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1033. Optimal Hour-Ahead Commitment and Storage Decisions of Wind Power Producers

Invited abstract in session MD-22: Energy transition and operations, stream Energy Management.

Monday, 14:30-16:00
Room: 81 (building: 116)

Authors (first author is the speaker)

1. Ece Cigdem Karakoyun
Econometrics, Erasmus University Rotterdam
2. Harun Avci
Industrial Engineering and Management Sciences, Northwestern University
3. Tim Huh
Sauder School of Business, University of British Columbia,
4. Ayse Selin Kocaman
Industrial Engineering, Bilkent University
5. Emre Nadar
Bilkent University

Abstract

Renewable energy generators often rely on their battery deployments to meet their dispatch or purchase commitments in electricity markets. However, the literature optimizing the commitment decisions with storage considerations is scarce. In this paper, we study the joint energy commitment and storage problem for a wind farm paired with a battery. The power producer decides, in each hour of a finite planning horizon, how much energy to commit to dispatching or purchasing for the next hour as well as how much wind energy to generate and how much energy to charge or discharge. The power producer pays a penalty cost if she does not fully meet her commitment. We model this problem as a Markov decision process with random electricity price and wind speed. We prove the optimality of a state-dependent threshold policy under positive prices. This policy partitions the state space into several disjoint domains, each associated with a different action type, such that it is optimal to bring the storage and commitment levels to a different threshold pair in each domain. We employ our structural results to develop a heuristic solution procedure in a more general problem where the price can also be negative. Numerical results for data-calibrated instances indicate the high efficiency and scalability of our solution procedure: it yields optimal or near-optimal solutions with a speedup of two orders of magnitude over the standard dynamic programming algorithm.

Keywords

Status: accepted

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