A scenario-based approach for optimal operation of energy hub under different schemes and structures

Because of economic and environmental issues' importance in the energy field, moving toward smart energy systems and energy hubs (EHs) has accelerated. The impacts of uncertainties, e.g., stochastic behaviors of renewable distributed generations (DGs), on EHs are fundamental challenges that should be considered carefully. Although several studies have been done in the area of EHs, a knowledge gap exists about developing an approach considering uncertainties under different EHs' structures and topologies. This research purposes of responding to such a research gap. In this research, a scenario-based approach for EHs' optimal operation considering wind turbine (WT) and photovoltaic (PV) uncertainties is proposed. The proposed approach is applied to EH under different schemes. Using the k-means clustering algorithm decreases the computational burden, while the appropriate accuracy is achievable. The proposed stochastic optimization problem is solved using the genetic algorithm (GA). The comparative view is considered to investigate the impacts of cooling and heating components like the heat pump (HP), absorption chiller (AC), and heat storage (HS) on EH's optimal operation and energy cost. According to this research findings, the EH's daily energy cost under a scheme using the AC, HP, and HS is approximately 6.5% less than a scheme only using HP. Also, using the HS and HP alongside the AC leads to 5.6% and 6.4% cost-saving, respectively. But for a better comparison, the investment and operation and maintenance (O&M) cost are considered, in which case Structure 3 (AC þ HP) is more efficient both in terms of energy consumption and investment costs

A novel solution for addressing the problem of soiling and improving performance of PV solar systems

The adoption of photovoltaic (PV) solar technology for power generation is one of the fastest growing sources of renewable energy. In recent years, significant cost reductions of PV modules and rising global demand for energy have been key drivers of the solar industry’s exponential growth. While the majority of research into improving efficiency of solar systems has focused on increasing power output, adverse effects of environmental factors on system performance have largely been neglected. The accumulation of dirt on panel surfaces, known as soiling, can significantly reduce the power generated by PV systems due to blocking the Sun’s irradiation from PV cells. Despite drawbacks associated with mitigating soiling, including wasteful water consumption and the risk of panel damage, such strategies must be implemented in order to maintain system performance and reduce financial losses. This report proposes a new innovation to address the problem of soiling, designed by Kirchner Solar Group, by inverting panels at night and using of natural condensation to remove dirt settlement without causing abrasion to panel surfaces. Known as ‘NightFlip’, this solution has been designed to be an intrinsic, self-cleaning feature that reduces the impact of soiling without using water. A prototype was built to evaluate the real-world performance of the system. Data was recorded for the prototype when operating alongside a standard system, and the extent to which system performance was improved is analyzed