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Impact of Optimal Storage Allocation on Price Volatility in Energy-Only Electricity Markets
KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.
2018 (English)In: IEEE Transactions on Power Systems, ISSN 0885-8950, E-ISSN 1558-0679, Vol. 33, no 2, p. 1903-1914Article in journal (Refereed) Published
Abstract [en]

Recent studies show that the fast growing expansion of wind power generation may lead to extremely high levels of price volatility in wholesale electricity markets. Storage technologies, regardless of their specific forms, e.g., pump-storage hydro, large-scale, or distributed batteries, are capable of alleviating the extreme price volatility levels due to their energy usage time shifting, fast-ramping, and price arbitrage capabilities. In this paper, we propose a stochastic bilevel optimization model to find the optimal nodal storage capacities required to achieve a certain price volatility level in a highly volatile energy-only electricity market. The decision on storage capacities is made in the upper level problem and the operation of strategic/regulated generation, storage, and transmission players is modeled in the lower level problem using an extended stochastic (Bayesian) Cournot-based game. The South Australia (SA) electricity market, which has recently experienced high levels of price volatility, and a 30-bus IEEE system are considered as the case studies. Our numerical results indicate that 50% price volatility reduction in the SA electricity market can be achieved by installing either 430-MWh regulated storage or 530-MWh strategic storage. In other words, regulated storage firms are more efficient in reducing the price volatility than strategic storage firms.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC , 2018. Vol. 33, no 2, p. 1903-1914
Keywords [en]
Bi-level optimization model, electricity market, Price volatility, storage technologies, strategic and regulated firms
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-224015DOI: 10.1109/TPWRS.2017.2727075ISI: 000425530300066Scopus ID: 2-s2.0-85028946729OAI: oai:DiVA.org:kth-224015DiVA, id: diva2:1192820
Note

QC 20180323

Available from: 2018-03-23 Created: 2018-03-23 Last updated: 2018-03-23Bibliographically approved

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