Blockchain for Smart Grid Energy Trading: Opportunities and Cybersecurity Challenges
Bibliographic record
Abstract
The integration of blockchain technology in smart grid energy trading has emerged as a transformative approach to enhance efficiency, transparency, and decentralization in modern power systems. As energy grids evolve to incorporate distributed energy resources (DERs) such as solar photovoltaics, wind turbines, and battery storage systems, there is a growing need for secure, automated, and peer-to-peer energy trading mechanisms. Blockchain, with its decentralized, immutable, and transparent ledger, presents significant opportunities in enabling secure transactions, reducing transaction costs, and improving market access for prosumers—individuals or entities that both produce and consume energy. This explores the dual dimensions of blockchain applications in smart grid energy trading: the opportunities it presents and the cybersecurity challenges it introduces. Key opportunities include enhanced peer-to-peer energy trading, real-time settlement of transactions, and improved grid flexibility through smart contracts. Additionally, blockchain can facilitate the integration of renewable energy sources and enable innovative pricing models that incentivize grid balancing and demand-side participation. However, the deployment of blockchain in energy systems also raises critical cybersecurity concerns. These include risks of consensus mechanism vulnerabilities, smart contract exploits, data privacy breaches, and scalability limitations that may expose the energy grid to malicious attacks or operational failures. Moreover, the immutable nature of blockchain complicates the reversal of erroneous or fraudulent transactions. This underscores the importance of adopting robust cybersecurity frameworks, secure consensus algorithms, and privacy-preserving techniques to mitigate these risks. It calls for interdisciplinary collaboration between energy utilities, blockchain developers, cybersecurity experts, and regulators to design resilient systems. Ultimately, while blockchain offers promising solutions for decentralizing and optimizing energy trading, its widespread adoption in smart grids hinges on effectively addressing cybersecurity risks to ensure safe, reliable, and equitable energy markets in the digital era.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".