In retail electricity, risk isn't just a footnote in the quarterly report; it's the central challenge of our business model. At Base Power Company, we've developed our own way of quantifying and managing our exposure to ERCOT's volatile market. This post walks through our risk management philosophy, the framework we've built, and how we've implemented it in our day-to-day operations.
The Fundamental Challenge
Retail electricity providers (REPs) operate in a world of structural risk asymmetry. We sell fixed-price contracts to customers while purchasing power from a market where prices can spike from $20/MWh to $5,000/MWh in minutes. This creates a potential for catastrophic loss that must be managed with precision.
The challenge is further complicated by the limited storability of electricity. Unlike commodity traders who can take physical delivery and warehouse their assets, most REPs must balance supply and demand instantaneously, 24/7/365. Our risk exposure includes:
- Price risk: Exposure to wholesale market volatility while serving fixed-price retail contracts
- Volume risk: Uncertain customer consumption patterns driven largely by weather events
Some other risks that we won’t cover today (but will in future posts) include:
- Credit risk: Customer payment default potential
- Regulatory risk: Changing market rules and compliance requirements
- Basis risk: Price differentials between financial hedging instruments and actual delivery locations
The complexity compounds because these risks interact in non-linear ways. A heat wave simultaneously increases consumption (volume risk) and market prices (price risk), creating a multiplicative rather than additive exposure.
Quantifying Risk
The first challenge is defining what risk means and figuring out how to measure it. For Base, risk means the potential financial exposure our retail electricity business faces due to market volatility. Specifically, it represents our obligation to procure power for our customers through counterparties or directly from ERCOT at prices that may substantially exceed what we've contracted to charge our customers.
Traditional approaches tend to use simplistic worst-case scenarios represented by the following equation:
maximum cost = max(load) · max(price) · duration
Where:
- duration is a time frame that an organization feels captures worst case risk appropriately
- max(price) in ERCOT is $5,000/MWh
- max(load) is the load of their portfolio they have to serve over the duration
This leads to excessive conservatism and inefficient capital allocation. We needed a more nuanced approach.
The key insight was recognizing that risk is looking at the tails of our distribution and isn’t very different to how we would look at our expected P&L. In this case the distribution is the P&L our REP could make in a variety of outcomes. The other insight was risk is computed over some timeframe, e.g. our risk tomorrow or our risk for the remainder of the year, which can lead to having multiple risk metrics that are tracked. This takes a single number and gives you a view on the business from multiple angles.
As our customers pay us a fixed rate for electricity we can reduce the modeling complexity by only looking at our cost to serve our customers and ignoring the revenue portion of the P&L equation:
cost to serve = Σ over t, Σ over c, of load(c, t) · price(t)
Where:
- c is a single customer, t is a 15 minute time step (ERCOT bills us based on electricity usage and prices in 15 minute increments)
- load(c, t) is the load of a given customer, c, at time, t
- price(t) is the ERCOT wholesale price of electricity at time, t (there are other costs associated with delivering power but for simplicity we’ll ignore them)
We can generate a distribution of cost to serve numbers over different load and price combinations. From here we can convert this into actionable metrics that are common in financial risk management:
- Value at Risk (VaR): The nth percentile of our potential daily obligation, representing our exposure in all but the most extreme n% of scenarios.
- Conditional Value at Risk (CVaR): The average of all outcomes above the nth percentile, providing visibility into the severity of tail events.
We chose to look at the 95th or 99th percentile as a way for us to understand our tail risk. You may be wondering why we don't look at the worst case scenario, we’ll dive into that in the next section.
Why Not Model the Absolute Worst Case?
Three key reasons:
- Statistical uncertainty: The 100th percentile is inherently unstable and cannot be reliably estimated from historical data
- Decision paralysis: Optimizing for the absolute worst case leads to prohibitively expensive hedging strategies
- Capital efficiency: The 99th percentile offers a practical balance between protection and performance
Simulation Framework: Monte Carlo with Historical Bootstrapping
Our initial implementation uses a Monte Carlo approach with random sampling from historical distributions:
- We sample from historical summer load and price data, preserving the correlation structure between these variables
- We scale historical customer load profiles to our projected customer base
- We calculate the resulting distribution of daily obligations
- We extract the 99th percentile and compare it to our risk threshold
The power of this approach comes from modeling the full distribution of outcomes rather than fixating on a single scenario. This gives us a much more nuanced view of our risk landscape.
Practical Application: From Mathematics to Market Decisions
Our risk framework translates directly into actionable hedging strategies. Each morning, we view updated risk metrics on a dashboard showing:
- Current VaR and CVaR by load zone
- Scenario analyses for extreme weather events
- Hedge recommendations to maintain risk within defined thresholds
We’ve built on this mathematical model to create a risk system that allows us to easily quantify the impact of our decisions, e.g. how would the risk profile and expected P&L would change if we purchased more hedges.
In a future post we’ll discuss how we incorporate our batteries into our strategy and we’ll deep dive into why over hedging can be detrimental to a REP.
About the author
Zayn Hanif works on the Markets Team, building algorithms and models to use Base batteries to stabilize the grid and power our retail engine. Before Base worked as a Quantitative Developer at Citadel on both their US Power Trading Desk & European Natural Gas Trading Desk.
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