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Record W2611511120 · doi:10.5539/jms.v7n4p16

Financial Viability of Residential Photovoltaic and Battery Systems in Californias

2017· article· en· W2611511120 on OpenAlexvenueno aff
Jonas Lehra, Evangelos Vrettos, Ram Rajagopal, Rishee K. Jain, Martin Everts

Bibliographic record

VenueJournal of Management and Sustainability · 2017
Typearticle
Languageen
FieldEngineering
TopicSmart Grid Energy Management
Canadian institutionsnot available
Fundersnot available
KeywordsPhotovoltaic systemBattery (electricity)ElectricityPhotovoltaicsPayback periodBenchmark (surveying)TariffComputer scienceEnvironmental economicsComputationFinanceAutomotive engineeringReliability engineeringEconomicsEnvironmental scienceBusinessElectrical engineeringEngineeringPower (physics)MicroeconomicsProduction (economics)

Abstract

fetched live from OpenAlex

Using a battery on a household level has become easier after the launch of Tesla’s Powerwall. Storing electricity during daytime’s PV overproduction or charging the battery during night with an attractive tariff is the most prominent applications. This paper explores the economic impact of the usage of residential battery storage combined with solar photovoltaics (PV) based on real load data from Northern California, USA. A data-driven, deterministic model to benchmark electricity cost savings for single households is presented and the financial viability of such systems is scrutinized for California. Our results indicate that under current capacity and price points, battery systems have limited financial viability and have a payback period exceeding 20 years in most cases. We deepen our analysis and compare the results of our deterministic model to that of a stochastic model to demonstrate that for an hourly time resolution the deterministic model provides an adequate benchmark for estimating cost (within 3%) savings with a short (1/60th) computation time.

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)0.002
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.062
Threshold uncertainty score0.507

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.006
GPT teacher head0.212
Teacher spread0.206 · 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 designObservational
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".

Quick stats

Citations3
Published2017
Admission routes1
Has abstractyes

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