A low latency and energy efficient handover scheme for LTE femtocell networks

Femtocells are small, inexpensive, low power cell sites, which are used to improve indoor capaity and coverage. They are generally consumer deployed and are connected to their own wired broadband backhaul connections. A large number of femtocell deployments in long term evolution (LTE) networks are highly anticipated. One of the main technical challenges of LTE-femtocell networks is the handover between a femtocell and the macrocell due to non availability of direct communication links between femto eNBs and macro eNBs. Due to small femtocell coverage areas, frequent handovers between femtocells and macrocells take place. Therefore, a fast handover scheme using small signaling overhead is required for LTE-femtocell networks to support fast and seamless handovers. The S1-based handover scheme specified in the LTE standards for handover between eNBs, which are not directly connected through X2 interface, can also be used for handover between a femtocell and a macrocell. However, the latency of a S1-based handover increases significantly with the Internet delay as HeNBs are connected to eNBs through public Internet. In this paper, we propose a fast and efficient handover scheme to facilitate handovers between femtocells and macrocells. We also introduce a novel concept of the Temporary Area Partners (TAP) network, which consists of the mobile user equipment connected through device-to-device communication. Due to aforesaid TAP network, the proposed handover scheme will be more robust than the forward and S1-based handover schemes. Our delay performance analysis shows that the proposed scheme has much lower latency than the existing S1-based and forward handover schemes

Investigating the Impact of Economic Uncertainty on Optimal Sizing of Grid-Independent Hybrid Renewable Energy Systems

One of the many barriers to decarbonization and decentralization of the energy sector in developing countries is the economic uncertainty. As such, this study scrutinizes economics of three grid-independent hybrid renewable-based systems proposed to co-generate electricity and heat for a small-scale load. Accordingly, the under-study systems are simulated and optimized with the aid of HOMER Pro software. Here, a 20-year average value of discount and inflation rates is deemed a benchmark case. The techno-economic-environmental and reliability results suggest a standalone solar/wind/electrolyzer/hydrogen-based fuel cell integrated with a hydrogen-based boiler system is the best alternative. Moreover, to ascertain the impact of economic uncertainty on optimal unit sizing of the nominated model, the fluctuations of the nominal discount rate and inflation, respectively, constitute within the range of 15–20% and 10–26%. The findings of economic uncertainty analysis imply that total net present cost (TNPC) fluctuates around the benchmark value symmetrically between $478,704 and$814,905. Levelized energy cost varies from an amount 69% less than the benchmark value up to two-fold of that. Furthermore, photovoltaic (PV) optimal size starts from a value 23% less than the benchmark case and rises up to 55% more. The corresponding figures for wind turbine (WT) are, respectively, 21% and 29%. Eventually, several practical policies are introduced to cope with economic uncertainty

A Cost-Efficient-Based Cooperative Allocation of Mining Devices and Renewable Resources Enhancing Blockchain Architecture

The impressive furtherance of communication technologies has exhorted industrial companies to link-up these developments with their own abilities with the target of efficiency enhancement through smart supervision and control. With this in mind, the blockchain platform is a prospective solution for merging communication technologies and industrial infrastructures, but there are several challenges. Such obstacles should be addressed to effectively adopt this technology. One of the most recent challenges relative to adopting blockchain technology is the energy consumption of miners. Thus, providing an accurate approach that addresses the underlying cause of the problem will carry weight in the future. This work addresses managing the energy consumption of miners by using the advantage of distributed generation resources (DGRs). Along the same vein, it appears that achieving the optimal solution requires executing the modified reconfirmation of DGRs and miners (indeed, mining pool systems) in the smart grid. In order to perform this task, this article utilizes the Intelligent Priority Selection (IPS) method since this method is up to snuff for corporative allocation. In order to find practical solutions for this problem, the uncertainty is also modeled as a credible index highly correlated with the load and generation. All in all, it can be said that the outcome of this research study can help researchers in the field of enhancement of social welfare by using the proposed technology

A Multi-Layer Security Scheme for Mitigating Smart Grid Vulnerability against Faults and Cyber-Attacks

To comply with electric power grid automation strategies, new cyber-security protocols and protection are required. What we now experience is a new type of protection against new disturbances namely cyber-attacks. In the same vein, the impact of disturbances arising from faults or cyber-attacks should be surveyed by network vulnerability criteria alone. It is clear that the diagnosis of vulnerable points protects the power grid against disturbances that would inhibit outages such as blackouts. So, the first step is determining the network vulnerable points, and then proposing a support method to deal with these outages. This research proposes a comprehensive approach to deal with outages by determining network vulnerable points due to physical faults and cyber-attacks. The first point, the network vulnerable points against network faults are covered by microgrids. As the second one, a new cyber-security protocol named multi-layer security is proposed in order to prevent targeted cyber-attacks. The first layer is a cyber-security-based blockchain method that plays a general role. The second layer is a cyber-security-based reinforcement-learning method, which supports the vulnerable points by monitoring data. On the other hand, the trend of solving problems becomes routine when no ambiguity arises in different sections of the smart grid, while it is far from a big network’s realities. Hence, the impact of uncertainty parameters on the proposed framework needs to be considered. Accordingly, the unscented transform method is modeled in this research. The simulation results illustrate that applying such a comprehensive approach can greatly pull down the probability of blackouts

Techno-Economic Assessment of a Grid-Independent Hybrid Power Plant for Co-Supplying a Remote Micro-Community with Electricity and Hydrogen

This study investigates the techno-economic feasibility of an off-grid integrated solar/wind/hydrokinetic plant to co-generate electricity and hydrogen for a remote micro-community. In addition to the techno-economic viability assessment of the proposed system via HOMER (hybrid optimization of multiple energy resources), a sensitivity analysis is conducted to ascertain the impact of ±10% fluctuations in wind speed, solar radiation, temperature, and water velocity on annual electric production, unmet electricity load, LCOE (levelized cost of electricity), and NPC (net present cost). For this, a far-off village with 15 households is selected as the case study. The results reveal that the NPC, LCOE, and LCOH (levelized cost of hydrogen) of the system are equal to $333,074, 0.1155$/kWh, and 4.59 $/kg, respectively. Technical analysis indicates that the PV system with the rated capacity of 40 kW accounts for 43.7% of total electricity generation. This portion for the wind turbine and the hydrokinetic turbine with nominal capacities of 10 kW and 20 kW equates to 23.6% and 32.6%, respectively. Finally, the results of sensitivity assessment show that among the four variables only a +10% fluctuation in water velocity causes a 20% decline in NPC and LCOE

SPSO Based Optimal Integration of DGs in Local Distribution Systems under Extreme Load Growth for Smart Cities

Renewable energy-based distributed generators (DGs) are gaining more penetration in modern grids to meet the growing demand for electrical energy. The anticipated techno-economic benefits of these eco-friendly resources require their judicious and properly sized allocation in distribution networks (DNs). The preeminent objective of this research is to determine the sizing and optimal placing of DGs in the condensed DN of a smart city. The placing and sizing problem is modeled as an optimization problem to reduce the distribution loss without violating the technical constraints. The formulated model is solved for a radial distribution system with a non-uniformly distributed load utilizing the selective particle swarm optimization (SPSO) algorithm. The intended technique decreases the power loss and perfects the voltage profile at the system’s nodes. MATLAB is used for the simulation, and the obtained results are also validated by the Electrical Transient Analysis Program (ETAP). Results show that placing optimally sized DGs at optimal system nodes offers a considerable decline in power loss with an improved voltage profile at the network’s nodes. Distribution system operators can utilize the proposed technique to realize the reliable operation of overloaded urban networks