scholarly journals A Coalitional Model Predictive Control for the Energy Efficiency of Next-Generation Cellular Networks

Energies ◽  
2020 ◽  
Vol 13 (24) ◽  
pp. 6546
Author(s):  
Eva Masero ◽  
Luis A. Fletscher ◽  
José M. Maestre
Keyword(s):  
Energy Efficiency ◽  
Model Predictive Control ◽  
Cellular Networks ◽  
Predictive Control ◽  
Large Scale ◽  
Network Performance ◽  
Renewable Energy Sources ◽  
High Energy ◽  
Base Stations ◽  
Next Generation

Next-generation cellular networks are large-scale systems composed of numerous base stations interacting with many diverse users. One of the main challenges with these networks is their high energy consumption due to the expected number of connected devices. We handle this issue with a coalitional Model Predictive Control (MPC) technique for the case of next-generation cellular networks powered by renewable energy sources. The proposed coalitional MPC approach is applied to two simulated scenarios and compared with other control methods: the traditional best-signal level mechanism, a heuristic algorithm, and decentralized and centralized MPC schemes. The success of the coalitional strategy is considered from an energy efficiency perspective, which means reducing on-grid consumption and improving network performance (e.g., number of users served and transmission rates).

scholarly journals Improving Flexibility and Energy Efficiency of Post-Combustion CO2 Capture Plants Using Economic Model Predictive Control

Processes ◽  
2018 ◽  
Vol 6 (9) ◽  
pp. 135 ◽  
Author(s):  
Benjamin Decardi-Nelson ◽  
Su Liu ◽  
Jinfeng Liu
Keyword(s):  
Energy Efficiency ◽  
Power Plant ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Economic Performance ◽  
Economic Model ◽  
Power Plants ◽  
Gas Flow ◽  
Renewable Energy Sources ◽  
Economic Model Predictive Control

To reduce CO 2 emissions from power plants, electricity companies have diversified their generation sources. Fossil fuels, however, still remain an integral energy generation source as they are more reliable compared to the renewable energy sources. This diversification as well as changing electricity demand could hinder effective economical operation of an amine-based post-combustion CO 2 capture (PCC) plant attached to the power plant to reduce CO 2 emissions. This is as a result of large fluctuations in the flue gas flow rate and unavailability of steam from the power plant. To tackle this problem, efficient control algorithms are necessary. In this work, tracking and economic model predictive controllers are applied to a PCC plant and their economic performance is compared under different scenarios. The results show that economic model predictive control has a potential to improve the economic performance and energy efficiency of the amine-based PCC process up to 6% and 7%, respectively, over conventional model predictive control.

scholarly journals Energy-aware caching and collaboration for green communication systems

2020 ◽  
pp. 47-59
Keyword(s):  
Energy Efficiency ◽  
Renewable Energy ◽  
Energy Harvesting ◽  
Cellular Networks ◽  
Communication Systems ◽  
Green Energy ◽  
Base Stations ◽  
Next Generation ◽  
Content Caching ◽  
Self Powered

Social networks and mobile applications tend to enhance the need for high-quality content access. To meet the growing demand for data services in 5G cellular networks, it is important to develop effective content caching and distribution techniques, to reduce redundant data transmission and thereby improve network efficiency significantly. It is anticipated that energy harvesting and self-powered Small Base Stations' (SBS) are the rudimentary constituents of next-generation cellular networks. However, uncertainties in harvested energy are the primary reasons to opt for energy-efficient (EE) power control schemes to reduce SBS energy consumption and ensure the quality of services for users. Using edge collaborative caching, such EE design can also be achievable via the use of the content cache, decreasing the usage of capacity limited SBSs backhaul and reducing energy utilisation. Renewable energy (RE) harvesting technologies can be leveraged to manage the huge power demands of cellular networks. To reduce carbon footprint and improve energy efficiency, we tailored a more practical approach and propose green caching mechanisms for content distribution that utilise the content caching and renewable energy concept. Simulation results and analysis provide key insights that the proposed caching scheme brings a substantial improvement regarding content availability, cache storage capacity at the edge of cellular networks, enhances energy efficiency, and increases cache collaboration time up to 24%. Furthermore, self-powered base stations and energy harvesting are an ultimate part of next-generation wireless networks, particularly in terms of optimum economic sustainability and green energy in developing countries for the evolution of mobile networks.

scholarly journals Energy Efficiency and Coverage Trade-Off in 5G for Eco-Friendly and Sustainable Cellular Networks

Symmetry ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 408 ◽  
Author(s):  
Mohammed Alsharif ◽  
Anabi Kelechi ◽  
Jeong Kim ◽  
Jin Kim
Keyword(s):  
Energy Efficiency ◽  
Wireless Network ◽  
Cellular Networks ◽  
High Speed ◽  
Network Performance ◽  
Energy Savings ◽  
Load Condition ◽  
Base Stations ◽  
Entire Area ◽  
Trade Off

Recently, cellular networks’ energy efficiency has garnered research interest from academia and industry because of its considerable economic and ecological effects in the near future. This study proposes an approach to cooperation between the Long-Term Evolution (LTE) and next-generation wireless networks. The fifth-generation (5G) wireless network aims to negotiate a trade-off between wireless network performance (sustaining the demand for high speed packet rates during busy traffic periods) and energy efficiency (EE) by alternating 5G base stations’ (BSs) switching off/on based on the traffic instantaneous load condition and, at the same time, guaranteeing network coverage for mobile subscribers by the remaining active LTE BSs. The particle swarm optimization (PSO) algorithm was used to determine the optimum criteria of the active LTE BSs (transmission power, total antenna gain, spectrum/channel bandwidth, and signal-to-interference-noise ratio) that achieves maximum coverage for the entire area during the switch-off session of 5G BSs. Simulation results indicate that the energy savings can reach 3.52 kW per day, with a maximum data rate of up to 22.4 Gbps at peak traffic hours and 80.64 Mbps during a 5G BS switched-off session along with guaranteed full coverage over the entire region by the remaining active LTE BSs.

scholarly journals LVD-NMPC: A learning-based vision dynamics approach to nonlinear model predictive control for autonomous vehicles

2021 ◽  
Vol 18 (3) ◽  
pp. 172988142110195
Author(s):  
Sorin Grigorescu ◽  
Cosmin Ginerica ◽  
Mihai Zaha ◽  
Gigel Macesanu ◽  
Bogdan Trasnea
Keyword(s):  
Neural Network ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Autonomous Vehicles ◽  
Process Model ◽  
Network Performance ◽  
Vision System ◽  
Nonlinear Model Predictive Control ◽  
Scaled Model

In this article, we introduce a learning-based vision dynamics approach to nonlinear model predictive control (NMPC) for autonomous vehicles, coined learning-based vision dynamics (LVD) NMPC. LVD-NMPC uses an a-priori process model and a learned vision dynamics model used to calculate the dynamics of the driving scene, the controlled system’s desired state trajectory, and the weighting gains of the quadratic cost function optimized by a constrained predictive controller. The vision system is defined as a deep neural network designed to estimate the dynamics of the image scene. The input is based on historic sequences of sensory observations and vehicle states, integrated by an augmented memory component. Deep Q-learning is used to train the deep network, which once trained can also be used to calculate the desired trajectory of the vehicle. We evaluate LVD-NMPC against a baseline dynamic window approach (DWA) path planning executed using standard NMPC and against the PilotNet neural network. Performance is measured in our simulation environment GridSim, on a real-world 1:8 scaled model car as well as on a real size autonomous test vehicle and the nuScenes computer vision dataset.

scholarly journals Overall Efficiency of On-Site Production and Storage of Solar Thermal Energy

2021 ◽  
Vol 13 (3) ◽  
pp. 1360
Author(s):  
Teodora M. Șoimoșan ◽  
Ligia M. Moga ◽  
Livia Anastasiu ◽  
Daniela L. Manea ◽  
Aurica Căzilă ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Thermal Energy ◽  
Urban Areas ◽  
Renewable Energy Sources ◽  
Multicriteria Analysis ◽  
High Energy ◽  
Solar Thermal ◽  
Solar Thermal Energy ◽  
The Impact ◽  
And Storage

Harnessing renewable energy sources (RES) using hybrid systems for buildings is almost a deontological obligation for engineers and researchers in the energy field, and increasing the percentage of renewables within the energy mix represents an important target. In crowded urban areas, on-site energy production and storage from renewables can be a real challenge from a technical point of view. The main objectives of this paper are quantification of the impact of the consumer’s profile on overall energy efficiency for on-site storage and final use of solar thermal energy, as well as developing a multicriteria assessment in order to provide a methodology for selection in prioritizing investments. Buildings with various consumption profiles lead to achieving different values of performance indicators in similar configurations of storage and energy supply. In this regard, an analysis of the consumption profile’s impact on overall energy efficiency, achieved in the case of on-site generation and storage of solar thermal energy, was performed. The obtained results validate the following conclusion: On-site integration of solar systems allowed the consumers to use RES at the desired coverage rates, while restricted by on-site available mounting areas for solar fields and thermal storage, under conditions of high energy efficiencies. In order to segregate the results and support optimal selection, a multicriteria analysis was carried out, having as the main criteria the energy efficiency indicators achieved by hybrid heating systems.

Sign in / Sign up

Export Citation Format

Share Document