Optimizing Hybrid Power Plant Profits through Feature-Driven Trading of Wind Power and Hydrogen

Konsep Inti

This paper develops a feature-driven model that enables hybrid power plants comprising wind turbines and electrolyzers to make optimal wind power and hydrogen trading decisions in the day-ahead stage, while also proposing a real-time adjustment strategy to settle imbalances. The proposed approach outperforms the deterministic model and achieves near-optimal profits compared to a benchmark with perfect information.

Abstrak

The paper presents a feature-driven trading strategy for hybrid power plants that produce both wind power and hydrogen. The key highlights are:

The authors propose a novel application of a prescriptive analytics framework based on decision rules to the multi-market bidding problem of hybrid power plants. This data-driven approach exploits contextual information, such as historical wind power forecasts, to directly map them to optimal trading decisions without the need for complex stochastic optimization.

The paper investigates various model architectures, including price-dependent and time-dependent policies, as well as different feature vectors that incorporate additional information like aggregated wind power forecasts in the region.

A real-time adjustment strategy is developed to account for deviations between the day-ahead schedule and realized wind power production, while ensuring the minimum daily hydrogen production requirement is met.

Numerical results show that the final profit obtained from the proposed feature-driven trading mechanism in the day-ahead stage, combined with the real-time adjustment strategy, is very close to that in an ideal benchmark with perfect information, outperforming the deterministic model.

The authors analyze the trade-offs between model complexity, training data requirements, and performance, providing insights on the appropriate choice of model architecture and feature vectors depending on the available historical data.

Statistik

The realized wind power generation P^W_t is used to determine the power imbalance that needs to be settled in real time.
The day-ahead price λ^DA_t, upward regulation price λ^UP_t, and downward regulation price λ^DW_t are used in the objective function to calculate the revenues and costs.
The minimum daily hydrogen production requirement H is enforced as a constraint.

Kutipan

"This is the first paper that develops such a model for hybrid power plants trading both wind power and hydrogen, which is a more complicated problem."
"Using an out-of-sample simulation, we show how the resulting profit from feature-driven trading in the day-ahead stage and the adjustment strategy in real time is very close to that in an ideal benchmark (oracle)."

Wawasan Utama Disaring Dari

Feature-Driven Strategies for Trading Wind Power and Hydrogen

by Emil Helgren... pada arxiv.org 04-01-2024

https://arxiv.org/pdf/2310.01385.pdf

Pertanyaan yang Lebih Dalam

How can the proposed feature-driven trading strategy be extended to incorporate other assets like batteries or hydrogen storage to further improve the overall profitability of the hybrid power plant?

The proposed feature-driven trading strategy can be extended to incorporate other assets like batteries or hydrogen storage by including additional relevant features in the feature vectors. For batteries, features related to the state of charge, charging and discharging rates, and efficiency can be included. Similarly, for hydrogen storage, features such as storage capacity, fill level, and conversion efficiency can be incorporated. By integrating these features into the model, the trading decisions can be optimized to account for the presence and characteristics of these additional assets. This extension would enable the hybrid power plant to make more informed decisions regarding the utilization of batteries or hydrogen storage in conjunction with wind power generation and hydrogen production.

What are the potential challenges in implementing this approach in practice, and how can they be addressed, such as dealing with non-stationary environments or inaccurate balancing price forecasts?

Implementing the feature-driven trading strategy in practice may face challenges related to non-stationary environments and inaccurate balancing price forecasts. Non-stationarity in the environment, such as changing market conditions or weather patterns, can impact the performance of the model trained on historical data. To address this challenge, the model can be regularly retrained using updated data to adapt to the evolving environment. By incorporating a sliding window approach or online learning techniques, the model can continuously learn from new data and adjust its trading strategies accordingly.
Dealing with inaccurate balancing price forecasts poses another challenge, as these forecasts are crucial for real-time adjustment decisions. One approach to mitigate this challenge is to develop robust optimization models that can handle uncertainty in price forecasts. By incorporating scenario-based optimization or robust decision-making techniques, the model can make more resilient trading decisions in the face of inaccurate forecasts. Additionally, improving the forecasting accuracy through advanced prediction models or data fusion techniques can help enhance the reliability of the balancing price forecasts and, consequently, the performance of the trading strategy.

Can the feature-driven modeling approach be applied to other energy systems beyond hybrid power plants, such as virtual power plants or multi-asset energy hubs, to optimize their trading decisions under uncertainty?

Yes, the feature-driven modeling approach can be applied to other energy systems beyond hybrid power plants, such as virtual power plants or multi-asset energy hubs, to optimize their trading decisions under uncertainty. Virtual power plants (VPPs) and multi-asset energy hubs often involve a diverse mix of energy resources, including renewable generation, storage systems, and demand response capabilities. By leveraging contextual information and historical data, feature-driven models can be developed to make optimal trading decisions for these complex energy systems.
In the case of VPPs, the feature-driven approach can incorporate features related to the generation profiles of different renewable sources, storage capacities, demand response capabilities, and market prices. By training the model on historical data and contextual information, the VPP can optimize its trading strategies to maximize profitability and grid reliability.
Similarly, for multi-asset energy hubs, the feature-driven modeling approach can consider features specific to each asset within the hub, such as generation capacities, storage constraints, and interconnections. By developing tailored feature vectors and training the model on relevant data, the energy hub can make informed decisions on energy trading, storage utilization, and demand response actions to achieve optimal operation under uncertainty.