turbine performance changes.