Abstract:The biological pigment melanin is present in most of the biological systems. It manifests a host of biological and pharmacological properties. Its role as a molecule with special properties and functions affecting general health, including photoprotective and immunological action, are well recognized. Its antioxidant, anti-inflammatory, immunomodulatory, radioprotective, hepatic, gastrointestinal and hypoglycaemic benefits have only recently been recognized and studied. It is also associated with certain disorders of the nervous system. In this MiniReview, we consider the steadily increasing literature on the bioavailability and functional activity of melanin. Published literature shows that melanin may play a number of possible pharmacological effects such as protective, stimulatory, diagnostic and curative roles in human health. In this MiniReview, possible health roles and pharmacological effects are considered.

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The feature of bidirectional communication in a smart grid involves the interaction between consumer and utility for optimizing the energy consumption of the users. For optimal management of the energy at the end user, several demand side management techniques are implemented. This work proposes a home energy management system, where consumption of household appliances is optimized using a hybrid technique. This technique is developed from cuckoo search algorithm and earthworm algorithm. However, there is a problem in such home energy management systems, that is, an uncertain behavior of the user that can lead to force start or stop of an appliance, deteriorating the purpose of scheduling of appliances. In order to solve this issue, coordination among appliances for rescheduling is incorporated in home energy management system using game theory. The appliances of the home are categorized in three different groups and their electricity cost is computed through the real-time pricing signals. Optimization schemes are implemented and their performance is scrutinized with and without coordination among the appliances. Simulation outcomes display that our proposed technique has minimized the total electricity cost by 50.6% as compared to unscheduled cost. Moreover, coordination among appliances has helped in increasing the user comfort by reducing the waiting time of appliances. The Shapley value has outperformed the Nash equilibrium and zero sum by achieving the maximum reduction in waiting time of appliances.

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In this work, a new orchestration of Consumer to Fog to Cloud (C2F2C) based framework is proposed for efficiently managing the resources in residential buildings. C2F2C is a three layered framework consisting of cloud layer, fog layer and consumer layer. Cloud layer deals with on-demand delivery of the consumer’s demands. Resource management is intelligently done through the fog layer because it reduces the latency and enhances the reliability of cloud. Consumer layer is based on the residential users and their electricity demands from the six regions of the world. These regions are categorized on the bases of the continents. Two control parameters are considered: clusters of buildings and load requests, whereas four performance parameters are considered: Request Per Hour (RPH), Response Time (RT), Processing Time (PT) and cost in terms of Virtual Machines (VMs), Microgrids (MGs) and data transfer. These parameters are analysed by the round robin algorithm, equally spread current execution algorithm and our proposed algorithm shortest job first. Two scenarios are used in the simulations: resource allocation using MGs and resource allocation using MGs and power storage devices for checking the effectiveness of the proposed work. The simulation results of the proposed technique show that it has outperformed the previous techniques in terms of the above-mentioned parameters. There exists a tradeoff in the PT and RT as compared to cost of VM, MG and data transfer.

In the last couple of decades, numerous energy management strategies have been devised to mitigate the effects of greenhouse gas emission, hence introducing the concept of microgrids. In a microgrid, distributed energy generators are used. Microgrid enables a point which ameliorates in exchanging power with the main grid during different times of day. Based on the system constraints, in this work, we aim to efficiently minimize the operating cost of the microgrid and shave the power consumption peaks. For this purpose, we introduce an improved binary bat (iBBat) algorithm which helps to schedule the load demand of smart homes and energy generation from distributed generator of microgrid to the load demand and supply. The proposed energy management algorithm is applied to both grid-connected and islanded modes of the microgrid. The constraints imposed on the algorithm ensure that the load of electricity consumer does not escalate during peak hours. The simulation results are compared with BBat and binary flower pollination algorithm, which validate that the iBBat reflects substantial reduction in operating cost of microgrid. Moreover, results also show a phenomenal reduction in the peak-to-average ratio of load demand from main the main grid. operate in two modes: grid-connected and island modes. In a grid-connected mode it sells the surplus energy to main grid and buys from it when its energy generation is less than the demand. In this mode microgrid is always connected to main grid. On the other hand, an island mode is useful mostly in cases when power supply is interrupted due to the detection of any fault in the grid or in regions such as Russia, where 60% of the territories are not connected to the utility due to their geographical positions [3]. In this case, the connection between microgrid and main grid is terminated.Optimally scheduling the microgrid resources has become a hot research topic from a couple of years. Devising a strategy for managing energy also puts a great impact to optimize the generation pattern of the microgrid in either of its modes. The authors in [4] noticed the large integration of RESs in the microgrid due to which the use of ESS dramatically increased. For this purpose, the authors introduced a bat algorithm to develop corrective strategies to perform least cost dispatches. The authors in [5] employed flower pollination algorithm (FPA) to schedule home appliances to balance the load demand of consumer for demand side management (DSM). Moreover, they put emphasis on the reduction of peak-to-average ratio (PAR) and electricity cost. Zhang et al. [6] introduced a method which helps to schedule the microgrid resources. For this purpose, they proposed a hybrid optimization algorithm. Muhammad et al. [7] introduced an architecture which integrates RESs. A mix-mode energy management strategy is introduced in [8]. It also presents a battery sizing method which helps to operate the microgrid with a minimum operating cost.The power mix generation has a great impact on the cross-country rel...

The increasing load demand in residential area and irregular electricity load profile encouraged us to propose an efficient Home Energy Management System (HEMS) for optimal scheduling of home appliances. We propose a multi-objective optimization based solution that shifts the electricity load from On-peak to Off-peak hours according to the defined objective load curve for electricity. It aims to manage the trade-off between conflicting objectives: electricity bill, waiting time of appliances and electricity load shifting according to the defined electricity load pattern. The defined electricity load pattern helps in balancing the load during On-peak and Off-peak hours. Moreover, for real-time rescheduling, concept of coordination among home appliances is presented. This helps the scheduler to optimally decide the ON/OFF status of appliances to reduce the waiting time of the appliance. Whereas, electricity consumers have stochastic nature, for which, nature-inspired optimization techniques provide optimal solution. For optimal scheduling, we proposed two optimization techniques: binary multi-objective bird swarm optimization and a hybrid of bird swarm and cuckoo search algorithms to obtain the Pareto front. Moreover, dynamic programming is used to enable coordination among the appliances so that real-time scheduling can be performed by the scheduler on user's demand. To validate the performance of the proposed nature-based optimization techniques, we compare the results of proposed schemes with existing techniques such as multiobjective binary particle swarm optimization and multi-objective cuckoo search algorithms. Simulation results validate the performance of proposed techniques in terms of electricity cost reduction, peak to average ratio and waiting time minimization. Also, test functions for convex, non-convex and discontinuous Pareto front are implemented to prove the efficacy of proposed techniques.INDEX TERMS Coordination, dynamic programming, knapsack, multi-objective optimization, Pareto front, meta-heuristic, nature-inspired, bird swarm and cuckoo search algorithm, multi-objective bird swarm optimization, hybrid technique, demand side management, demand response, smart grid. ABBREVATIONS AMI Advanced Metering Infrastructure BSO Bird Swarm OptimizationThe associate editor coordinating the review of this manuscript and approving it for publication was Salvatore Favuzza .

In order to ensure optimal and secure functionality of Micro Grid (MG), energy management system plays vital role in managing multiple electrical load and distributed energy technologies. With the evolution of Smart Grids (SG), energy generation system that includes renewable resources is introduced in MG. This work focuses on coordinated energy management of traditional and renewable resources. Users and MG with storage capacity is taken into account to perform energy management efficiently. First of all, two stage Stackelberg game is formulated. Every player in game theory tries to increase its payoff and also ensures user comfort and system reliability. In the next step, two forecasting techniques are proposed in order to forecast Photo Voltaic Cell (PVC) generation for announcing optimal prices. Furthermore, existence and uniqueness of Nash Equilibrium (NE) of energy management algorithm are also proved. In simulation, results clearly show that proposed game theoretic approach along with storage capacity optimization and forecasting techniques give benefit to both players, i.e., users and MG. The proposed technique Gray wolf optimized Auto Regressive Integrated Moving Average (GARIMA) gives 40% better result and Cuckoo Search Auto Regressive Integrated Moving Average (CARIMA) gives 30% better results as compared to existing techniques.