Relay Selection for Heterogeneous Cellular Networks with Renewable Green Energy Sources

Minimizing electromagnetic pollution and power consumption in green heterogeneous small cell network deployment

A deep learning-based forecasting model for renewable energy scenarios to guide sustainable energy policy: A case study of Korea

The solar and wind renewable energy sources are gaining popularity to encourage green energy into the power system. The cost of generation of solar and wind energy sources are decreasing and competing with conventional coal-based generation. Therefore, it is very important to integrate these renewable sources into the power system. Integrating Solar and wind energy sources require to solve the uncertainty problem. Both the solar and wind energy generation is uncertain and not controllable. In this paper, sliding window optimal ARIMA forecasting algorithm is proposed to solve the uncertainty associated with solar and wind sources. The proposed forecasting method is used on the data collected from National Renewable Energy Laboratory website.

Power system flexibility analysis using net-load forecasting based on deep learning considering distributed energy sources and electric vehicles

Attention paid to energy efficiency in datacenters has been increasing significantly in the last decade. One of the latest trends on this issue is supplying datacenters with renewable energy, and reducing the carbon footprint of datacenters is a major step towards green computing. Following this trend, more and more datacenters are built in cold climate areas for cheaper cooling and increased energy efficiency of the facilities. However, such geographical locations have a very varying availability of renewable energy (especially solar energy), depending on the annual month and fitting the datacenters completely with renewable energy is hence more difficult. This paper analyzes the feasibility of using renewable energies for a datacenter located on the 60° latitude north. We analyze the energy consumption caused by the server workload and the cost-to-energy trade-off related to available wind and solar energy sources. A wind and solar power model is built based on real weather data for the city of Turku, Finland, and a feasibility study is conducted for fitting a datacenter with renewable energy sources on such a location. The available monthly and annual renewable energy is analyzed for different scenarios and compared with the energy consumption of a simulated datacenter. We also analyze the ratio between number of wind turbines and m2 solar panel to achieve a desired fraction of renewable energy sources for a given datacenter.

With the advances of green energy technologies, clean and sustainable energy sources have been considered as alternative energy sources for powering cellular networks. However, it is very challenging to exploit the charged energy to satisfy the QoS demands of wireless multimedia services due to the dynamics in the energy charging and discharging processes. In this article, we study how to efficiently utilize the green energy supply to fulfill the QoS requirements of multimedia services in a cellular network. Specifically, we study network deployment and power allocation of small cell BSs in a two-tier cellular network in support of high-rate multimedia services. Our objective is to maximize the transmission efficiency while ensuring energy sustainability in green cellular networks powered by green energy sources. For network deployment, we aim to deploy the minimum number of small cell BSs to meet the high throughput demands of multimedia services and statistically guarantee energy sustainability of wireless networks. In addition, a power allocation scheme with cooperative communication is presented to maximize throughput. It is shown that the network sustainability and throughput of green cellular networks can be significantly improved by using the proposed BS placement and power allocation schemes.

Multi-hop relaying has been considered as a key technology for future wireless communication networks to overcome coverage and capacity problems in 5th generation mobile networks (5G). Recently, there has been increasing interest from 3GPP operators to integrate multi-hop relaying functionalities into LTE-A standards. However, many issues remain to be resolved due to the complexity of the multi-hop relaying paradigm, specifically resource allocation and mobility management. In this work, we focus on the relay-selection problem, in which a user may have in his range several relay stations from which to choose. The conventional selection scheme is based on the channel condition between the relay and the user, such that the user will achieve a high level of throughput. Instead, we propose a novel relay selection strategy based on Markov Decision Process (MDP) for multi-hop relay networks. Our goal is to achieve significant performance improvement against legacy, centralized relay selection strategies such as the disjoint-path selection scheme. Simulation results show that our design has the advantage of reducing inter-cell handoff frequency and lowering handoff delay.

To enhance the utilization of base stations (BSs) and face the challenge of the upcoming mobile data tsunami, energy saving and traffic offloading are two important issues to address in green cellular networks. In this paper, we design an optimal green policy, called Genie, to strike a balance between energy saving and traffic offloading in the heterogeneous cellular network, so that the system can either activate hotspot cells for traffic offloading or to deactivate the hotspot cells for energy saving. We prove that the optimal green policy is monotone hysteretic, and our system can thus be realized by simple switch-up and switch-down thresholds, while avoiding the ping-pong effect suffered by existing works. We show via simulations that our optimal green policy can significantly reduce total energy cost and perform energy saving and traffic offloading intelligently under all traffic conditions.

Summary. With the continuous expansion of grid-connected power generation from renewable energy sources, such as wind and light, the forms of power sources are more diversified. The coupling and uncertainty of multiple energy sources will cause significant changes in the characteristics of the power system. Problems like the complexity of the power system lack a scientific and complete explanation. This paper comprehensively considers the regional weather conditions, the uncertainty of the output of wind and solar energy sources, and the time and space complementary characteristics of multiple power sources. Based on the theory of complex adaptive systems, a systematic and holistic optimization planning method is studied. Combining the idea of complementarity because of differences, a multipoint layout planning and design method for multienergy power systems is proposed to give full play to the synergistic effects of natural resources, achieve optimal resource allocation, and provide a theoretical basis for improving the consumption of wind and renewable energy. Taking an actual provincial power system in China as an example, through the comparison of production simulation calculations and multiscenario analysis and planning methods, the proposed method can significantly increase the consumption of wind and solar renewable energy.

Renewable energies are a good option to face the growing demand of energy consumption in the next generation cellular systems, where is expected a significant increase in the number of connected devices. However, there are still major challenges related to the optimization of grid consumption and energy management in cellular networks with hybrid energy sources (grid and renewable energy). In this paper, we compare two control schemes to reduce grid consumption in a heterogeneous cellular network (HetNet) powered by hybrid energy sources. Both schemes are based on a traffic flow method that offloads traffic to base stations powered by green energy. The first one is a reactive strategy that minimizes the grid consumption in each time slot. The second scheme uses a model predictive controller to incorporate forecast of green energy source behaviour. These schemes are evaluated in a simulation scenario, where it is possible to observe that the model predictive controller has a better response reducing grid consumption.

This study employed the novel Dynamic Autoregressive Distributed Lag (DYARDL) model to analyze the impact of renewable energy consumption, green technological innovation and financial development on CO2 emissions in Nigeria. Furthermore, Fully Modified Ordinary Least Square (FMOLS) and Canonical Cointegrating Regression (CCR) approaches are employed for robustness checks. The DYARDL results indicate that green technological innovation, renewable energy consumption and financial development respectively, promote environmental sustainability in the longrun, whereas, in the shortrun, green technological innovation is negatively related to the environmental quality in Nigeria. However, trade openness contributes towards environmental degradation both in the shortrun and the long run. The result of the Dynamic ARDL in this study is in conformity with the findings from both the FMOLS and CCR models. The study therefore, recommends that in order to achieve the target set by Nigeria Renewable Energy Master Plan (REMP) of generating 36% of its electricity from renewable energy sources by 2030, with a primary focus on hydro, Biomass-based, wind and solar energy sources, government should provide fiscal incentives to private investors in form of tax holidays, capital incentives, moratorium on import duties and loan opportunities for renewable energy projects in the country. Also, policymakers should strengthen policies that encourage green technological investments, in order to promote economic growth while also further reducing environmental emissions.

In this work, application of two different control strategies to three-phase DC-AC PWM inverter used in smart microgrid system, is analyzed. The objective of control design is to achieve low THD output voltage, fast transient response and asymptotic tracking of the reference output voltage under different loading conditions minimizing the effect of the harmonic frequencies. First, the PID control technique is applied to the three-phase inverter, and it is shown that PID controller works very well for the linear loads and achieves acceptable level of harmonic reduction. However, with nonlinear loads, PID controller cannot achieve satisfactory level of harmonic suppression. The second control technique – robust servomechanism problem (RSP) control design combined with sliding mode control –eliminates this problem, and achieves satisfactory level of harmonic suppression in presence of nonlinear loads. The control design is performed in discrete-time domain. For both control techniques, controller structure is defined, necessary controller parameters are determined and the simulation of the closed-loop system is performed in MATLAB/Simulink.Typically, smart microgrid systems will have several components. These components include green and renewable energy sources with their associated power converters, efficient transformers, and storage systems. To the end users, residential customers, this power can come from many different energy sources. For example photovoltaic cells, microturbines, fuel cells, etc. The photovoltaic cells (PV) are sources of DC power, whereas, high speed microturbine generators are sources of high frequency AC power. Since these generators are designed for high speed operation, they have a low weight, a low volume, a low foot print. They use natural gas with a lower carbon foot print. The fuel cells are also considered as green energy source since they have close to zero carbon foot prints. The variable wind speed generators have the same operating principle as microturbine generators, except they run at variable speed and generate variable AC power. To utilize the variable frequency AC power sources, the produced power are rectified to DC power using AC/DC rectifier, and then, DC/AC inverters are used to convert the generated DC power output to AC power at the system operating frequency. The wind and PV power sources are depicted in Fig. 5.1 and 5.2, respectively. The renewable green energy sources use three-phase DC/AC inverters, and converts direct current (DC) power to sinusoidal alternating currents (AC).KeywordsSliding ModeHarmonic FrequencyLoad VoltageNonlinear LoadLoad VersusThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Investigation of an integrated hydrogen production system based on nuclear and renewable energy sources: Comparative evaluation of hydrogen production options with a regenerative fuel cell system

With the ubiquitous data-rate demand in cellular networks, total power consumption is growing rapidly. Thus, it is essential to design the future networks with energy and spectral efficient strategies. Using heterogeneous networks (HetNets) is one possible way of improving spectral and energy efficiencies in cellular networks. In this paper, under coverage ratio and area spectral efficiency constraints, we consider deploying strategies with low power small cells in order to improve the spectral and energy efficiencies of the conventional macrocell only cellular network. To compare the efficiencies of different scenarios, we employ area spectral efficiency and power consumption per bit criterion. As a result, we observe that deploying microcells increases both energy and spectral efficiencies.

Feasibility of using renewable energy to supply data centers in 60[formula omitted] north latitude