Home > Research > Publications & Outputs > Optimal planning of slow-ramping power producti...

Research

Associated organisational unit

Links

Text available via DOI:

View graph of relations

Optimal planning of slow-ramping power production in energy systems with renewables forecasts and limited storage

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Published

Standard

Optimal planning of slow-ramping power production in energy systems with renewables forecasts and limited storage. / Richmond, Nathaniel; Jacko, Peter; Makowski, Armand M.
Probabilistic Methods Applied to Power Systems (PMAPS), 2014 International Conference on. IEEE, 2014. p. 1-6.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

Richmond, N, Jacko, P & Makowski, AM 2014, Optimal planning of slow-ramping power production in energy systems with renewables forecasts and limited storage. in Probabilistic Methods Applied to Power Systems (PMAPS), 2014 International Conference on. IEEE, pp. 1-6. https://doi.org/10.1109/PMAPS.2014.6960651

APA

Richmond, N., Jacko, P., & Makowski, A. M. (2014). Optimal planning of slow-ramping power production in energy systems with renewables forecasts and limited storage. In Probabilistic Methods Applied to Power Systems (PMAPS), 2014 International Conference on (pp. 1-6). IEEE. https://doi.org/10.1109/PMAPS.2014.6960651

Vancouver

Richmond N, Jacko P, Makowski AM. Optimal planning of slow-ramping power production in energy systems with renewables forecasts and limited storage. In Probabilistic Methods Applied to Power Systems (PMAPS), 2014 International Conference on. IEEE. 2014. p. 1-6 doi: 10.1109/PMAPS.2014.6960651

Author

Richmond, Nathaniel ; Jacko, Peter ; Makowski, Armand M. / Optimal planning of slow-ramping power production in energy systems with renewables forecasts and limited storage. Probabilistic Methods Applied to Power Systems (PMAPS), 2014 International Conference on. IEEE, 2014. pp. 1-6

Bibtex

@inproceedings{6c121cfca83b49a095e6d2838d30f365,
title = "Optimal planning of slow-ramping power production in energy systems with renewables forecasts and limited storage",
abstract = "We address the cost-efficient operation of an energy production system under renewables uncertainty. We develop an MDP model for an idealized system with the following features: (1) perfectly predictable power demand, (2) a renewable power source subject to uncertain forecast, (3) limited energy storage, (4) an unlimited fast-ramping power source, and (5) a slow-ramping power source which requires (optimal) planning. A finite-horizon stochastic optimization problem is introduced to minimize the overall cost of operating the system, and then solved numerically using standard approaches (based on backward induction) and available data. In contrast with the unit commitment problem which is traditionally optimized for a single planning frame, we show in simple scenarios that it may be beneficial to optimize over a few planning frames, and that there is no benefit to considering longer (e.g., infinite) horizons. We discretize the state space in an attempt to mitigate the curse of dimensionality usually associated with numerically solving MDPs. We note that few discretization states already yield a significant decrease in the total cost.",
author = "Nathaniel Richmond and Peter Jacko and Makowski, {Armand M.}",
year = "2014",
doi = "10.1109/PMAPS.2014.6960651",
language = "English",
pages = "1--6",
booktitle = "Probabilistic Methods Applied to Power Systems (PMAPS), 2014 International Conference on",
publisher = "IEEE",

}

RIS

TY - GEN

T1 - Optimal planning of slow-ramping power production in energy systems with renewables forecasts and limited storage

AU - Richmond, Nathaniel

AU - Jacko, Peter

AU - Makowski, Armand M.

PY - 2014

Y1 - 2014

N2 - We address the cost-efficient operation of an energy production system under renewables uncertainty. We develop an MDP model for an idealized system with the following features: (1) perfectly predictable power demand, (2) a renewable power source subject to uncertain forecast, (3) limited energy storage, (4) an unlimited fast-ramping power source, and (5) a slow-ramping power source which requires (optimal) planning. A finite-horizon stochastic optimization problem is introduced to minimize the overall cost of operating the system, and then solved numerically using standard approaches (based on backward induction) and available data. In contrast with the unit commitment problem which is traditionally optimized for a single planning frame, we show in simple scenarios that it may be beneficial to optimize over a few planning frames, and that there is no benefit to considering longer (e.g., infinite) horizons. We discretize the state space in an attempt to mitigate the curse of dimensionality usually associated with numerically solving MDPs. We note that few discretization states already yield a significant decrease in the total cost.

AB - We address the cost-efficient operation of an energy production system under renewables uncertainty. We develop an MDP model for an idealized system with the following features: (1) perfectly predictable power demand, (2) a renewable power source subject to uncertain forecast, (3) limited energy storage, (4) an unlimited fast-ramping power source, and (5) a slow-ramping power source which requires (optimal) planning. A finite-horizon stochastic optimization problem is introduced to minimize the overall cost of operating the system, and then solved numerically using standard approaches (based on backward induction) and available data. In contrast with the unit commitment problem which is traditionally optimized for a single planning frame, we show in simple scenarios that it may be beneficial to optimize over a few planning frames, and that there is no benefit to considering longer (e.g., infinite) horizons. We discretize the state space in an attempt to mitigate the curse of dimensionality usually associated with numerically solving MDPs. We note that few discretization states already yield a significant decrease in the total cost.

U2 - 10.1109/PMAPS.2014.6960651

DO - 10.1109/PMAPS.2014.6960651

M3 - Conference contribution/Paper

SP - 1

EP - 6

BT - Probabilistic Methods Applied to Power Systems (PMAPS), 2014 International Conference on

PB - IEEE

ER -