[Paper Review] Online Offering Strategies for Storage-Assisted Renewable Power Producer in Hour-Ahead Market

Lin Yang, Mohammad Hajiesmaili+2 more|arXiv (Cornell University)|Dec 1, 2016

Smart Grid Energy Management10 references3 citations

TL;DR

This paper proposes online offering strategies—sOffer, mOffer, and gOffer—for storage-assisted renewable power producers participating in hour-ahead electricity markets. By leveraging competitive online algorithm design, the strategies maximize profit under uncertain renewable output and dynamic clearing prices, achieving near-optimal performance with competitive ratios of O(log θ) and robustness to forecasting errors and price volatility.

ABSTRACT

A promising approach to hedge against the inherent uncertainty of renewable generation is to equip the renewable plants with energy storage systems. This paper focuses on designing profit maximization offering strategies, i.e., the strategies that determine the offering price and volume, for a storage-assisted renewable power producer that participates in hour-ahead electricity market. Designing the strategies is challenging since (i) the underlying problem is coupled across time due to the evolution of the storage level, and (ii) inputs to the problem including the renewable output and market clearing price are unknown when submitting offers. Following the competitive online algorithm design approach, we first study a basic setting where the renewable output and the clearing price are known for the next hour. We propose sOffer, a simple online offering strategy that achieves the best possible competitive ratio of O(log θ), where $θ$ is the ratio between the maximum and the minimum clearing prices. Then, we consider the case where the clearing price is unknown. By exploiting the idea of submitting multiple offers to combat price uncertainty, we propose mOffer, and demonstrate that the competitive ratio of mOffer converges to that of sOffer as the number of offers grows. Finally, we extend our approach to the scenario where the renewable output has forecasting error. We propose gOffer as the generalized offering strategy and characterize its competitive ratio as a function of the forecasting error. Our trace-driven experiments demonstrate that our algorithms achieve performance close to the offline optimal and outperform a baseline alternative significantly.

Motivation & Objective

• Address the challenge of profit maximization for renewable power producers with energy storage in dynamic hour-ahead electricity markets.
• Design online offering strategies that operate without full knowledge of future renewable generation or market clearing prices.
• Account for temporal coupling due to storage dynamics and the need to balance energy storage across time.
• Develop strategies that are robust to price volatility and forecasting errors in renewable output.
• Achieve competitive performance guarantees (competitive ratios) under realistic market conditions with incomplete information.

Proposed method

• Propose sOffer for a basic setting where next-hour renewable output and clearing price are known, achieving a competitive ratio of O(log θ), where θ is the ratio of maximum to minimum clearing prices.
• Introduce mOffer to handle unknown clearing prices by submitting multiple offers, reducing price uncertainty impact and converging to sOffer's performance as the number of offers increases.
• Develop gOffer as a generalized strategy for scenarios with forecasting errors in renewable output, characterizing its competitive ratio as a function of error magnitude.
• Use competitive online algorithm design to derive theoretical performance bounds (competitive ratios) for all proposed strategies.
• Apply trace-driven experiments using real market data to evaluate performance against offline optimal and baseline methods.
• Leverage storage to shift energy from low-price to high-price periods, enhancing profit beyond simple commitment fulfillment.

Experimental results

Research questions

• RQ1 What is the best achievable competitive ratio for an online offering strategy in a storage-assisted renewable producer setting with known future prices?
• RQ2 How can multiple offers be used to mitigate uncertainty in market clearing prices when they are not known in advance?
• RQ3 How does forecasting error in renewable output affect the performance of online offering strategies, and what strategies can maintain robustness?
• RQ4 How does storage capacity influence the profit and competitive ratio of online offering strategies?
• RQ5 To what extent can online strategies achieve performance close to the offline optimal in real-world electricity market traces?

Key findings

• sOffer achieves a competitive ratio of O(log θ), where θ is the ratio of maximum to minimum clearing prices, representing the best possible performance under known future prices.
• mOffer’s performance converges to that of sOffer as the number of submitted offers increases, effectively mitigating uncertainty in unknown clearing prices.
• gOffer demonstrates robustness to renewable forecasting errors below 20%, with profit decreasing rapidly when errors exceed this threshold.
• The empirical competitive ratio of gOffer remains close to 1.0 (near-optimal) for storage capacities up to 2x the renewable capacity, indicating strong performance in realistic settings.
• FixedOnline, a baseline strategy, shows an empirical competitive ratio 90% higher than gOffer, highlighting gOffer’s superiority under price volatility.
• As storage capacity increases, gOffer’s profit increases by only 3%, while FixedOnline’s profit increases by 90%, indicating gOffer’s efficiency in utilizing storage information.