dataset · Zenodo (CERN European Organization for Nuclear Research)
This dataset and code deposit supports research into carbon-aware predictive dispatch for a grid-connected hybrid renewable energy system. The examined plant incorporates a 50 kW photovoltaic array, a 60 kW wind turbine, and a 100 kWh lithium-ion battery subjected to a four-period time-of-use tariff. Under this tariff, export remuneration and off-peak import rates both equal 0.08 EUR/kWh. This price parity creates a signal degeneracy where charging via local renewable surplus or the overnight grid costs the same, despite a two-fold difference in carbon intensity. Standard cost-minimising controllers remain indifferent between these choices, causing dispatch policies to vary in annual emissions by 10.6 percent. Introducing a carbon shadow price of 0.02 EUR/kg resolves this indifference, reducing annual emissions by 10.6 percent and operating costs by 2.7 percent simultaneously. The deposit provides synthetic profiles, reproducible generation scripts, and complete experimental result sets.
Renewable energy systems controlled solely by electricity prices can inadvertently draw carbon-intensive grid power when tariffs fail to reflect emissions. Demonstrating that identical prices can mask substantial emission differences highlights a critical flaw in tariff structures. Providing methods to factor carbon intensity into automated dispatch helps clean energy operators cut both environmental impact and operational expenditure without requiring changes to hardware.
The findings are relevant to energy management system developers, software providers for microgrids, and industrial operators managing hybrid solar, wind, and battery assets. The work operates at the level of simulation and algorithmic testing using synthetic data rather than live field deployments. Implementing these carbon shadow price controls into predictive dispatch software could enable operators to reduce operational expenses and carbon footprints under specific time-of-use contracts.
AI-generated from the published abstract. Always read the original work before citing.
This deposit contains the complete input dataset and a reproducible generator for a study of carbon-aware risk-constrained stochastic predictive dispatch in a grid-connected hybrid renewable plant comprising a 50 kW photovoltaic array, a 60 kW wind turbine and a 100 kWh lithium-ion battery operating under a four-period time-of-use tariff. The associated study identifies a degeneracy in the price signal. Export remuneration and the off-peak import tariff are both 0.08 EUR/kWh, so charging the battery from local surplus and charging it from the overnight grid carry identical cost while differing by a factor of two in carbon intensity. A cost-minimising controller is therefore indifferent across a set of dispatch policies whose annual emissions differ by 10.6 percent. Admitting a carbon shadow price of 0.02 EUR/kg resolves the indifference and lowers annual emissions by 10.6 percent and annual operating cost by 2.7 percent at the same time. The coincidence of the two prices is a property of the deposited tariff rather than an oversight, and it is the object of the study. The generator, HRES_Dataset_Generator.m, regenerates every exogenous input: the ground-truth photovoltaic, wind and demand profiles for three weather regimes; the tariff with its period index; the export price; the diurnal grid carbon intensity; and the Monte Carlo forecast uncertainty ensembles. The profiles reproduce bit-for-bit under the seeding protocol documented in the README. It requires base MATLAB R2024a with no toolboxes. The deposit also contains the numerical results underlying every table and figure of the associated article and its supplementary file: ten comma-separated result files covering comparative performance, paired significance tests, annualised techno-economics, scenario reduction fidelity, forecast-error stress response, non-anticipativity ablation, carbon accounting under two conventions, the cost-carbon frontier, risk-measure degeneracy and the lifecycle carbon assessment; the per-seed campaign record over 30 independent realisations in each of three regimes; and thirty-four vector figures, seventeen of which appear in the article and seventeen in the supplementary file. All profiles are synthetic. They are constructed to span surplus-dominated, deficit-dominated and volatility-dominated operation rather than to reproduce any specific location, and the grid carbon intensity is an average-factor model.
This page summarises published work. The authoritative version sits with the publisher.
DOI: 10.5281/zenodo.21874938
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