Distributed Communication and Control Frameworks for Smart Grids using the Internet of Things and Blockchain Technology
Bibliographic record
Abstract
Smart distribution grids (SDGs) are power systems that harness distributed energy resources (DERs) to increase their operational efficiency and sustainability. However, the uncontrolled operation of DERs lead to operational challenges, resulting in transformer overload and voltage violations. Distribution system operators (DSOs) are responsible for preventing such issues, however, DERs are typically owned by agents such as homeowners and private enterprises, whose motivations revolve around financial incentives and maximizing operational convenience, which do not always align with the DSO's objectives. Thus, new communication and control frameworks are required to coordinate the actions of agents and DSOs to deliver mutually beneficial results. The architectures of these frameworks should be distributed to avoid unilateral authority, and auditable to alleviate any trust issues between participants. \n \nThus, this thesis develops distributed communication and control frameworks for SDGs that are built upon modern communication technologies such as the Internet of Things (IoT), and blockchains, both of which provide architectures that are distributed. The proposed control strategies of this thesis are inspired from principles related to transactive energy systems (TES), where distributed control techniques are combined with economically oriented decision making to improve overall energy efficiency. \n \nAccordingly, this thesis proposes three new frameworks, and validates their efficacy using both simulated and real-world experiments at a microgrid in Vaughan, Ontario. First, a fully distributed communication framework (DCF) is proposed for agent messaging, which is built upon the IoT-based framework known as Data Distribution Service (DDS). The DCF provides 1000 messages/second at 36 millisecond latency, and also enhances the efficacy of agents in resolving voltage violations in real-time at the microgrid. Second, a blockchain-based TES is proposed to enable agents to bid for voltage regulation services, where smart contracts enable multiple violations to be resolved in parallel, leading to less bidding cycles. Third, a blockchain-based residential energy trading system (RETS) is proposed , which enables residential communities and DSOs to participate in peer to peer energy trading and demand response. The RETS reduces the peak demand of the community by 48 kW (62%), which leads to an average savings of $1.02 M for the DSO by avoiding transformer upgrades.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".