From root: How can simulations help me take better actions?

How should a grid battery be dispatched as the grid gets greener?

Grid-scale batteries promise to balance the intermittent renewables of a net-zero electricity system: but how should they be dispatched, and does the answer change as the grid gets greener?

The simulation below is fitted to five years of NESO half-hourly demand data and tracks how a 100 MW / 200 MWh battery’s state of charge, revenue, and carbon savings evolve under different dispatch strategies and grid mixes.

Pick a dispatch policy, a grid scenario, and tune the price thresholds; watch how the battery’s revenue and carbon impact evolve over a year.

Grid battery dispatch policy support: pick a dispatch strategy and grid scenario, tune the trigger thresholds; the simulator (fitted to NESO half-hourly demand data) shows the resulting battery operation, revenue, and carbon savings over a 14-day window. This is a research model fitted to open data, not a trading or operational tool.

Simulation

 

 

Live controls
Dispatch policy
Grid scenario

Price-threshold settings

Carbon-threshold settings

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The link to this project can be found here.

Data and licence: This simulation is fitted to half-hourly demand and embedded-generation data from the NESO Data Portal (NESO open data), with carbon-intensity and generation-mix figures from the Carbon Intensity API (CC BY 4.0) and solar estimates from Sheffield Solar PV_Live. The data is used here for illustration only and is not redistributed.

References and further reading

Albadi, Mohamed H., and Ehab F. El-Saadany. 2008. “A Summary of Demand Response in Electricity Markets.” Electric Power Systems Research 78 (11): 1989–96. https://doi.org/10.1016/j.epsr.2008.04.002.
Hardwick, Robert J. 2022. “Stochadex: A Simulation Engine Written in Go.” GitHub. https://stochadex.github.io/.
Hardwick, Robert J. 2026a. “energy-balancer: Energy Storage Dispatch and Demand Response Optimisation.” GitHub. https://github.com/umbralcalc/energy-balancer.
Hardwick, Robert J. 2026b. “How can simulations help me take better actions?” umbralcalculations, May. https://umbralcalc.github.io/posts/how_can_simulations_help_me_take_better_actions.html.
Hardwick, Robert J. 2026c. “How does a simulation learn its parameters from data?” umbralcalculations, November. https://umbralcalc.github.io/posts/how_does_a_simulation_learn_its_parameters_from_data.html.
Hardwick, Robert J. 2026d. “What is a state partition?” umbralcalculations, November. https://umbralcalc.github.io/posts/what_is_a_state_partition.html.
National Energy System Operator. n.d.-a. “Carbon Intensity API.” National Energy System Operator. https://carbonintensity.org.uk/.
National Energy System Operator. n.d.-b. “NESO Data Portal.” National Energy System Operator. https://www.neso.energy/data-portal.
Palensky, Peter, and Dietmar Dietrich. 2011. “Demand Side Management: Demand Response, Intelligent Energy Systems, and Smart Loads.” IEEE Transactions on Industrial Informatics 7 (3): 381–88. https://doi.org/10.1109/TII.2011.2158841.
Sheffield Solar. n.d. “PV_Live API.” University of Sheffield. https://www.solar.sheffield.ac.uk/api/.
Siano, Pierluigi. 2014. “Demand Response and Smart Grids—a Survey.” Renewable and Sustainable Energy Reviews 30: 461–78. https://doi.org/10.1016/j.rser.2013.10.022.