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In recent years, there has been an increasing interest in renewable energies in view of the fact that fossil fuels are the leading cause of catastrophic environmental consequences. Ocean wave energy is a renewable energy source that is particularly prevalent in coastal areas. Since many countries have tremendous potential to extract this type of energy, a number of researchers have sought to determine certain effective factors on wave converters’ performance, with a primary emphasis on ambient factors. In this study, we used metaheuristic optimization methods to investigate the effects of geometric factors on the performance of an Oscillating Surge Wave Energy Converter (OSWEC), in addition to the effects of hydrodynamic parameters. To do so, we used CATIA software to model different geometries which were then inserted into a numerical model developed in Flow3D software. A Ribed-surface design of the converter's flap is also introduced in this study to maximize wave-converter interaction. Besides, a Bi-level Hill Climbing Multi-Verse Optimization (HCMVO) method was also developed for this application. The results showed that the converter performs better with greater wave heights, flap freeboard heights, and shorter wave periods. Additionally, the added ribs led to more wave-converter interaction and better performance, while the distance between the flap and flume bed negatively impacted the performance. Finally, tracking the changes in the five-dimensional objective function revealed the optimum value for each parameter in all scenarios. This is achieved by the newly developed optimization algorithm, which is much faster than other existing cutting-edge metaheuristic approaches. 

Kalina cycle system (KCS) offers an attractive prospect to produce power by utilizing low-grade heat sources where traditional power cycles cannot be implemented. Intending to explore the potential of exploiting low-grade heat sources for conversion to electrical energy, this study proposes two modified power generation cycles based on KCS-34. A multi-phase expander is positioned between the Kalina separator and the second heat regenerator in the proposed X-modification. In contrast, it is located between the mixer and second regenerator for Y-modification. To explore the potential benefits and limitations of the proposed modifications contrasted with the KCS-34, thermodynamic modeling and optimization have been conducted. The influence of critical decision parameters on overall cycle performance is analyzed. The result elucidates that by implementing an additional multi-phase expander, a significant amount of energy can be extracted from a lean ammonia water loop and X-modification can deliver superior thermodynamic performance compared with the Y-modification and the original KCS-34. With a reduced turbine inlet pressure of 58 bar and an ammonia concentration of 80%, the X-modified cycle's efficiency reaches a peak value of 17% and a net power yield of 1015 kW. An increase of 6.35% can be achieved compared with the conventional KCS-34 operating at the same conditions. Maximum exergy destruction of the working substance was observed in the condenser. 

In many remote localities, one of the underlying reasons for not receiving life-saving vaccines is the lack of electricity to store the vaccines in the required refrigerated conditions. Solar Photovoltaic (PV) refrigerators have been considered as a viable and green solution to store the vaccines in remote localities having no access to electricity. In this paper, a detailed methodology has been presented for the performance evaluation of a solar PV powered vaccine refrigerator for remote locations. Thermal modelling with hourly cooling load calculations and refrigeration cycle simulations were carried out. The performance parameters for three environment-friendly refrigerants: R152a, R1234yf, and R1234ze(E) has been compared against the commonly used R134a for two remote, off-grid locations in Bangladesh and South Sudan. The energy systems comprising of solar PV panels and batteries to run the refrigerator were modelled in HOMER software for techno-economic optimizations. For both the locations, R152a was found to be the best performing refrigerant exhibiting higher COP (2%−5.29%) as compared to the other refrigerants throughout the year, while R1234ze(E) exhibited COPs on par with R134a, and R1234yf had the least performance. Techno-economic analysis showed an energy system providing electricity to the refrigerator with R152a also had lower levelized cost of electricity (0.48%−2.54%) than the systems having other refrigerants in these locations.

Lithium-ion battery storage has emerged as a promising solution for various energy systems. However, complex degradation behavior, relatively short lifetime, high capital, and operational costs, and electricity market volatility are critical factors that challenge its practical viability. Thus, to ensure sustained profitability of Lithium-ion batteries in real-life applications, a smart and optimal management strategy considering key influencing factors is imperative for achieving efficient battery utilization. This study proposes two day-ahead battery-behavior-aware operation scheduling strategies to maximize profitability and longevity in residential grid-connected applications with dynamic electricity pricing. Each scenario employs unique approaches to make optimal decisions for optimal battery utilization. The first scenario optimizes short-term profitability by prioritizing revenue gains under three charge/discharge rates (high, moderate, low), considering daily charge and discharge timings as decision variables. Conversely, the second scenario proposes a smart strategy capable of making intelligent decisions on a wide range of variables to simultaneously maximize revenue and minimize degradation costs, ensuring short-term and long-term profitability. Decision variables include the cycle frequency for each specific day, timings as well as durations for charging and discharging per cycle. To ensure effective long-term assessment, both scenarios accurately estimate battery performance, calendric and cyclic capacity degradations, remaining-useful-lifetime, and internal states under real operational conditions until battery reaches its end-of-life criteria. The scenarios are assessed economically using various indicators. Furthermore, the impact of battery price and size on optimization outcomes are examined. The key findings indicate that, among the first set of scenarios, the strategy with low charge/discharge rate extends the battery lifetime most efficiently, estimated at 14.8 years. However, it proved to be the least profitable, resulting in negative profit of −3€/kWh/yr. On the other hand, strategies with high and moderate charge/discharge rates resulted in positive profit of 8.3 €/kWh/year and 9.2 €/kWh/year, despite having shorter battery lifetimes, estimated at 10.1 years and 13.6 years, respectively. Furthermore, from a payback perspective, the strategy with fast charge/discharge capability led to 1.5 years shorter payback period than that of the moderate rate strategy. The findings highlight that the first set of scenarios limits the strategy's flexibility in achieving both sustainability and profitability. In contrast, the second scenario achieves impressive profit (18 €/kWh/yr), shortest payback period (7.5 year), a commendable lifespan (12.5 years), contrasting revenue-focused scenarios, emphasizing the importance of striking optimal balance between revenue gain and degradation costs for charging/discharging actions, ensuring sustained profitability. The findings offer valuable insights for decision-makers, enabling informed strategic choices and effective solutions.