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## Optimization - What is it and why does it matter for energy storage?

###### By:

###### Tom Thunell

###### Date:

###### October 6, 2022

Modeling energy production is a core task for those developing, financing, and operating renewable energy projects. However, with energy storage, since no energy is being produced, the main consideration to model is when should the battery charge and discharge? To answer this we need to define what we mean by “should” and come up with a way to represent the physical attributes of energy storage and the commercial/market rules for how they are allowed to operate. This type of problem lends itself to a method called optimization. We define an objective (the “should”), apply constraints (which captures the physical and commercial/market attributes), and determine the decisions that lead to the best outcome. This blog post describes the intuition behind optimization and how applying different constraints and optimization approaches impacts results.

Optimization is the process of maximizing or minimizing an objective function by finding the best available **decisions** across a set of inputs. When applied to energy storage modeling, the objective function is to **maximize revenue*** given a set of **constraints.**

Those constraints are driven by both physical battery and electrical properties (ex: limited amount of charge, or energy capacity) and market-based (ex: ISO requirements for certain reserved charge to serve a previous commitment). To understand how to maximize revenue, we also need to have some price expectation. For the rest of this blog, we consider price to be a fixed input and focus on the decision of when to **discharge** and **charge** to make the most revenue, subject to constraints. The charge/discharge is therefore the quantity of the Price x Quantity that makes up our revenue calculation that we are maximizing. Finally, because charging requires buying energy from the grid (or charging off of a co-located resource) and later supplying energy, the optimization captures a simple economic approach: buying energy at the lowest cost and selling it at the highest possible value.

All that said, optimization can be complicated and computationally intensive. Therefore, it is useful to have a rough approximation of how an optimization process works. For energy storage, a common approximation is the TxBx method. With this, modelers take the sum of the top X priced hours in a day and subtract the sum of the bottom X priced hours in a day to estimate the revenue you could generate from a storage project with X hours of duration. The benefit of this method is that it captures the simple economic approach of a battery: buying low and selling high. It also replicates some of the core constraints, namely the limited amount of charge capacity. Finally, it is simple to replicate in Excel, and can serve as an effective indicator of energy price volatility and a high potential energy storage opportunity.

Of course, in the field, the physical and market constraints are not optional, and need to be considered when modeling how a battery will operate. A true optimization, such as Tyba’s approach, ends up being required to align modeled battery behavior with realistic battery operations. Nevertheless, the TxBx method has useful explanatory power. Thus, we wanted to outline the broader set of relevant constraints for batteries and to what extent the TxBx method captures them — and the table below includes a (non-exhaustive) set of common physical and commercial constraints you may want to consider.

We want to understand how constraints impact optimization results (planned operations and revenue) and to ensure that model assumptions accurately reflect our distinct point of view on the market. To help illustrate this, we modeled a standalone storage project ERCOT over the course of a reference year under both the TxBx** and Tyba optimization approaches and varied the constraint assumptions around the:

*Hurdle rate, which is the delta between buying and selling energy required to be active with storage resource*

- None
- $10 per MWh

*Market participation strategy*

- Day-Ahead (DA) energy only
- DA energy and Real-Time (RT) energy co-optimized (also known as DART)
- DA energy and ERCOT’s Responsive Reserve Service (RRS) product co-optimized

*Utilization uncertainty associated with providing RRS*

- High utilization uncertainty
- Low utilization uncertainty

**What is utilization uncertainty?**

For ancillary service products like RRS, the amount the ISO will call on a resource to provide the service during an interval is typically not known. This leads to uncertainty in the exact amount of energy that is needed to deliver the service. To address this, Tyba allows users to set utilization bands that ensure you have a minimum of X MWh and maximum of Y MWh of ability to discharge / stop charge (for an up service) or charge / stop discharge (for a down service).

**Summary Results**

**Constraint & Optimization Approach Impacts**

**Optimization approach**- The required level of optimization sophistication will depend on the specifics of your project. In the case study above, a TxBx approach provided a rough (and overstated) revenue approximation for the energy-only strategies. However, as you move to more involved market strategies and/or granular project diligence, a more robust optimization approach is required, which is where Tyba’s approach shines and provides a more accurate view of the real world opportunity.**Focus on key constraints**- Understand the relevant constraints, which of those are most impactful for your project, and how to align those to fit within your broader strategy and risk profile. Tyba makes this easy by allowing you to quickly simulate performance under different constraint scenarios.**Build vs. Partner**- If your storage modeling efforts require a more sophisticated optimization approach, building your own optimizer can be extremely time consuming, requiring years of engineering work. You can partner with Tyba and seamlessly plug in Tyba’s optimizer (along with our input and output libraries) into your broader modeling and operations workflow.

If interested in learning more about how Tyba’s storage optimization approach and how it can support your modeling efforts, shoot us an email at info@tybaenergy.com.

*Tyba defaults to optimizing for revenue net of charging costs.

**For the TxBx approach, assumed you take the top 2 (T2) priced hours at any point in the day, the bottom 2 (B2) priced hours at any point in the day, and subtract T2 less B2 and apply charge/discharge efficiencies. This method does not ensure SOC feasibility as one of the T2 hours may come at a time when you have no energy in the battery or the B2 hours may come at a time when you have a full state of charge and thus no ability to charge.

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