Impact of renewable energy forecast imperfections on market-clearing outcomes

Ordoudis, Christos

doi:10.1109/energycon.2016.7513962

Cited by 5 publications

(2 citation statements)

References 10 publications

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“…The initial wind power scenarios are normalized and then multiplied by the wind farm capacity; thus a higher penetration level results in scenarios with higher mean and variance. We refer the reader to (Ordoudis & Pinson, 2016) for further discussion on this issue.…”

Section: Comparison Of Market-clearing Models and The Effect Of Coord...mentioning

confidence: 99%

An Integrated Market for Electricity and Natural Gas Systems with Stochastic Power Producers

Ordoudis¹,

Pinson²,

Morales

2019

European Journal of Operational Research

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137

118

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In energy systems with high shares of weather-driven renewable power sources, gas-fired power plants can serve as a back-up technology to ensure security of supply and provide short-term flexibility. Therefore, a tighter coordination between electricity and natural gas networks is foreseen.In this work, we examine different levels of coordination in terms of system integration and time coupling of trading floors. We propose an integrated operational model for electricity and natural gas systems under uncertain power supply by applying two-stage stochastic programming. This formulation co-optimizes day-ahead and real-time dispatch of both energy systems and aims at minimizing the total expected cost. Additionally, two deterministic models, one of an integrated energy system and one that treats the two systems independently, are presented. We utilize a formulation that considers the linepack of the natural gas system, while it results in a tractable mixed-integer linear programming (MILP) model. Our analysis demonstrates the effectiveness of the proposed model in accommodating high shares of renewables and the importance of proper natural gas system modeling in short-term operations to reveal valuable flexibility of the natural gas system. Moreover, we identify the coordination parameters between the two markets and show their impact on the system's operation and dispatch.

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Section: Comparison Of Market-clearing Models and The Effect Of Coord...mentioning

confidence: 99%

An Integrated Market for Electricity and Natural Gas Systems with Stochastic Power Producers

Ordoudis¹,

Pinson²,

Morales

2019

European Journal of Operational Research

Self Cite

137

118

View full text Add to dashboard Cite

show abstract

“…The importance of characterizing the whole distribution becomes even more relevant in applications with asymmetric costs such as those found in power systems applications [23]. For instance, load-shed costs are in general much higher than the cost of spilling renewable energy; the price and quantity risk due to the need of purchasing in high spot prices when RG falls short of meeting contract amounts is, in general, higher than the risk of clearing RG surplus in low spot prices [10]; just to mention a few.…”

Section: Literature Review On Wind-power Forecastmentioning

confidence: 99%

A Multi-Quantile Regression Time Series Model with Interquantile Lipschitz Regularization for Wind Power Probabilistic Forecasting

Ruas¹,

Street²,

Fernandes³

2020

Preprint

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Producing probabilistic forecasts for renewable generation (RG) has become an important topic in power systems applications. This is due to the significant growth of RG participation in power systems worldwide. Additionally, it is well-known that decision making under uncertainty generally benefits from stochastic aware models, specially those relying on non symmetric costs and risk-averse assessments. Therefore, objective of this work is to propose an adaptive non-parametric time-series model driven by a regularized multiple-quantile-regression (MQR) framework. The goal is to derive a dynamic model for the conditional distribution function (CDF) describing renewable generation time series. To accomplish that, the quantile space is discretized within a userdefined granularity and an interpolation method is used to derive the full predictive CDF. Instead of estimating each quantile model separately, all models are jointly estimated through a single linear optimization problem. Thus, the estimation process converges to the global optimal parameters in polynomial time. Parsimony is imposed to the coefficient estimates across quantiles and covariates. An innovative feature of our work is the consideration of a penalization term based on a Lipschitz regularization of the first derivative of coefficients in the quantile space. The proposed regularization imposes a smooth coupling effect among quantiles creating a single non-parametric CDF model with improved out-of-sample performance. A case study with realistic wind-power generation data from the Brazilian system shows: 1) the regularization model is capable to improve the performance of MQR probabilistic forecasts, and 2) our MQR model outperforms five relevant benchmarks: two based on the MQR framework, and three based on parametric models, namely, SARIMA, and GAS with Beta and Weibull CDF.

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Timescale classification in wind forecasting: A review of the state‐of‐the‐art

Roungkvist

Enevoldsen

2020

Journal of Forecasting

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The intermittency of the wind has been reported to present significant challenges to power and grid systems, which intensifies with increasing penetration levels. Accurate wind forecasting can mitigate these challenges and help in integrating more wind power into the grid. A range of studies have presented algorithms to forecast the wind in terms of wind speeds and wind power generation across different timescales. However, the classification of timescales varies significantly across the different studies (2010–2014). The timescale is important in specifying which methodology to use when, as well in uniting future research, data requirements, etc. This study proposes a generic statement on how to classify the timescales, and further presents different applications of these forecasts across the entire wind power value chain.

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Impact of renewable energy forecast imperfections on market-clearing outcomes

Cited by 5 publications

References 10 publications

An Integrated Market for Electricity and Natural Gas Systems with Stochastic Power Producers

An Integrated Market for Electricity and Natural Gas Systems with Stochastic Power Producers

A Multi-Quantile Regression Time Series Model with Interquantile Lipschitz Regularization for Wind Power Probabilistic Forecasting

Timescale classification in wind forecasting: A review of the state‐of‐the‐art

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