Enhancing Participation in Electricity Markets through Conformal Prediction for Stochastic Decision-Making of Photovoltaic Power

Core Concepts

Conformal prediction can be effectively combined with various bidding strategies to enhance the reliability and profitability of photovoltaic power participation in electricity markets.

Abstract

This paper proposes a framework that leverages conformal prediction (CP), an emerging probabilistic forecasting method, to enhance decision-making for photovoltaic (PV) power participation in electricity markets.
The framework consists of three main steps:

Point predictions of PV power are generated using machine learning models such as simple linear regression, multiple linear regression, and random forest regression.
Uncertainty of the point predictions is quantified using various CP methods, including basic CP, CP with k-nearest neighbors, CP with Mondrian binning, and conformal predictive systems (CPS).
The quantified uncertainty is then used as input for different bidding strategies, including trust-the-forecast, worst-case, Newsvendor, and expected utility maximization (EUM), to determine the optimal quantity bids for the day-ahead electricity market.

The performance of the framework is evaluated using actual weather and energy market data from the Netherlands. The results show that:

CP methods, especially CPS with k-nearest neighbors and Mondrian binning, outperform linear quantile regression in terms of forecasting performance.
Combining CP methods with certain bidding strategies, such as EUM with conditional value at risk, can yield high profit with minimal energy imbalance.
The best-performing approach is using CPS with k-nearest neighbors and Mondrian binning after random forest regression, which achieves up to 93% of the potential profit with minimal imbalance.
The proposed framework can help market participants increase their profit while being aware of the associated risk, and grid operators improve their insights into the expected grid loading.

Stats

The day-ahead market prices for the Netherlands in the considered time period were extracted from the ENTSO-E Transparency Platform.
The up- and down-regulation prices in the real-time market were obtained from TenneT, the Dutch TSO.

Quotes

"Conformal prediction (CP) is an emerging distribution-free and model-agnostic probabilistic forecasting method that offers a measure of confidence by transforming point predictions into prediction intervals."
"Combining CP in combination with certain bidding strategies can yield high profit with minimal energy imbalance."
"Using conformal predictive systems with k-nearest neighbors and Mondrian binning after random forest regression yields the best profit and imbalance regardless of the decision-making strategy."

Key Insights Distilled From

Conformal Prediction for Stochastic Decision-Making of PV Power in Electricity Markets

by Yvet Renkema... at arxiv.org 04-01-2024

https://arxiv.org/pdf/2403.20149.pdf

Deeper Inquiries

How can the proposed framework be extended to include intraday market participation and price bidding strategies

To extend the proposed framework to include intraday market participation and price bidding strategies, several key steps can be taken. Firstly, incorporating intraday market participation would involve generating additional forecasts for shorter time intervals within the day, allowing for more dynamic decision-making. This could be achieved by adjusting the existing models to provide forecasts at shorter time horizons, such as hourly or sub-hourly intervals.
In terms of price bidding strategies, the framework could be expanded to include the optimization of price bids alongside quantity bids. This would involve developing models that can predict not only the quantity of PV power to be supplied but also the optimal price at which to offer it in the market. By integrating price forecasting models into the framework, market participants can make more informed decisions based on both quantity and price predictions.
Furthermore, the inclusion of intraday market participation and price bidding strategies would require the development of new decision-making algorithms that take into account the additional variables and uncertainties associated with pricing dynamics. These algorithms could optimize bids considering both quantity and price, taking into account market conditions, demand fluctuations, and pricing trends throughout the day.

What are the potential challenges and limitations of applying conformal prediction in real-world electricity market operations, and how can they be addressed

Applying conformal prediction in real-world electricity market operations may pose several challenges and limitations that need to be addressed for successful implementation. One key challenge is the computational complexity of conformal prediction methods, especially when dealing with large datasets and complex models. This can lead to increased processing times and resource requirements, which may not be feasible in real-time market scenarios. To address this, optimization techniques and parallel processing methods can be employed to enhance the efficiency of the conformal prediction algorithms.
Another limitation is the assumption of perfect information in the proposed framework, which may not hold in real-world scenarios where market conditions are constantly changing and uncertain. To mitigate this, the framework can be enhanced to incorporate adaptive learning mechanisms that continuously update models based on new data and market feedback. This would improve the robustness and adaptability of the framework to changing market conditions.
Additionally, the interpretability of conformal prediction intervals and their impact on decision-making in electricity markets need to be carefully considered. Ensuring that market participants understand the uncertainty quantification provided by conformal prediction methods is crucial for effective decision-making. This can be addressed through clear visualization techniques and user-friendly interfaces that communicate the implications of uncertainty in a comprehensible manner.

How can the insights from this study on the benefits of uncertainty quantification be applied to improve decision-making in other energy-related domains, such as energy storage or demand-side management

The insights gained from the study on the benefits of uncertainty quantification in electricity market decision-making can be applied to improve decision-making in other energy-related domains, such as energy storage and demand-side management. By incorporating uncertainty quantification methods like conformal prediction, energy storage operators can optimize their storage strategies based on probabilistic forecasts of energy supply and demand. This can help in maximizing the utilization of energy storage systems while minimizing operational risks.
In demand-side management, the use of uncertainty quantification techniques can enhance the prediction of energy consumption patterns and facilitate more efficient demand response strategies. By providing probabilistic forecasts of energy demand, demand-side management systems can optimize load shedding or shifting actions to reduce costs and improve grid stability. This can lead to more effective demand-side management programs that align with market conditions and operational constraints.
Overall, the application of uncertainty quantification methods in energy storage and demand-side management can improve the resilience, efficiency, and cost-effectiveness of energy systems by enabling stakeholders to make informed decisions based on a comprehensive understanding of uncertainty and risk factors.