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This article offers a cohesive design optimization and control framework of a large-scale grid-connected battery and battery-less hybrid solar/wind system. Primarily, a techno-enviro-socio-economic design optimization and feasibility analysis were performed for eight distinct energy alternatives. Secondly, a finite-set model predictive current control (FS-MPCC) was proposed to improve both stability and dynamic behavior of the optimal energy alternative under various climatic circumstances. Moreover, finite-set model predictive control is furtherly employed to exploit the maximum permissible power from both solar and wind energies. The proposed framework is verified over a new case-study in Sokhna Industrial Zone in Suez, Egypt. The studied load demand combines both industrial and residential load sectors with a daily consumption of 40,500 kWh and of 4-MW peak. The design optimization results revealed that the grid-connected photovoltaic/wind turbines/converter system has superior sustainable and economic performances with the least net present cost ($17,371,980) and energy cost (0.0782 $/kWh). These values were less than those of the base-case system (grid-only) by 17 % and 22 %, respectively. Moreover, the reliability analysis found a tiny and tolerable daily capacity shortage of 5835-kWh despite the five annual grid outages. Besides, around 52 % of the total energy production was acquired from both solar and wind generation systems, which interpreted in a maximum saving in carbon dioxide emissions by 45.8 % compared to the base-case system and offered substantial social benefits in terms of creating nearly 10 jobs. The FS-MPCC results proved a reinforced dynamic behavior for the maximum power tracking profiles of solar and wind systems as well as in the DC-bus voltage profile regarding the over- and undershoot and the steady-state response. Compared to PI controller, the proposed FS-MPCC creates dq-axis voltages and currents with less disturbances and ripples which inject current/voltage to the grid with fewer harmonics and thus increases the generator's lifetime. To sum up, the proposed method features an integrative and compelling case to be used as an efficient tool to implement large-scale renewable energy projects. It could act as a blueprint for energy developers, researchers, investors, and authorities to achieve affordable and sustainable energy access. • On-grid solar/wind energy system is optimized to meet industrial and residential loads. • PI. FS-MPCC technique is developed and compared with PI for optimal system control. • Optimal design has the least net present and energy costs and maximum carbon savings. • FS-MPCC prove a strengthened dynamic performance under different climatic conditions. [ABSTRACT FROM AUTHOR]

L 1 adaptive controller has been recognized for having a structure that allows decoupling between robustness and adaption owing to the introduction of a low pass filter with adjustable gain in the feedback loop. The trade-off between performance, fast adaptation and robustness, is the main criteria when selecting the structure or the coefficients of the filter. Several off-line methods with varying levels of complexity exist to help finding bounds or initial values for these coefficients. Such values may require further refinement using trial-and-error procedures upon implementation. Subsequently, these approaches suggest that once implemented these values are kept fixed leading to sub-optimal performance in both speed of adaptation and robustness. In this paper, a new practical approach based on fuzzy rules for online continuous tuning of these coefficients is proposed. The fuzzy controller is optimally tuned using Particle Swarm Optimization (PSO) taking into accounts both the tracking error and the controller output signal range. The simulation of several examples of systems with moderate to severe nonlinearities demonstrate that the proposed approach offers improved control performance when benchmarked to L 1 adaptive controller with fixed filter coefficients. [ABSTRACT FROM AUTHOR]

Agrivoltaic systems represent an intelligent solution combining electricity production from solar photovoltaic technology with agricultural production to avoid land use conflicts. Geographic Information System technologies can support the implementation and spread of agrivoltaic systems by identifying the most suitable areas using useful spatially explicit information concerning techno-agro-socio-economic criteria. In this study, we have developed a procedure to identify and classify suitable areas for agrivoltaic systems in Sweden. An Ordinal Priority Approach based multi-criteria decision-making algorithm is established to calculate the weights of the selected evaluation criteria through expert interviews. The land use data refers to the Corine Land Cover 2018 product. The results show that about 8.6% of the Swedish territory, approximately 38,485 km2, is suitable for installing agrivoltaic systems. Among this area, about 0.2% is classified as "excellent", about 15% as "very good", about 72% as "good", about 13% as "moderate", and about 0.1% as "poor". Most "excellent"-classified areas are in Kalmar, Skåne, and Gotland. In contrast, most "very good" sites are in Skåne, Kalmar, and Östergötland. By deploying vertically mounted agrivoltaic systems with bifacial photovoltaic modules, the total potential installed capacity for "excellent" areas is about 2.5 GW p , while for areas classified "excellent" and "very good" is about 221 GW p. The total "excellent" areas can potentially supply about 2.4 TWh of electricity against the electricity consumption in 2021 of about 143 TWh. On the other hand, the land classified as "excellent" and "very good" could potentially provide about 207 TWh. The County of Västra Götaland shows the greatest potentials in terms of total potential electricity supply from agrivoltaic systems with about 227 TWh, followed by Skåne with a total potential of 206 TWh. • A GIS-MCDM approach to identify and classify suitable areas for agrivoltaic systems in Sweden is developed. • About 8.6% of the Swedish territory is suitable for installing agrivoltaic systems. • Among the suitable areas, about 0.2% is classified as "excellent" while about 15% as "very good". • The total potential installed capacity for "excellent" areas is about 2.5 GW p. • The areas classified as "excellent" have the potential to supply about 2.4 TWh. [ABSTRACT FROM AUTHOR]