An Efficient Energy Management Approach Using Fog-as-a-Service for Sharing Economy in a Smart Grid

Khalid, Adia; Aslam, Sheraz; Aurangzeb, Khursheed; Haider, Syed Irtaza; Ashraf, Mahmood; Javaid, Nadeem

doi:10.3390/en11123500

Cited by 36 publications

(24 citation statements)

References 22 publications

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“…The authors in Reference [30] proposed a decentralized market mechanism using a private blockchain and an auction method to determine the price. However, trading of surplus energy through auction or bidding is logically impossible for all users [33]. The reason is that some consumers or prosumers cannot participate in the trading due to limited time, lack of expertise, and above all, lack of technology.…”

Section: Problem Statementmentioning

confidence: 99%

Blockchain Based Sustainable Local Energy Trading Considering Home Energy Management and Demurrage Mechanism

et al. 2020

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With the increase in local energy generation from Renewable Energy Sources (RESs), the concept of decentralized peer-to-peer Local Energy Market (LEM) is becoming popular. In this paper, a blockchain-based LEM is investigated, where consumers and prosumers in a small community trade energy without the need for a third party. In the proposed model, a Home Energy Management (HEM) system and demurrage mechanism are introduced, which allow both the prosumers and consumers to optimize their energy consumption and to minimize electricity costs. This method also allows end-users to shift their load to off-peak hours and to use cheap energy from the LEM. The proposed solution shows how energy consumption and electricity cost are optimized using HEM and demurrage mechanism. It also provides economic benefits at both the community and end-user levels and provides sufficient energy to the LEM. The simulation results show that electricity cost is reduced up to 44.73% and 28.55% when the scheduling algorithm is applied using the Critical Peak Price (CPP) and Real-Time Price (RTP) schemes, respectively. Similarly, 65.15% and 35.09% of costs are reduced when CPP and RTP are applied with demurrage mechanism. Moreover, 51.80% and 44.37% electricity costs reduction is observed when CPP and RTP are used with both demurrage and scheduling algorithm. We also carried out security vulnerability analysis to ensure that our energy trading smart contract is secure and bug-free against the common vulnerabilities and attacks.

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Section: Problem Statementmentioning

confidence: 99%

Blockchain Based Sustainable Local Energy Trading Considering Home Energy Management and Demurrage Mechanism

et al. 2020

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“…They utilize a fog node as a sanitizer that randomly transfers encrypted energy states and DR strategies in a homomorphic manner. Khalid et al [41] investigated a fog-based approach to share energy among consumers, where a fog server enables the consumers to communicate each other. Javed et al [42] addressed the case of communication failure between a cloud BEMS and endpoints (e.g., electric vehicles).…”

Section: Fog Computing In Smart Energy Applicationsmentioning

confidence: 99%

Fog BEMS: An Agent-Based Hierarchical Fog Layer Architecture for Improving Scalability in a Building Energy Management System

Lee

2020

Sustainability

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It has been found that a cloud building energy management system (BEMS) alone cannot support increasing numbers of end devices (e.g., energy equipment and IoT devices) and emerging energy services efficiently. To resolve these limitations, this paper proposes Fog BEMS, which applies an emerging fog computing concept to a BEMS. Fog computing places small computing resources (fog nodes) just next to end devices, and these nodes process data in real time and manage local contexts. In this way, the BEMS becomes distributed and scalable. However, existing fog computing models have barely considered scenarios where many end devices and fog nodes are deployed and interconnected. That is, they do not scale up and cannot be applied to scalable applications like BEMS. To solve the problem, this paper (i) designs a fog network where a list of functionally heterogeneous nodes is deployed in a hierarchy for collaboration and (ii) designs an agent-based, modular programming model that eases the development and management of computing services at a fog node. We develop a prototype of a fog node and build a real-world testbed on a campus to demonstrate the feasibility of the proposed system. We also conduct experiments, and results show that Fog BEMS is scalable enough for a node to connect up to 900 devices and that network traffic is reduced by 27.22–97.63%, with varying numbers of end devices.

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“…For a randomly selected day, the proposed model is applied to predict the wind speed for 24 h. The predicted wind speed is then used to approximate the generated wind energy based on Equation 8, which we also used in our recent work [69]. The predicted wind speed and generated wind power are presented in Figure 16.…”

Section: Generation Estimation and Bill Reduction Analysis: San Francmentioning

confidence: 99%

“…It is clear from the figure that with an increase in the wind speed, the power generated by the wind turbine also increases. The power generation of the wind turbine is approximated by implementing Equation 8 For a randomly selected day, the proposed model is applied to predict the wind speed for 24 h. The predicted wind speed is then used to approximate the generated wind energy based on Equation (8), which we also used in our recent work [69]. The predicted wind speed and generated wind power are presented in Figure 16.…”

Section: Generation Estimation and Bill Reduction Analysis: San Francmentioning

confidence: 99%

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Microgrid-Level Energy Management Approach Based on Short-Term Forecasting of Wind Speed and Solar Irradiance

2019

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Background: The Distributed Energy Resources (DERs) are beneficial in reducing the electricity bills of the end customers in a smart community by enabling them to generate electricity for their own use. In the past, various studies have shown that owing to a lack of awareness and connectivity, end customers cannot fully exploit the benefits of DERs. However, with the tremendous progress in communication technologies, the Internet of Things (IoT), Big Data (BD), machine learning, and deep learning, the potential benefits of DERs can be fully achieved, although a significant issue in forecasting the generated renewable energy is the intermittent nature of these energy resources. The machine learning and deep learning models can be trained using BD gathered over a long period of time to solve this problem. The trained models can be used to predict the generated energy through green energy resources by accurately forecasting the wind speed and solar irradiance. Methods: We propose an efficient approach for microgrid-level energy management in a smart community based on the integration of DERs and the forecasting wind speed and solar irradiance using a deep learning model. A smart community that consists of several smart homes and a microgrid is considered. In addition to the possibility of obtaining energy from the main grid, the microgrid is equipped with DERs in the form of wind turbines and photovoltaic (PV) cells. In this work, we consider several machine learning models as well as persistence and smart persistence models for forecasting of the short-term wind speed and solar irradiance. We then choose the best model as a baseline and compare its performance with our proposed multiheaded convolutional neural network model. Results: Using the data of San Francisco, New York, and Los Vegas from the National Solar Radiation Database (NSRDB) of the National Renewable Energy Laboratory (NREL) as a case study, the results show that our proposed model performed significantly better than the baseline model in forecasting the wind speed and solar irradiance. The results show that for the wind speed prediction, we obtained 44.94%, 46.12%, and 2.25% error reductions in root mean square error (RMSE), mean absolute error (MAE), and symmetric mean absolute percentage error (sMAPE), respectively. In the case of solar irradiance prediction, we obtained 7.68%, 54.29%, and 0.14% error reductions in RMSE, mean bias error (MBE), and sMAPE, respectively. We evaluate the effectiveness of the proposed model on different time horizons and different climates. The results indicate that for wind speed forecast, different climates do not have a significant impact on the performance of the proposed model. However, for solar irradiance forecast, we obtained different error reductions for different climates. This discrepancy is certainly due to the cloud formation processes, which are very different for different sites with different climates. Moreover, a detailed analysis of the generation estimation and electricity bill reduction indicates that the proposed framework will help the smart community to achieve an annual reduction of up to 38% in electricity bills by integrating DERs into the microgrid. Conclusions: The simulation results indicate that our proposed framework is appropriate for approximating the energy generated through DERs and for reducing the electricity bills of a smart community. The proposed framework is not only suitable for different time horizons (up to 4 h ahead) but for different climates.

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An Efficient Energy Management Approach Using Fog-as-a-Service for Sharing Economy in a Smart Grid

Cited by 36 publications

References 22 publications

Blockchain Based Sustainable Local Energy Trading Considering Home Energy Management and Demurrage Mechanism

Blockchain Based Sustainable Local Energy Trading Considering Home Energy Management and Demurrage Mechanism

Fog BEMS: An Agent-Based Hierarchical Fog Layer Architecture for Improving Scalability in a Building Energy Management System

Microgrid-Level Energy Management Approach Based on Short-Term Forecasting of Wind Speed and Solar Irradiance

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