Electric Power System Operation Mechanism with Energy Routers Based on QoS Index under Blockchain Architecture

Gong, Gangjun; Zhang, Zhening; Zhang, Xinyu; Mahato, Nawaraj Kumar; Liu, Lin; Su, Chang; Yang, Huijie

doi:10.3390/en13020418

Cited by 13 publications

(6 citation statements)

References 37 publications

(36 reference statements)

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“…Blockchain is different from general distributed storage. Each node in the blockchain stores a copy of the blockchain database [8]. Blockchain is formed by linking blocks with hash values.…”

Section: Quantum Blockchainmentioning

confidence: 99%

Research on Multimicrogrid Transaction Model and Cross-Chain Transaction Mechanism Based on Blockchain

Xue

Zhang

2022

Mathematical Problems in Engineering

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The traditional microgrid electricity transactions face a low security, poor economic benefits, and weak stability. To solve these problems, this paper proposes a multimicrogrid cross-chain transaction model based on quantum blockchain. Specifically, a bidding strategy was developed for the noncooperative dynamic game of aggregator-multimicrogrid alliance, aiming to balance and optimize the benefits of all parties and effectively enhance the consumption rate of electric energy. To improve the transaction efficiency between aggregators, a consensus mechanism was designed for multimicrogrid cross-chain communication, realizing consistent self-adaptation to cross-chain information. In addition, the quantum threshold signature technology was adopted to ensure the reliability of transaction data and create an unconditional secure communication environment. Case analysis shows that our model proposed in this paper not only ensures the security and stability of transactions but also enhances the economic benefits, providing theoretical support and decision support for the optimization of multimicronetwork cross-chain transaction model.

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Section: Quantum Blockchainmentioning

confidence: 99%

Research on Multimicrogrid Transaction Model and Cross-Chain Transaction Mechanism Based on Blockchain

Xue

Zhang

2022

Mathematical Problems in Engineering

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“…The smart metering system is, thus, the basic interaction device between prosumers and the energy blockchain. Based on the smart meter information, predictions on consumption and production patterns are possible, which are useful for planning loads on the public grid [96]. Smart meters are fundamental to organize local flexibility markets and a blockchain-based automated electricity feed-in and consumption scheme.…”

Section: Smart Meteringmentioning

confidence: 99%

“…Smart pricing concepts depend on decentral smart metering systems that assess and indicate the consumed or provided amounts at each grid participant [133]. Consumers would have to accept price fluctuations and demand-based schemes accordingly [96].…”

Section: Subsidiary Peer-to-peer (P2p) Marketsmentioning

confidence: 99%

“…Prices would then be adaptive to grid conditions, i.e., electricity is cheaper in periods of high inflow and low demand and more expensive for high demand and low inflow [135]. Electricity consumers and providers would be free to accept prices matched by the blockchain and participate in the trade [96]. The trading mechanism would be fully automatized by smart meters.…”

Section: Subsidiary Peer-to-peer (P2p) Marketsmentioning

confidence: 99%

See 1 more Smart Citation

Regional Flexibility Markets—Solutions to the European Energy Distribution Grid—A Systematic Review and Research Agenda

Rösch

Treffinger

Koch³

2021

Energies

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The German government is aiming to increase the share of renewable energies in the electricity supply to 80% in 2050. To date, however, neither the technical requirements nor the market requirements to implement this aim are provided: Germany is struggling to establish the technical requirements and the market requirements to meet this goal. As an important incentive mechanism, the German government has used and continues to use support measures, such as guaranteed feed-in tariffs, and continuously adapts these to market developments and requirements of the European Union. The purpose of the study is to outline a concept for the implementation of regional flexibility markets in Europe based on a thorough review of technical solutions. The method of a comprehensive review of research in regional flexibility markets of electricity, distribution, and pricing from the study is applied to summarize and discuss the opportunities, risks, and future potentials of grid distribution technology. Based on the insights, a new market-based supply and distribution scheme for electricity, which is aimed to benefit of a fully regenerative, decentral and fairly priced electricity markets on the European level is presented. The study suggests a blockchain based pricing mechanism which shall allow equal market access for consumer, providers, and grid operators and rewards regenerative production and short-distance transmission.

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“…The work titled "Electric Power System Operation Mechanism with Energy Routers Based on QoS Index under Blockchain Architecture" [6] by G.J. Gong et al proposed an integrated application of blockchain technology on energy routers at transmission and distribution networks with increased renewable energy penetration.…”

Section: Brief Overview Of the Contributionsmentioning

confidence: 99%

Machine Learning for Energy Systems

2020

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The objective of this editorial is to overview the content of the special issue “Machine Learning for Energy Systems”. This special issue collects innovative contributions addressing the top challenges in energy systems development, including electric power systems, heating and cooling systems, and gas transportation systems. The special attention is paid to the non-standard mathematical methods integrating data-driven black box dynamical models with classic mathematical and mechanical models. The general motivation of this special issue is driven by the considerable interest in the rethinking and improvement of energy systems due to the progress in heterogeneous data acquisition, data fusion, numerical methods, machine learning, and high-performance computing. The editor of this special issue has made an attempt to publish a book containing original contributions addressing theory and various applications of machine learning in energy systems’ operation, monitoring, and design. The response to our call had 27 submissions from 11 countries (Brazil, Canada, China, Denmark, Germany, Russia, Saudi Arabia, South Korea, Taiwan, UK, and USA), of which 12 were accepted and 15 were rejected. This issue contains 11 technical articles, one review, and one editorial. It covers a broad range of topics including reliability of power systems analysis, power quality issues in railway electrification systems, test systems of transformer oil, industrial control problems in metallurgy, power control for wind turbine fatigue balancing, advanced methods for forecasting of PV output power as well as wind speed and power, control of the AC/DC hybrid power systems with renewables and storage systems, electric-gas energy systems’ risk assessment, battery’s degradation status prediction, insulators fault forecasting, and autonomous energy coordination using blockchain-based negotiation model. In addition, review of the blockchain technology for information security of the energy internet is given. We believe that this special issue will be of interest not only to academics and researchers, but also to all the engineers who are seriously concerned about the unsolved problems in contemporary power engineering, multi-energy microgrids modeling.

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Electric Power System Operation Mechanism with Energy Routers Based on QoS Index under Blockchain Architecture

Cited by 13 publications

References 37 publications

Research on Multimicrogrid Transaction Model and Cross-Chain Transaction Mechanism Based on Blockchain

Research on Multimicrogrid Transaction Model and Cross-Chain Transaction Mechanism Based on Blockchain

Regional Flexibility Markets—Solutions to the European Energy Distribution Grid—A Systematic Review and Research Agenda

Machine Learning for Energy Systems

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