Strategic Generation Investment in Energy Markets: A Multiparametric Programming Approach

An investor has to carefully select the location and size of new generation units it intends to build, since adding capacity in a market affects the profit from units this investor may already own. To capture this closed-loop characteristic, strategic investment (SI) of generation can be posed as a...

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Vydáno v:IEEE transactions on power systems Ročník 37; číslo 4; s. 2590 - 2600
Hlavní autoři: Taheri, Sina, Kekatos, Vassilis, Veeramachaneni, Sriharsha
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.07.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
bilevel programming
Costs
Generators
Investment
locational marginal prices
Mathematical programming with equilibrium constraints
Mixed integer
multiparametric programming
Optimization
Power flow
Programming
Schedules
Searching
Solvers
stochastic gradient descent
strategic investment
Task analysis
ISSN:0885-8950, 1558-0679
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Shrnutí:An investor has to carefully select the location and size of new generation units it intends to build, since adding capacity in a market affects the profit from units this investor may already own. To capture this closed-loop characteristic, strategic investment (SI) of generation can be posed as a bilevel optimization. By analytically studying a small market, we first show that its objective function can be non-convex and discontinuous. Realizing that existing mixed-integer problem formulations become impractical for larger markets and number of instances, this work put forth two SI solvers: a grid search to handle setups where the candidate investment locations are few, and a stochastic gradient descent approach for otherwise. Both solvers leverage powerful results of multiparametric programming (MPP), each in a unique way. The grid search entails finding the primal/dual solutions for a large number of optimal power flow (OPF) problems, which nonetheless can be efficiently computed several at once thanks to the properties of MPP. The same properties facilitate the rapid calculation of gradients in a mini-batch fashion, thus accelerating the implementation of a stochastic (sub)-gradient descent search. Tests on the IEEE 30- and 118-bus systems using real-world data corroborate the advantages of the novel solvers.
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ISSN:0885-8950
1558-0679
DOI:10.1109/TPWRS.2021.3125624