A Value-Oriented Price Forecasting Approach to Optimize Trading of Renewable Generation

Stratigakos, Akylas; Michiorri, Andrea

doi:10.1109/powertech46648.2021.9494832

Cited by 5 publications

(4 citation statements)

References 20 publications

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“…However, this approach does not directly leverage the optimization component. Our previous work [32] suggests employing a risk-averse trading strategy as an alternative loss function to forecast market quantities, prior to deriving trading decisions, which still does not reduce the modeling effort and cannot handle multiple uncertainties. In [33], the framework put forward in [16] is extended by proposing linear decision rules to improve both the forecasting and trading performance of a WPP participating in a DA market.…”

Section: B Literature Reviewmentioning

confidence: 99%

“…Reduced modeling effort [28], [30], [31], [37] * --[29] * , † † - [33], [34] † -This work * , † † case studies of increasing complexity related to renewable trading. First, we examine trading in a DA market under different pricing mechanisms and propose strategies that balance trading cost and predictive accuracy.…”

Section: Multiple Types Of Uncertaintymentioning

confidence: 99%

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Prescriptive Trees for Integrated Forecasting and Optimization Applied in Trading of Renewable Energy

Stratigakos

Camal

Michiorri

et al. 2022

IEEE Trans. Power Syst.

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Deriving decisions from data typically involves a sequential process with two components, forecasting and optimization. Forecasting models learn by minimizing a loss function that stands as a proxy for task-specific costs (e.g., trading, scheduling) without considering the downstream optimization, which in practice creates a performance bottleneck and obscures the impact of data on decisions. This work proposes a single data-driven module that leverages the structure of the optimization component and directly learns a policy conditioned on explanatory data. For this purpose, we describe an algorithm to train ensembles of decision trees by directly minimizing taskspecific costs, and prescribe decisions via a weighted Sample Average Approximation of the original problem. We then develop a generic framework to assess the impact of explanatory data on prescriptive performance. To illustrate the efficacy of the proposed modeling approach, we consider two case studies related to trading renewable energy. First, we examine trading in a day-ahead market and propose strategies that balance optimal trading decisions and predictive accuracy. Next, we append a storage device and co-optimize the day-ahead offers and the operational policy, based on a tractable approximation using the linear decision rule approach. The empirical results demonstrate improved prescriptive performance compared to solutions derived under the standard stochastic optimization framework. Further, we provide valuable insights on how explanatory data impact optimization performance and how this impact evolves under different market designs.

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Section: B Literature Reviewmentioning

confidence: 99%

Section: Multiple Types Of Uncertaintymentioning

confidence: 99%

Prescriptive Trees for Integrated Forecasting and Optimization Applied in Trading of Renewable Energy

Stratigakos

Camal

Michiorri

et al. 2022

IEEE Trans. Power Syst.

Self Cite

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show abstract

“…Choices about innovation use are frequently accepted on the fly, without regard to precision or audio techniques [ 7 ]. Additionally, because the findings of technological choice methods in the research are only relevant for a limited time, these must be repeated as knowledge progresses [ 8 , 9 ]. As a result, to generate the best choice depending on its atmosphere's features, a recyclable fully evolved, and extensible judgment strategy is required [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

Built-In Calibration Standard and Decision Support System for Controlling Structured Data Storage Systems Using Soft Computing Techniques

Chandran

Prabha

Malathi³

et al. 2022

Computational Intelligence and Neuroscience

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Several research that assesses, or assess computer systems has been undertaken in previous decades. Choosing an appropriate DBMS system in a computer application, though, was never completely arbitrary, based on the professional study. Developing a viable answer for such a challenge depending on business goals and needs from judgment necessitates a thorough study on information access as well as comprehensive professional evaluation. The research presents a DSS to help non-kinetics discover their proper DBMS solutions and otherwise information retention types faster. This suggested DSS is unique in that it uses MoSCoW to evaluate criterion weighting or deliberate, as well as assessment frameworks for quantifying overall levels of quasi criterion and ISO/IEC qualitative features to show the link between criterion based upon industry specialists’ expertise. Companies that produce programs have difficulty integrating new innovations, such as Internet computing or information management platforms, into their operations. Because computer engineers and top programmers are usually specialists in their field, users need to consult or train other professionals. Therefore, computer development is an appropriate area for implementing judgment support technologies that can proactively help this prediction to choose the best technologies for their products. We offer a decision support system (DSS) to assist in the selection of the best appropriate information architecture. According to both example reports and specialists, this technique improves visibility into the choice method gives a deeper prioritized choice range than if customers have conducted their study individually, or saves the duration and expense of the judicial procedure.

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“…It takes a variety of forecasting and decision-making methods to integrate RES in the power markets. To come at the best options, the conventional method entails estimating power output and market volumes, followed by including the findings into an optimization problem (Stratigakos et al, 2021).…”

mentioning

confidence: 99%

Improving the Strategies of the Market Players Using an Ai-Powered Price Forecast for Electricity Market

Bâra,

Oprea,

Ciurea

2023

Technological and Economic Development of Economy

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This paper analyses the recent evolution of the electricity price of one of the EastEuropean countries’ Balancing Markets (BM) – Romania, aiming to understand the prices trend and predict them in the current economic and geopolitical context. This is especially important as the electricity producers have to allocate their output between wholesale electricity market, ancillary services markets and BM targeting to maximize value and achieve a sustainable economic development. Therefore, in this paper, we propose an AI-powered electricity price forecast using several types of standout Machine Learning (ML) algorithms such as classifiers and regressors to predict the electricity price on BM. This approach, consisting of two steps, identifies the imbalance sign and significantly enhances the performance of the price forecast. The proposed method offers valuable insights into the market participants’ trading opportunities using two prediction solutions. The first prediction solution consists of averaging the results of five ensemble ML algorithms. The second one consists in weighting the results of the five forecasting ML algorithms using either a linear regression or a decision tree algorithm. Thus, we propose to combine supervised and unsupervised ML algorithms and find the fundamentals for creating optimal bidding strategies for electricity market players.

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A Value-Oriented Price Forecasting Approach to Optimize Trading of Renewable Generation

Cited by 5 publications

References 20 publications

Prescriptive Trees for Integrated Forecasting and Optimization Applied in Trading of Renewable Energy

Prescriptive Trees for Integrated Forecasting and Optimization Applied in Trading of Renewable Energy

Built-In Calibration Standard and Decision Support System for Controlling Structured Data Storage Systems Using Soft Computing Techniques

Improving the Strategies of the Market Players Using an Ai-Powered Price Forecast for Electricity Market

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