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Record W3024622097 · doi:10.11575/prism/37822

Microgeneration and Smart Grids: The Alberta Context

2019· article· en· W3024622097 on OpenAlexaboutno aff
Mikaela Shae Doyle

Bibliographic record

VenueUniversity of Calgary · 2019
Typearticle
Languageen
FieldEngineering
TopicIntegrated Energy Systems Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)Smart gridComputer scienceEnvironmental scienceGeologyEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

Alberta has mandated that 30 percent of its electricity must be generated from renewable sources by 2030. Microgeneration is a type of electricity generation that can contribute to this goal. Microgeneration provides many environmental and economic benefits, such as reduced greenhouse gas emissions and other forms of pollution, a reduced need for more transmission infrastructure, and lower wholesale electricity prices. Currently, microgeneration only accounts for 0.25 percent of electricity generation in Alberta, but is a rapidly growing source of electricity in the province: solar microgeneration has grown 500 percent from 6 MW in 2015 to 35 MW in 2018 (Alberta 2018a). The Alberta government wanted to understand how microgeneration could contribute to its renewable electricity goal and how growth of the industry could be supported. Therefore, in March 2017, the government directed the Alberta Utilities Commission to complete a review of distributed generation, including microgeneration, and identify barriers to distributed generation growth in the province (Alberta 2018e). One barrier identified was that the maximum amount of microgeneration that can be integrated to Alberta’s distribution grid is unknown. There is limited ability to track how much microgenerated electricity is added to the grid at any one time. As such, the maximum allowable amount of microgeneration that can be added to the grid is undefined, causing concerns regarding grid safety and reliability. This limits the incentive for distribution wire owners1 to integrate microgeneration to the grid and dissuades investment in microgeneration, limiting growth of microgeneration in the province. This lack of understanding of grid capacity will need to be addressed to promote the growth of distributed generation in Alberta as a part of efforts to increase renewable-generated electricity in the province. Smart gird technologies can provide visibility into grid capacity and support further microgeneration integration. However, such grid modernization comes at a financial cost. The Alberta Utilities Commission review explored whether distribution wire owners, microgenerators, or non-generating consumers should pay for costs associated with increased microgeneration integration by providing points of view from various stakeholders. This debate is discussed here. In this capstone, various policies that support smart grid implementation and microgeneration integration are reviewed, and costs and benefits are evaluated. Policies that directly support smart grid implementation include a smart grid implementation organizations and smart meter deployment. Smart grid implementation organizations coordinate grid upgrades across jurisdictions or multiple market players. Coordination can identify infrastructure gaps and provide information needed by distribution wire owners and grid regulators, ensuring coordinated action from multiple distribution wire owners and other relevant stakeholders in implementing smart grid technology. Such organizations can ensure equal access to upgraded grids within a region so that all constituents have access to microgeneration integration if desired. The United States has created several such coordinative organizations as part of its national smart grid strategy. Smart meters are an important smart grid technology that enables monitoring and data transfers from microgeneration units to the grid controller. Many jurisdictions in Canada have mandated and deployed smart meters as an initial and essential part of any grid modernization strategy. Several distribution wire owners and electricity retailers in Alberta already utilize smart meters, and so mandating the use of smart meters is a natural first step towards wide-spread smart grid implementation. Policies that financially support microgeneration uptake and grid modernization are also reviewed, and their costs and benefits are evaluated. These policies include feed-intariffs and net metering. A feed-in-tariff provides a guaranteed price for electricity generation over a certain period. Feed-in-tariffs have been widely used across Europe and have been successful in supporting growth in renewable electricity and microgeneration. However, feed-in-tariffs are challenging to implement properly and are often priced higher than the value of benefits received from increased renewable generation. As a result, electricity prices increase dramatically for consumers and reduce the overall benefit received from supporting microgeneration growth in the first place. Net metering is an alternative pricing approach that is designed to support microgeneration growth. Alberta currently utilizes a net billing system, where microgenerators can only apply credit received for any excess electricity provided back to the grid to the electricity portion of their bill; microgenerators are still charged transmission and distribution fees even when they do not use electricity from the grid. Net metering is a pricing mechanism that ensures the price or credit received by microgenerators for their electricity sales includes the benefits of lower transmission costs. This means that microgenerators are only charged partial transmission or distribution fees, or no such fees at all, if they do not utilize electricity from the grid. This can result in an electricity bill of $0 if they produce enough electricity from their microgeneration unit to meet their own needs, depending on the policy in place. Net metering reduces the payback time from investing in microgeneration, increasing the value of to the owner and incentivizing uptake. However, if microgenerators do not pay transmission or distribution fees, then utility companies lose revenue needed to pay for the infrastructure they provide. As a result, transmission and distribution fees may increase for non-generating consumers to make up for the revenue no longer received from microgenerators. The fairness of net metering is debated due to this cross-subsidization. A value-of-solar (VOS) style of net metering may help to offset this cross-subsidization, and it can be applied to any type of microgeneration. VOS is used to compensate microgenerators based on the amount of benefit they provide to the grid rather than the general retail price of electricity. The VOS credits microgenerators for avoided purchases of electricity from other polluting generation sources; avoided additional power plant capacity to meet peak electricity needs; providing electricity at a fixed price for a long-term; and reducing wear and tear on the electricity grid. Cross-subsidization is then minimized by compensating microgenerators based on the benefit they provide to the grid, as both utilities and non-generation consumers do not have to pay more than the benefit they receive from microgeneration. Based on the analysis of these policies, Alberta should implement a smart grid coordination organization of its own and mandate smart meter deployment. The coordinating organization will ensure strong and efficient implementation of smart grid technologies. As 70 percent of Alberta’s meters are already smart meters, mandating smart meter upgrades for the remaining 30 percent is an easy and excellent first step towards grid modernization. Mandating smart meters will lead to a better understanding of grid capacity for integrating microgeneration. Alberta should also implement a VOS-style net metering system. This will increase the value of microgeneration in the province, encouraging uptake, while minimizing the costs incurred by utilities and non-generating consumers.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.389
Threshold uncertainty score0.274

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.003
GPT teacher head0.132
Teacher spread0.129 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

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Published2019
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