Drivers of the Resource Adequacy Contribution of Solar and Storage for Florida Municipal Utilities

Abstract

Solar's variable generation limits its contribution to reliably meeting peak demand, or its resource adequacy contribution. Energy storage is a leading option to increase solar's resource adequacy contribution, yet the contribution from different configurations of solar and storage is not widely understood. We develop methods for exploring the primary drivers of an estimate of the resource adequacy contribution of solar and storage, and we apply the methods to a case study in Florida, where demand peaks in winter and summer. We find that the portion of solar nameplate capacity that contributes to resource adequacy-its capacity credit-is less than 50% and that it declines with increasing solar penetration. The capacity credit of storage, even though it is fully controllable by the system operator, strongly depends on the duration of storage. The capacity credit of 1 hour of storage can be less than the capacity credit of solar. Achieving a 90% capacity credit requires at least 4-5 hours of storage when storage capacity is small relative to the system peak. As storage deployment increases to 20% of the peak demand, 9 hours-and sometimes more than 10 hours-of storage are needed to achieve a 90% capacity credit. Increased solar deployment at the system level can increase storage's capacity credit. Directly pairing solar and storage can also impact the capacity credit. Storage with a power rating similar to the solar inverter rating loses capacity credit when coupled with solar if its duration is more than 1-2 hours, because storage competes with solar for use of the inverter. On the other hand, there is no reduction in capacity credit when the storage is small relative to the solar inverter. The approach and tools developed here for exploratory analysis can be useful for many other utilities and regions grappling with similar preliminary questions, prior to evaluation of specific cases using more detailed and resourceintensive modeling.

Research Priorities and Opportunities in United States Wholesale Electricity Markets

“…Also, a fast-changing energy system landscape requires advanced quantification rules for emerging technology capacity contribution, including battery storage (Denholm et al 2020;Sioshansi et al 2013), DR (Parks 2019;Liu 2017), and hybrid resources (Mills and Rodriguez 2019;Byers and Botterud 2019). Additional research is needed to explore and capture the dynamics of capacity credits of these new technologies under different system configurations and market participation rules, and thereby to inform ISO market design.…”

“…As discussed in greater detail in section 4, methods for estimating the capacity credit of VRE, using probabilistic effective load-carrying capability (ELCC) estimates, are increasingly used by system planners and can be applied to new VRE resources like offshore wind (Beiter et al 2020). A variety of approaches to calculating the capacity credit of energy-limited resources are being developed for storage, including an approach proposed by PJM (Muzhikyan et al 2019), DR (Klem and Stephen 2019;Parks 2019), and hybrids (Mills and Rodriguez 2019). Analysis by MISO highlights the challenge created by the fact that increasing penetration of VRE changes the risk profile (or distribution of loss-of-load probability) of systems (Pickering et al 2019).…”

“…Muzhikyan et al (2019) similarly find that shifts can occur with increases in energy-limited resources although there is less literature on that aspect. This shift causes changes in the capacity credit with increasing penetration (Heath and Figueroa-Acevedo 2018;Parks 2019) and allows for potential synergies between multiple technologies (Byers and Botterud 2019;Mills and Rodriguez 2019). In practice, capacity markets vary in the methods used to calculate capacity credits of emerging technologies (Byers et al 2018), and deviations between capacity credits assigned in practice versus those estimated with state-of-the-art methods can lead to inefficient market signals (Bothwell and Hobbs 2017).…”

Comparing Capacity Credit Calculations for Wind: A Case Study in Texas

“…In most of these approximation-based methods, the CC is estimated by calculating the average CF during key time periods, chosen to represent times of system stress. These approximation methods are widely used with various assumptions (Stephen, Hale, and Cowiestoll 2020, Mills and Rodriguez 2019). We choose these key periods with multiple selection criteria to compare across various methodologies typically encountered.…”

Representing DC-Coupled PV+Battery Hybrids in a Capacity Expansion Model

“…For tightly DCcoupled systems in particular, the capacity credit may be reduced if the solar array is not generating enough to recharge the storage device between high-risk periods (from a resource adequacy perspective) (Schleifer et al 2021). The capacity credit of a loosely DC-coupled system may be somewhat larger (but still smaller than the sum of the capacity credits for independent PV and battery systems), especially in regions with longer-duration high-risk periods (Mills and Rodriguez 2019;Cowiestoll 2020).…”