Economically optimised power dispatch in local systems using evolutionary algorithms and dynamic programming

Hable, Matthias; Meisenbach, C.; Winkler, G.

doi:10.1049/cp:20020030

Cited by 3 publications

(1 citation statement)

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“…To maximize utility in expectation, we employed dynamic programming [Bellman, 1957], a technique that solves complex stochastic optimization problems by breaking them into simpler, solvable subproblems [Dasgupta et al, 2006]. Dynamic programming has been used to solve decision problems under weather uncertainty in fields including power dispatch [Hable et al, 2002], irrigation [Wilks and Wolfe, 1998], and air traffic management [Nilim et al, 2001]. Using dynamic programming, the implications of any flight decision can be broken into two parts: the implications for today and the implications for the rest of the experiment [Hanlon et al, 2013].…”

Section: Optimization Modulementioning

confidence: 99%

Automated decision algorithm applied to a field experiment with multiple research objectives: The DC3 campaign

Hanlon

Small²,

Bose³

et al. 2014

J. Geophys. Res. Atmos.

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Automated decision systems have shown the potential to increase data yields from field experiments in atmospheric science. The present paper describes the construction and performance of a flight decision system designed for a case in which investigators pursued multiple, potentially competing objectives. The Deep Convective Clouds and Chemistry (DC3) campaign in 2012 sought in situ airborne measurements of isolated deep convection in three study regions: northeast Colorado, north Alabama, and a larger region extending from central Oklahoma through northwest Texas. As they confronted daily flight launch decisions, campaign investigators sought to achieve two mission objectives that stood in potential tension to each other: to maximize the total amount of data collected while also collecting approximately equal amounts of data from each of the three study regions. Creating an automated decision system involved understanding how investigators would themselves negotiate the trade-offs between these potentially competing goals, and representing those preferences formally using a utility function that served to rank-order the perceived value of alternative data portfolios. The decision system incorporated a custom-built method for generating probabilistic forecasts of isolated deep convection and estimated climatologies calibrated to historical observations. Monte Carlo simulations of alternative future conditions were used to generate flight decision recommendations dynamically consistent with the expected future progress of the campaign. Results show that a strict adherence to the recommendations generated by the automated system would have boosted the data yield of the campaign by between 10 and 57%, depending on the metrics used to score success, while improving portfolio balance.

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