Day-Ahead Market Bidding Taking the Balancing Power Market Into Account

Klæboe, Gro; Braathen, Jørgen; Eriksrud, Anders Lund; Fleten, Stein–Erik

doi:10.2139/ssrn.3434318

Cited by 5 publications

(5 citation statements)

References 34 publications

(24 reference statements)

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“…In the previous studies (e.g., [15], [17], [20]), which have not considered ATD of balancing energy offers, there is an illegal arbitrage between day-ahead and balancing energy markets. On those works (considering high prediction for the value of stored water), one possible strategy is to allocate some (or full) part of available capacity to the day-ahead energy market and then buy it back in the downward balancing energy market with a lower price without any electricity generation obligation.…”

Section: Revenue Breakdown Analyses Of the Proposed Atdmentioning

confidence: 99%

“…B. Literature Review 1) Optimal Offer-Function in the Multimarket Setup: Optimal offer-function calculation in the multimarket setting has been addressed in the previous literature with different assumptions and perspectives [11], [12], [13], [14], [15], [16], [17]. In [11], a detailed coordinated offering strategy in the day-ahead energy and balancing markets has been presented in which the electricity price and dispatched volumes are uncertain at the time offer-function submission.…”

Section: A Motivationmentioning

confidence: 99%

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Multimarket Trading Strategy of a Hydropower Producer Considering Active-Time Duration: A Distributional Regression Approach

Khodadadi

Hesamzadeh

Söder

2023

IEEE Systems Journal

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This article presents a new approach for finding the optimal multimarket trading strategy of cascaded hydropower plants (HPPs) in the sequential electricity markets. These markets are day-ahead energy market, the market for frequency containment reserve in normal mode (FCR-N), and manual frequency restoration reserve markets for both energy production and capacity reserve. The active-time duration (ATD) of an mFRR energy offer is an important required parameter and it is uncertain at the time of day-ahead offer-function submission. Hence, we suggest a distributional regression approach for ATD modeling in an optimal multimarket setup. Also, a modified machine learning approach is proposed to generate price scenarios for the mFRR energy market taking into account uncertain ATD parameters. To illustrate our proposed approach, various numerical experiments are performed. Our numerical results show how proper modeling of ATD parameters can lead to a more realistic multimarket offer-function for cascaded HPPs. Furthermore, the results show how the inclusion of FCR-N and mFRR capacity markets change the optimal day-ahead offer-function.

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Section: Revenue Breakdown Analyses Of the Proposed Atdmentioning

confidence: 99%

Section: A Motivationmentioning

confidence: 99%

Multimarket Trading Strategy of a Hydropower Producer Considering Active-Time Duration: A Distributional Regression Approach

Khodadadi

Hesamzadeh

Söder

2023

IEEE Systems Journal

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“…Ottesen, Tomasgard and Fleten (2018) deploy a multi-stage stochastic program to derive an optimal trading strategy for a portfolio of demand side management units in three market segments: Starting with an option market that is cleared for an entire week, followed by a daily spot market and an hourly flexibility market, the trader faces three possible revenue streams with uncertain prices as in the Nordic market design. Klaeboe, Braathen, Eriksrud and Fleten (2019) continue the investigation of coordinated bidding strategies for hydropower plants in the Nordic market with a similar multi-stage stochastic approach.…”

Section: Literature Review and Research Gapmentioning

confidence: 99%

Stochastic optimization of trading strategies in sequential electricity markets

Kraft

Russo

Keles

et al. 2023

European Journal of Operational Research

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“…The runtime of the daily model varies between 3 and 17 minutes. Klaeboe et al (2019), finally, consider a three-stage stochastic program where the hydropower producer submits the spot and reserve bids in the first and third stage, respectively, while the spot market prices and reserve market prices are observed in the second and third stage, respectively. The authors report computation times between 2 and 7 hours.…”

Section: Introductionmentioning

confidence: 99%

A Planner-Trader Decomposition for Multi-Market Hydro Scheduling

Schindler¹,

Rujeerapaiboon²,

Kühn³

et al. 2021

Preprint

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Peak/off-peak spreads on European electricity forward and spot markets are eroding due to the ongoing nuclear phaseout and the steady growth in photovoltaic capacity. The reduced profitability of peak/offpeak arbitrage forces hydropower producers to recover part of their original profitability on the reserve markets. We propose a bi-layer stochastic programming framework for the optimal operation of a fleet of interconnected hydropower plants that sells energy on both the spot and the reserve markets. The outer layer (the planner's problem) optimizes end-of-day reservoir filling levels over one year, whereas the inner layer (the trader's problem) selects optimal hourly market bids within each day. Using an information restriction whereby the planner prescribes the end-of-day reservoir targets one day in advance, we prove that the trader's problem simplifies from an infinite-dimensional stochastic program with 25 stages to a finite twostage stochastic program with only two scenarios. Substituting this reformulation back into the outer layer and approximating the reservoir targets by affine decision rules allows us to simplify the planner's problem from an infinite-dimensional stochastic program with 365 stages to a two-stage stochastic program that can conveniently be solved via the sample average approximation. Numerical experiments based on a cascade in the Salzburg region of Austria demonstrate the effectiveness of the suggested framework.

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Day-Ahead Market Bidding Taking the Balancing Power Market Into Account

Cited by 5 publications

References 34 publications

Multimarket Trading Strategy of a Hydropower Producer Considering Active-Time Duration: A Distributional Regression Approach

Multimarket Trading Strategy of a Hydropower Producer Considering Active-Time Duration: A Distributional Regression Approach

Stochastic optimization of trading strategies in sequential electricity markets

A Planner-Trader Decomposition for Multi-Market Hydro Scheduling

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