A renewable energy microgrids trading management platform based on permissioned blockchain

Lei, Yu-Tian; Mirza, Nawazish; Ren, Yi-Shuai; Narayan, Seema; Chen, Xun-Qi

doi:10.1016/j.eneco.2022.106375

Cited by 33 publications

(11 citation statements)

References 112 publications

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“…The significant potential of solar energy to meet global energy demands, highlighted by Lewis and Nocera [22], emphasizes the need for technological innovation in efficiently harnessing and storing solar power. This discussion complements the exploration of a blockchain-based energy trading platform for renewable energy microgrids, showcasing the role of digital technologies in facilitating efficient and secure energy transactions and enhancing renewable energy markets [23].…”

Section: Literature Reviewmentioning

confidence: 81%

Global Residual Demand Analysis in a Deep Variable Renewable Energy Penetration Scenario for Replacing Coal: A Study of 42 Countries

Cho,

Lee,

Qvist

2024

Energies

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This study analyzes the residual demand curves of 42 countries under five scenarios with varying variable renewable energy (VRE) levels to observe how replacing coal with VRE can alter the demand curve. Using 2018 demand data, the residual demand was calculated and analyzed by subtracting the VRE supply curve from the demand curve. The operational requirements for low-carbon load-following sources amid high VRE penetration are examined. Key findings indicate that substantial peak residual demand persists even with 70% energy from VREs, emphasizing the need for significant load-following resources. Transitioning to a 70% VRE scenario could reduce CO2 emissions by approximately 16.799 billion tons, advancing towards carbon neutrality. However, this benefit depends on maintaining grid stability, highlighting the importance of adequate load-following plants to manage VRE intermittency. Countries like Malaysia, South Korea, Tunisia, the UK, Japan, Indonesia, Thailand, and Libya face higher load-following demands due to specific renewable energy contexts. This study reveals varying renewable energy environments across countries, suggesting that a universal strategy for carbon neutrality and replacing coal may not be feasible. Each nation must develop its own approach to emission reduction, considering its unique conditions. This research emphasizes the urgent need for developing cost-effective, flexible, low-carbon load-following sources to enhance decarbonization potential globally.

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Section: Literature Reviewmentioning

confidence: 81%

Global Residual Demand Analysis in a Deep Variable Renewable Energy Penetration Scenario for Replacing Coal: A Study of 42 Countries

Cho,

Lee,

Qvist

2024

Energies

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“…In recent years, wind power has emerged as a significant contributor to the global power grid. With the development of renewable energy trading management platforms, the value utilization of renewable energy is also more convenient [10]. Its inherent cleanliness and environmental friendliness have propelled its rapid integration.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Constraint (7) means that any conducted PM must be finished before the end period |T|. Constraints ( 8)- (10) calculate the continual working time of the wind turbine at each period; the continual working time of the wind turbine will be set to 0 when a PM is conducted in one period. Constraint (11) ensures that the maximum continual working time will be obeyed, which guarantees the reliability of the system.…”

Section: Mathematical Modelingmentioning

confidence: 99%

Sustainable Operation and Maintenance of Offshore Wind Farms Based on the Deep Wind Forecasting

Zhou,

Ke,

Zhu

et al. 2023

Sustainability

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Offshore wind farms are becoming a pivotal solution to address the increasing energy demand worldwide and reduce carbon emissions to achieve a sustainable energy sector. Considering the higher operational and maintenance cost of offshore wind farms, it is important to make a good maintenance plan to guarantee the system’s reliability and reduce the total cost related to maintenance activities at the same time. Because maintenance planning is a long-term decision problem and the wind force is random, long-term wind force prediction is needed to help managers evaluate the loss caused by maintenances to be executed in the future. However, long-term wind force prediction is naturally complicated, which is much harder than the short-term (e.g., day-ahead) prediction widely investigated in the literature. In order to overcome this difficulty, we design a deep learning framework combining variational mode decomposition, a convolution neural network, long short-term memory network, and full-connected network. Using the public data from the city of Leeds, the prediction accuracy of the above framework is validated by comparing it with other prediction techniques. Then, the predicted wind force is input into the established optimization model determining preventive maintenances during a predefined period. Because the uncertainty of wind force is replaced by the prediction value, the optimization model can be established as a mixed-integer linear programing model, which only contains limited variables and can be solved quickly. Lastly, an abundance of numerical experiments are conducted to validate the effectiveness of the proposed optimization model, based on which some managerial insights are provided to the managers of offshore wind farms about the optimal operations and maintenance strategy. The research outcome will greatly promote the development of the wind power industry in the future.

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“…The Function Hiding Inner Product encryption has been used to encrypt data for an energy market, such as order amounts and prices [ 60 , 61 ]. Moreover, it can infer properties or compare two encoded numbers, a useful feature in energy markets [ 62 ]. By applying functional encryption, the energy data can be compared without knowing their decrypted values, and the result can be used for other functions on the market.…”

Section: Related Workmentioning

confidence: 99%

Smart contracts and homomorphic encryption for private P2P energy trading and demand response on blockchain

Mitrea,

Toderean,

Cioara

et al. 2023

Heliyon

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A renewable energy microgrids trading management platform based on permissioned blockchain

Cited by 33 publications

References 112 publications

Global Residual Demand Analysis in a Deep Variable Renewable Energy Penetration Scenario for Replacing Coal: A Study of 42 Countries

Global Residual Demand Analysis in a Deep Variable Renewable Energy Penetration Scenario for Replacing Coal: A Study of 42 Countries

Sustainable Operation and Maintenance of Offshore Wind Farms Based on the Deep Wind Forecasting

Smart contracts and homomorphic encryption for private P2P energy trading and demand response on blockchain

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