Efficient Forecasting Scheme and Optimal Delivery Approach of Energy for the Energy Internet

Du, Liufeng; Zhang, Linghua; Tian, Xiyan; Lei, Jinhui

doi:10.1109/access.2018.2812211

Cited by 14 publications

(7 citation statements)

References 37 publications

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“…Energy Internet plays an important role to utilize the RES efficiently and intelligently. The authors in [21] discussed the efficient forecasting and optimum utilization of energy. A hierarchical integration architecture is designed for energy Internet.…”

Section: Related Workmentioning

confidence: 99%

Energy Management in Smart Sectors Using Fog Based Environment and Meta-Heuristic Algorithms

et al. 2019

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Smart Grid (SG) plays vital role in modern electricity grid. The data is increasing with the drastic increase in number of users. An efficient technology is required to handle this dramatic growth of data. Cloud computing is then used to store the data and to provide numerous services to the consumers. There are various cloud Data Centers (DC), which deal with the requests coming from consumers. However, there is a chance of delay due to the large geographical area between cloud and consumer. So, a concept of fog computing is presented to minimize the delay and to maximize the efficiency. However, the issue of load balancing is raising; as the number of consumers and services provided by fog grow. So, an enhanced mechanism is required to balance the load of fog. In this paper, a three-layered architecture comprising of cloud, fog and consumer layers is proposed. A meta-heuristic algorithm: Improved Particle Swarm Optimization with Levy Walk (IPSOLW) is proposed to balance the load of fog. Consumers send request to the fog servers, which then provide services. Further, cloud is deployed to save the records of all consumers and to provide the services to the consumers, if fog layer is failed. The proposed algorithm is then compared with existing algorithms: genetic algorithm, particle swarm optimization, binary PSO, cuckoo with levy walk and BAT. Further, service broker policies are used for efficient selection of DC. The service broker policies used in this paper are: closest data center, optimize response time, reconfigure dynamically with load and new advance service broker policy. Moreover, response time and processing time are minimized. The IPSOLW has outperformed to its counterpart algorithms with almost 4.89% better results.INDEX TERMS Cloud computing, fog computing, smart grid, smart city, load balancing, server broker policies.

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Section: Related Workmentioning

confidence: 99%

Energy Management in Smart Sectors Using Fog Based Environment and Meta-Heuristic Algorithms

et al. 2019

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“…Besides Big Data, the recent advancement of information and communication technologies allows the regime switch from a traditional ''predict (forecast) and provide'' approach to a more flexible and responsive demand-based approach of power system management. The purpose of this approach is to reach several policy targets regarding sustainability, such as reducing carbon emissions, generating power from renewable resources to a certain percentage, smoothing peak demand, assuring a better rate of return on investments, and preventing network overprovisioning [15], [39], [40].…”

Section: Introductionmentioning

confidence: 99%

A Qualitative Study on the United States Internet of Energy: A Step Towards Computational Sustainability

2020

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The burgeoning growth of Big Data not only matures and improves the data management efficiency and useful information extraction techniques, but also motivates the computational science researchers to come up with a new method or solution that can be repurposed for problems across the domain. Computational Sustainability joins this movement for a transferrable computational technique for sustainable development and a better future. Internet-of-energy (IoE)-leveraging IoT to smart grids associated with advanced analytics-is one of the prominent efforts in this regard. This paper presents a qualitative analysis on the elements of the energy and power management ecosystem in the United States. This qualitative study includes the Grid Overview of the United States; Weather and Climate and its impact on the entire energy generation and consumption dynamics; Peak Load Forecasting and its techniques and burgeoning challenges; Variable Renewable Energy, its reliability challenges and how we can take advantage of this variability; Commodity Prices and its criticality; Energy Disaggregation and its impact on consumptionawareness; and Generation Expansion and Decision Analysis. Besides, IoE integration, associated tradeoffs, challenges, research opportunities and transferable computational techniques are addressed in this communication. Furthermore, schematics and quantitative analysis are presented in support of this study.INDEX TERMS Computational sustainability, Internet of Things, smart grid, Internet of Energy, big data, transferable computational techniques.

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“…Lin et al optimized the IoE operation by allowing prosumers to employ battery storage utilities to charge and discharge the electricity and shifted the peak load demand effectively [7]. Du et al presented a hierarchical power management scheme for the IoE to exchange tremendous amounts of power and information for renewable-grid deployment on a large scale [8]. Dou et al took power network security into consideration and developed four types of differential hybrid Petri-net control mechanism, in which the power generated by distributed renewables were fully utilized to meet peak electricity load [9].…”

Section: Introductionmentioning

confidence: 99%

An Emission-Aware Day-Ahead Power Scheduling System for Internet of Energy

2019

KSII TIIS

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As a subset of the Internet of Things, the Internet of Energy (IoE) is expected to tackle the problems faced by the current smart grid framework. Notably, the conventional day-ahead power scheduling of the smart grid should be redesigned in the IoE architecture to take into consideration the intermittence of scattered renewable generations, large amounts of power consumption data, and the uncertainty of the arrival time of electric vehicles (EVs).Accordingly, a day-ahead power scheduling system for the future IoE is proposed in this research to maximize the usage of distributed renewables and reduce carbon emission caused by the traditional power generation. Meanwhile, flexible charging mechanism of EVs is employed to provide preferred charging options for moving EVs and flatten the load profile simultaneously. The simulation results revealed that the proposed power scheduling mechanism not only achieves emission reduction and balances power load and supply effectively, but also fits each individual EV user's preference.

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Efficient Forecasting Scheme and Optimal Delivery Approach of Energy for the Energy Internet

Cited by 14 publications

References 37 publications

Energy Management in Smart Sectors Using Fog Based Environment and Meta-Heuristic Algorithms

Energy Management in Smart Sectors Using Fog Based Environment and Meta-Heuristic Algorithms

A Qualitative Study on the United States Internet of Energy: A Step Towards Computational Sustainability

An Emission-Aware Day-Ahead Power Scheduling System for Internet of Energy

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