The fundamental objective of economic load dispatch is to operate the available generating units such that the needed load demand satisfies the lowest generation cost and also complies with the various constraints. With proper power system operation planning using optimized generation limits, it is possible to reduce the cost of power generation. To fulfill the needs of such objectives, proper planning and economic load dispatch can help to plan the operation of the electrical power system. To optimize the economic load dispatch problems, various classical and new evolutionary optimization approaches have been used in research articles. Classical optimization techniques are outdated due to many limitations and are also unable to provide a global solution to the ELD problem. This work uses a new variant of particle swarm optimization techniques called modified particle swarm optimization, which is effective and efficient at finding optimum solutions for single as well as multi-objective economic load dispatch problems. The proposed MPSO is used to solve single and multi-objective problems. This work considers constraints like power balance and power generation limits. The proposed techniques are tested for three different case studies of ELD and EELD problems. (1) The first case is tested using the data of 13 generating unit systems along with the valve point loading effect; (2) the second case is tested using 15 generating unit systems along with the ramp rate limits; and (3) the third case is tested using the economic emission dispatch (EELD) as a multi-objective problem for 6 generating unit systems. The outcomes of the suggested procedures are contrasted with those of alternative optimization methods. The results show that the suggested strategy is efficient and produces superior optimization outcomes than existing optimization techniques.

Proteins are complex molecules, which play a vital role in our body’s function, the building of tissues, and the regulation of metabolic activity. They are crucial to children’s growth and serve as a key component in the body’s process of distributing oxygen. Proteins fuel the body by supplying the required nutrition and energy. Currently, there is an increasing demand for proteins on large scales with no detrimental effects. The adverse health effects of animal proteins have resulted in a growing preference for plant-based proteins, which offer a healthier daily dosage. Valuable proteins can be extracted from various parts of the plant, including stems, leaves, seeds, fruits, vegetables, and roots. Notably, protein extraction from waste plant and mushroom parts minimizes the product wastage and improves the overall production to support economic sustainability. There are several protein extraction techniques available, where the replacement of non-thermal methods with thermal ones is promising nowadays due to the appreciable retainment of protein quality. Pulsed Electric Field (PEF) technology is one of the most efficient non-thermal tools used to assist with extracting these proteins at the minimum processing time and energy consumption when compared with thermal techniques. It relies on the application of a high-voltage pulse between two electrodes to treat samples inside the treatment chamber. While electrode shapes and treatment chamber designs primarily govern the electric field’s application, optimizing process parameters such as electric field strength, pulse width, number of pulses, and pulse waveshape assists in obtaining a desirable enhancement in the protein yield. The primary objective of this review is to explain the PEF-assisted protein extraction process applicable to waste plant parts and deformed mushrooms. While PEF is not a novel concept, utilizing it as a pre-extraction treatment to the aforementioned waste resources would aid in improving the production of value-added protein products economically. So far, PEF has shown immense promise in assisting with protein extraction studies, but requires further research in order to establish this area for large-scale industrial applications.

The increased focus on sustainability in response to climate change has given rise to many new initiatives to meet the rise in building load demand. The concept of distributed energy resources (DER) and optimal control of supply to meet power demands in buildings have resulted in growing interest to adopt microgrids for a precinct or a university campus. In this paper, a model for an actual physical microgrid has been constructed in OPAL-RT for real-time simulation studies. The load demands for SIT@NYP campus and its weather data are collected to serve as input to run on the digital twin model of DERs of the microgrid. The dynamic response of the microgrid model in response to fluctuations in power generation due to intermittent solar PV generation and load demands are examined via real-time simulation studies and compared with the response of the physical assets. It is observed that the simulation results match closely to the performance of the actual physical asset. As such, the developed microgrid model offers plug-and-play capability, which will allow power providers to better plan for on-site deployment of renewable energy sources and energy storage to match the expected building energy demand.

A healthy diet rich in plant proteins can help in preventing chronic degenerative diseases. Plant-based protein consists of derivatives from algae, fungi (like mushrooms) and other plant products including stems, leaves, fruits, vegetables, grains, seeds, legumes and nuts. These sources are not only rich in protein, but also contain a high percentage of iron, calcium, folates, fiber, carbohydrates, fats etc. Hence, it is essential to explore plant-based protein sources and their other nutritional components to address existing food insecurity issues. Nowadays, the impact of food processing has produced promising results in extracting valuable bio-compounds including proteins from the plant matrix. In this view, PEF technology has secured an exceptional place in solving food quality issues through minimized thermal effects in the samples, improved extraction capabilities at a shorter time, higher extraction levels, high nutritional content of extracted samples, greater shelf-life extension and increased microbial killing efficiency. It is an energy efficient process which is used as a pre-treatment to increase selective extraction of intracellular compounds through electroporation technique. Here, the processing parameters play a significant role in obtaining enhanced extraction levels. These parameters have also considerably influenced the protein digestibility and amino acid modification. So far, PEF has been producing remarkable results in plant protein extraction research. Among various plant sources mentioned above, there is a limited literature available on the use of PEF-assisted protein extraction from legumes. In this review, the authors have discussed essential legumes and their nutritional components and have highlighted how PEF can be beneficial in extracting the protein levels from these sources. Further research should focus on PEF-assisted protein extraction from legumes, specifically analyzing the properties of protein quality and quantity.

Effective scheduling algorithms are needed in the cloud paradigm to leverage services to customers seamlessly while minimizing the makespan, energy consumption and SLA violations. The ineffective scheduling of resources while not considering the suitability of tasks will affect the quality of service of the cloud provider, and much more energy will be consumed in the running of tasks by the inefficient provisioning of resources, thereby taking an enormous amount of time to process tasks, which affects the makespan. Minimizing SLA violations is an important aspect that needs to be addressed as it impacts the makespans, energy consumption, and also the quality of service in a cloud environment. Many existing studies have solved task-scheduling problems, and those algorithms gave near-optimal solutions from their perspective. In this manuscript, we developed a novel task-scheduling algorithm that considers the task priorities coming onto the cloud platform, calculates their task VM priorities, and feeds them to the scheduler. Then, the scheduler will choose appropriate tasks for the VMs based on the calculated priorities. To model this scheduling algorithm, we used the cat swarm optimization algorithm, which was inspired by the behavior of cats. It was implemented on the Cloudsim tool and OpenStack cloud platform. Extensive experimentation was carried out using real-time workloads. When compared to the baseline PSO, ACO and RATS-HM approaches and from the results, it is evident that our proposed approach outperforms all of the baseline algorithms in view of the above-mentioned parameters.

Digital twin technology is a promising solution for achieving optimized microgrid control with enhanced efficiency, reliability, and sustainability. In this paper, we focus on a real-world microgrid in Singapore and develop a cognitive digital twin. Our digital twin consists of a client, located near the physical microgrid for real-time control, and a cloud-based server for running computationally intensive algorithms for energy management and optimization. We design and implement communication architectures to ensure seamless and real-time communication. The functionality and performance of our digital twin are validated through different microgrid operational scenarios. The results show that our digital twin outperforms comparison algorithms significantly and approximates the theoretical optimal with merely a 0.24% difference in operation cost. Overall, we demonstrate the effectiveness of our digital twin in enabling real-time optimization and management of microgrid operations, paving the way for technology adoption in smart grids to achieve improved grid resilience and efficiency.