Portfolio size’s effects on gains from coordinated bidding in electricity markets

Kongelf, Håkon; Overrein, Kristoffer; Klæboe, Gro; Fleten, Stein–Erik

doi:10.1007/s12667-018-0294-8

Cited by 13 publications

(10 citation statements)

References 26 publications

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“…Our results of limited gains are in line with previous results (e.g. [27]) but are moderate compared to [28], which find gains up to 2%. However, they do not model uncertain demand in the balancing market as we do and thus their estimates are more optimistic.…”

Section: Coordinated Versus Sequential Biddingsupporting

confidence: 93%

Backtesting coordinated hydropower bidding using neural network forecasting

et al. 2021

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A stochastic programming model for a price-taking, profit-maximizing hydropower producer participating in the Nordic day-ahead and balancing market is developed and evaluated by backtesting over 200 historical days. We find that the producer may gain 0.07% by coordinating its trades in the day-ahead and balancing market, compared to considering the two markets sequentially. It is thus questionable whether a coordinated bidding strategy is worthwhile. However, the gain from coordinating trades is dependent on the quality of the forecasts for the balancing market. The limited gain of 0.07% comes from using an artificial neural network prediction model that is trained on historical data on seasonal effects, day-ahead market price, wind and temperature forecasts. To quantify the effect of the forecasting model on the gain of coordination, we therefore develop a benchmarking framework for two additional prediction models: a naive forecast predicting zero imbalance in expectation, and a perfect information forecast. Using the naive method, we estimate the lower bound of coordination to be 0.0% which coincides with theory. When having perfect information, we find that the upper bound for the gain is 3.8% which indicates that a substantial gain in profits can be obtained by coordinated bidding if accurate prediction methods could be developed.

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Section: Coordinated Versus Sequential Biddingsupporting

confidence: 93%

Backtesting coordinated hydropower bidding using neural network forecasting

et al. 2021

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“…Based on data from the Scandinavian Nord Pool market, the authors report gains of 25% for a price-taker and of up to 5% for a market where there is price impact. Kongelf et al (2019) propose a three-stage stochastic program that additionally considers the market for primary reserves and report gains of less than 1%. As this literature focuses on the Scandinavian markets, where intraday trading still plays only a minor role, intraday trading is not considered.…”

Section: Introductionmentioning

confidence: 99%

The Value of Coordination in Multimarket Bidding of Grid Energy Storage

Löhndorf

Wozabal

2023

Operations Research

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Grid energy storage plays a key role in making carbon-free, renewable energy production a reality. Yet, when it comes to maximizing profit, owners of storage assets still struggle with coordinating their trading activities across time because of the complex nature of multisettlement electricity markets. In “Coordination of Multimarket Bidding of Grid-Energy Storage,” Nils Löhndorf and David Wozabal propose a multistage stochastic programming model for market-oriented optimization of energy storage. To calculate lower and upper bounds on optimal values, they develop novel methods for scenario-tree generation and information relaxation. They show that a coordinated policy that reserves capacity for the short-term markets is optimal and that the gap to a sequential policy increases with short-term price volatility and market liquidity. The authors find that coordination is beneficial for all considered asset types and that flexible storages with high price impact benefit most. Their findings inform storage owners which markets contribute most value, how to organize trading across time, and how to calculate optimal bidding strategies.

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“…The upper level is a stochastic program that models the integrated trading, itself, while the lower-level problems are the dispatching problems on the day-ahead and intraday market. Sequential bidding in a day-ahead and a balancing market is considered in, e.g., Boomsma et al (2014); Kumbartzky et al (2017); Kongelf et al (2019); Mazzi et al (2019). Most authors restrict themselves to a small number of markets.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimal bidding functions for renewable energies in sequential electricity markets

Finnah

2021

OR Spectrum

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In most modern energy markets, electricity is traded in pay-as-clear auctions. Usually, multiple sequential markets with daily auctions, in which each hourly product is traded separately, coexist. In each market and for each traded hour, each power producer and consumer submits multiple price and volume combinations, called bids. After all bids are submitted by the market participants, the market-clearing price for each hour is published, and the market participants must fulfill their accepted commitments. The corresponding decision problem is particularly difficult to solve for market participants with stochastic supply or demand. We formulate the energy trading problem as a dynamic program and derive the optimal bidding functions analytically via backward recursion. We demonstrate that, for each hour and market, the optimal bidding function is completely defined by two bids. While we focus on power producers with stochastic supply (e.g., wind or solar), our model is applicable to power consumers with stochastic demand, as well. The optimal policy is applicable in most liberalized energy markets, virtually independent of the structure of the underlying electricity price process.

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Portfolio size’s effects on gains from coordinated bidding in electricity markets

Cited by 13 publications

References 26 publications

Backtesting coordinated hydropower bidding using neural network forecasting

Backtesting coordinated hydropower bidding using neural network forecasting

The Value of Coordination in Multimarket Bidding of Grid Energy Storage

Optimal bidding functions for renewable energies in sequential electricity markets

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