Optimization of bidding strategy in the day-ahead market by consideration of seasonality trend of the market spot price

Sedeh, Omid Motamedi; Ostadi, Bakhtiar

doi:10.1016/j.enpol.2020.111740

Cited by 14 publications

(4 citation statements)

References 20 publications

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“…The data is collected from the New South Wales electricity market dataset. Electricity prices from 01/01/2018 to 31/12/2019 [26]. Every 30 minutes, the dataset on electricity prices in New South Wales is collected; there are 48 collections made daily.…”

Section: A Data Acquisitionmentioning

confidence: 99%

Optimization Research on Provincial Users' Participation in Regional Electricity Spot Market Bidding Based on Hierarchically Gated Recurrent Neural Network

Jinqing Luo

2024

jes

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The creation of strategic pricing models is crucial in the highly competitive centralized energy markets in order to maximize revenues. In order to achieve maximum profit, market participants must act intelligently while bidding on electrical energy purchases or sales. Notwithstanding the many restructuring changes of the Chinese electricity sector, market participants are nonetheless exposed to risks associated with volatility in market prices and uncertainty over demand behavior. The data are collected from New South Wales electricity market. Data-adaptive Gaussian average filtering (DAGAF) is used to pre-process the data during the pre-processing step. Hierarchically Gated Recurrent Neural Networks (HGRNNs) are successfully used to classify both long-term and spot transactions. The neural network's weight parameter is optimized by the Lotus Effect Optimization Algorithm (LEA), which enhances the HGRNN. The RESMB-HGRNN-LEA proposed is implemented on the Python working platform. To calculate the suggested approach, performance measures including accuracy, precision, sensitivity, computation time, and recall were looked at.In comparison to the existing technique, the proposed RESMB-HGRNN-LEA method yields better results in terms of accuracy (16.65% and 18.85%), sensitivity (16.34% and 12.23%), precision (14.89% and 16.89%), and computing time (82.37% and 94.47%).

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Section: A Data Acquisitionmentioning

confidence: 99%

Optimization Research on Provincial Users' Participation in Regional Electricity Spot Market Bidding Based on Hierarchically Gated Recurrent Neural Network

Jinqing Luo

2024

jes

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“…Various models and algorithms have been proposed to analyse bidding strategies in the literature. Traditionally, these approaches can be categorised into four main groups: optimisation models, game theory-based models, agent-based models, and hybrid models [4].…”

Section: Introductionmentioning

confidence: 99%

Optimal Battery Energy Storage Dispatch for the Day-Ahead Electricity Market

Gonzalez-Saenz,

Becerra

2024

Preprint

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This work presents an innovative application of optimal control theory to the strategic scheduling of battery storage in the day-ahead electricity market, with a focus on enhancing profitability while factoring in battery degradation. The study incorporates in the optimisation framework the cost of battery capacity degradation, the effects of capacity degradation and internal resistance degradation into dynamics. We employ a continuous-time representation of the dynamics, in contrast with many other studies that use a discrete-time approximation with rather coarse intervals. We adopt an equivalent circuit model coupled with empirical degradation parameters to simulate a battery cell’s behaviour and degradation mechanisms with good support from experimental data. Utilising direct collocation methods with mesh refinement allows for precise numerical solutions to the complex, nonlinear dynamics involved. Through a detailed case study of Belgium’s day-ahead electricity market, we determine the optimal charging and discharging schedules under varying objectives: maximizing net revenues, profits considering capacity degradation, and profits considering both capacity degradation and internal resistance increase due to degradation. The results demonstrate the viability of our approach and underscore the significance of integrating degradation costs into the market strategy for battery operators, alongside its effects on the battery’s dynamic behaviour. Our methodology extends previous work by offering a more comprehensive model that empirically captures the intricacies of battery degradation, including a fine and adaptive time domain representation, focusing on the day-ahead market, and utilising accurate direct methods for optimal control. The paper concludes with insights into the potential of optimal control applications in energy markets and suggestions for future research avenues.

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“…However, as the time gets closer to the point of actual participation, market prices become less favorable to the generator, and amendments to commitments come at a cost. Accurate forecasts [3] combined with strategic planning [7] can greatly increase the profitability of an energy producer.…”

Section: Background and Motivationmentioning

confidence: 99%

“…Therefore constraints (7) and (8) ensure that the amount of energy the WF produces, 𝑃𝑃 𝑒𝑒,𝑜𝑜 𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤 , is both less than total AWE, 𝑃𝑃 � 𝑒𝑒,𝑜𝑜 𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤 , and the rated power of the farm, 𝑃𝑃 � 𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤 .…”

Section: Constraintsmentioning

confidence: 99%

Decision making under uncertainty for variable resource generators participating in energy markets

Al Lawati

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Your constant support, encouragement, and guidance have been invaluable throughout this journey, and I am indebted to your unwavering dedication to my success. I also extend a special thanks to my committee members, Dr. Kayse Lee Maass and Dr. Xiaoning "Sarah" Jin, Associate Professors, Mechanical and Industrial Engineering. Your thoughtful feedback, insightful comments, and critical analysis have greatly shaped this work and helped me to grow as a researcher. I would also like to acknowledge my colleagues at the Decisions Analytics lab, Dr. Tasnim Ibn Faiz, Dr. Jose Luis Crespo, and Victoria Okra. Your technical support and assistance in talking through the models were essential to the success of this project. I am grateful for your knowledge, expertise, and friendship. I am also indebted to Dr. Alison Miller, Dr. Melinda Valente, and all my friends at The Academic Writers Space for creating a welcoming and supportive environment full of understanding, encouragement, and laughs. Your kindness and generosity have been a source of inspiration throughout this journey. And last but never least, my family, for always being there, no matter what.

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Optimization of bidding strategy in the day-ahead market by consideration of seasonality trend of the market spot price

Cited by 14 publications

References 20 publications

Optimization Research on Provincial Users' Participation in Regional Electricity Spot Market Bidding Based on Hierarchically Gated Recurrent Neural Network

Optimization Research on Provincial Users' Participation in Regional Electricity Spot Market Bidding Based on Hierarchically Gated Recurrent Neural Network

Optimal Battery Energy Storage Dispatch for the Day-Ahead Electricity Market

Decision making under uncertainty for variable resource generators participating in energy markets

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