Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Energy Storage Arbitrage Under Price Uncertainty: Market Risks and Opportunities

Yiqian Wu, Bolun Xu, James Anderson

TL;DR

The paper addresses profitability and risk of energy storage arbitrage under price uncertainty in electricity markets. It introduces a computational framework that combines robust optimization and chance-constrained approaches, implemented across polyhedral, ellipsoidal, and probabilistic uncertainty representations to form efficient frontiers. Key findings show that robust strategies outperform chance-constrained ones in risk management, particularly under high volatility, while achieving similar expected profits; polyhedral and ellipsoidal models that capture price correlations broaden the frontier, though performance depends on data and representation. The work provides a practical decision-support tool for risk-aware storage participants to adapt arbitrage strategies to market conditions using data-driven frontiers.

Abstract

We investigate the profitability and risk of energy storage arbitrage in electricity markets under price uncertainty, exploring both robust and chance-constrained optimization approaches. We analyze various uncertainty representations, including polyhedral, ellipsoidal uncertainty sets and probabilistic approximations, to model price fluctuations and construct efficient frontiers that highlight the tradeoff between risk and profit. Using historical electricity price data, we quantify the impact of uncertainty on arbitrage strategies and compare their performance under distinct market conditions. The results reveal that arbitrage strategies under uncertainties can effectively secure expected profits, and robust strategies perform better in risk management across varying levels of conservativeness, especially under highly volatile market conditions. This work provides insights into storage arbitrage strategy selection for market participants with differing risk preferences, emphasizing the adaptability of efficient frontiers to the electricity market.

Energy Storage Arbitrage Under Price Uncertainty: Market Risks and Opportunities

TL;DR

The paper addresses profitability and risk of energy storage arbitrage under price uncertainty in electricity markets. It introduces a computational framework that combines robust optimization and chance-constrained approaches, implemented across polyhedral, ellipsoidal, and probabilistic uncertainty representations to form efficient frontiers. Key findings show that robust strategies outperform chance-constrained ones in risk management, particularly under high volatility, while achieving similar expected profits; polyhedral and ellipsoidal models that capture price correlations broaden the frontier, though performance depends on data and representation. The work provides a practical decision-support tool for risk-aware storage participants to adapt arbitrage strategies to market conditions using data-driven frontiers.

Abstract

We investigate the profitability and risk of energy storage arbitrage in electricity markets under price uncertainty, exploring both robust and chance-constrained optimization approaches. We analyze various uncertainty representations, including polyhedral, ellipsoidal uncertainty sets and probabilistic approximations, to model price fluctuations and construct efficient frontiers that highlight the tradeoff between risk and profit. Using historical electricity price data, we quantify the impact of uncertainty on arbitrage strategies and compare their performance under distinct market conditions. The results reveal that arbitrage strategies under uncertainties can effectively secure expected profits, and robust strategies perform better in risk management across varying levels of conservativeness, especially under highly volatile market conditions. This work provides insights into storage arbitrage strategy selection for market participants with differing risk preferences, emphasizing the adaptability of efficient frontiers to the electricity market.
Paper Structure (17 sections, 7 equations, 3 figures, 1 table)

This paper contains 17 sections, 7 equations, 3 figures, 1 table.

Table of Contents

  1. Introduction
  2. Problem Formulation
  3. Energy Storage Arbitrage
  4. Robust Decision Making
  5. Chance-Constrained Decision Making
  6. Uncertainty Representation
  7. Polyhedral uncertainty set
  8. Ellipsoidal uncertainty set
  9. Probability distribution approximation
  10. Risk-Profit Analysis and Strategy Selection
  11. CASE STUDIES
  12. Data Description
  13. Arbitrage under Uncertainty
  14. Risk-Profit Analysis given Different Market Conditions
  15. Risk-Profit Analysis given Different Representations
  16. ...and 2 more sections

Figures (3)

  • Figure 1: NYC LMP from 2014 to 2023 (extreme outliers omitted).
  • Figure 2: Efficient frontiers for different arbitrage strategies under uncertainty given variant market conditions.
  • Figure 3: Efficient frontiers tested with prices from year 2022 for strategies characterized by datasets from year $n$ to 2021.