Battery Operations in Electricity Markets: Strategic Behavior and Distortions

TL;DR

This paper models how decentralized batteries in electricity markets behave strategically, causing distortions in operation and increasing system costs, with bounded inefficiency and insights on market power mitigation.

Contribution

It provides an analytically tractable model comparing centralized and decentralized battery operations, quantifies market power effects, and evaluates mitigation mechanisms.

Findings

Decentralized batteries distort discharge decisions in three ways.

The Price of Anarchy is bounded between 9/8 and 4/3.

Market power mitigation mechanisms can backfire, increasing total costs.

Abstract

Electric power systems are undergoing a major transformation as they integrate intermittent renewable energy sources, and batteries to smooth out variations in renewable energy production. As privately-owned batteries grow from their role as marginal "price-takers" to significant players in the market, a natural question arises: How do batteries operate in electricity markets, and how does the strategic behavior of decentralized batteries distort decisions compared to centralized batteries? We propose an analytically tractable model that captures salient features of the highly complex electricity market. We derive in closed form the resulting battery behavior and generation cost in three operating regimes: (i) no battery, (ii) centralized battery, and (ii) decentralized profit-maximizing battery. We establish that a decentralized battery distorts its discharge decisions in three ways. First, there is quantity withholding, i.e., discharging less than centrally optimal. Second, there is a shift in participation from day-ahead to real-time, i.e., postponing some of its discharge from day-ahead to real-time. Third, there is reduction in real-time responsiveness, or discharging less in response to smoothing real-time demand than centrally optimal. We also quantify the impact of the battery market power on total system cost via the Price of Anarchy metric, and prove that the it is always between $9/8$ and $4/3$. That is, incentive misalignment always exists, but it is bounded even in the worst case. We calibrate our model to real data from Los Angeles and Houston. Lastly, we show that competition is very effective at reducing distortions, but many market power mitigation mechanisms backfire, and lead to higher total cost.