Neha Patankar

This Research Digest summarizes key findings and implications of the recent publication, “Uncertainty-Aware Grid Planning in the Real World: A Method Enabling Large-Scale, Two-Stage Adaptive Robust Optimization for Capacity Expansion Planning.”

Capacity expansion models are frequently used to inform multi-billion dollar grid infrastructure decisions, a context in which there is significant uncertainty surrounding the future need for and performance of such infrastructure. However, despite much academic literature on the topic, virtually no grid planning processes use capacity expansion models that endogenously consider uncertainty, an oversight which frequently leads to short-sighted infrastructure decisions. This is partially due to a technology transfer gap, but it is also due to a lack of methods that work at large scale. In this paper we introduce a method for endogenizing uncertainty into capacity expansion models, a variant of adaptive robust optimization, that addresses this gap. We apply the method to a real-world capacity expansion planning problem, that of the State of California, and compare its performance to that of traditional adaptive robust optimization. We find that both the traditional method and our method identify increased transmission investment as a key lever for increasing robustness and adaptability, while helping to avoid downside risks that current deterministic planning processes may be exposing ratepayers to. Our method performs similarly to the traditional method in terms of outcomes, while significantly reducing computational complexity, making it scalable to real-world planning problems.

In this paper, we present modeling to generate continuous alternatives (MGCA), an MGA post-processing methodology. MGCA can be utilized once an initial MGA exploration has been completed and consists of the following steps: 1. Dimensional reduction of the MGA data to capacity decisions at the generator cluster level and metric evaluations (defined as affine transformations of variables e.g. cost, emissions, land use). 2. Formulation of an exploratory problem which includes MGCA’s novel constraint implicitly encoding the convex hull of MGA solutions. 3. Utilization of the exploratory problem to explore the convex hull of MGA solutions via introduction of additional constraints and new objectives, ideally in an interactive setting. 4. Export and evaluation of discovered interpolated solutions (later referenced as interpolates) to an operational model to evaluate operational metric values consistent with economic dispatch. The key innovation of MGCA lies in a recognition that a convex hull can be fully described by the set of convex combinations of exterior solutions which can be encoded as a constraint in an optimization problem. MGCA introduces several new capabilities compared to previous work: 1. Implicitly define the shape of the convex hull of all investment decisions with full spatial and technical resolution as discovered by a previous MGA exploration. 2. Create new feasible solutions with estimates of their associated outcome metric values (note, Vågerö et al [25] simultaneously invented a similar methodology for estimating metric values). 3. Allow users to define new optimization problems within the identified near-optimal feasible space which solve in fractions of a second. The MGCA approach enhances the usability of MGA results by allowing utility planners and stakeholders to rapidly generate feasible capacity mixes which optimize over their constraints and objectives of interest with generator cluster-level detail and estimated operational characteristics.

Expanding transmission capacity is likely a bottleneck that will restrict variable renewable energy (VRE) deployment required to achieve ambitious emission reduction goals. Interconnection and inter-zonal transmission buildout may be displaced by the optimal sizing of VRE to grid connection capacity and by the co-location of VRE and battery resources behind interconnection. However, neither of these capabilities is commonly captured in macro-energy system models. We develop two new functionalities to explore the substitutability of storage for transmission and the optimal capacity and siting decisions of renewable energy and battery resources through 2030 in the Western Interconnection of the United States. Our findings indicate that modeling optimized interconnection and storage co-location better captures the full value of energy storage and its ability to substitute for transmission. Optimizing interconnection capacity and co-location can reduce total grid connection and shorter-distance transmission capacity expansion on the order of 10% at storage penetration equivalent to 2.5%–10% of peak system demand. The decline in interconnection capacity corresponds with greater ratios of VRE to grid connection capacity (an average of 1.5–1.6 megawatt (MW) PV:1 MW inverter capacity, 1.2–1.3 MW wind:1 MW interconnection). Co-locating storage with VREs also results in a 9%–13% increase in wind capacity, as wind sites tend to require longer and more costly interconnection. Finally, co-located storage exhibits higher value than standalone storage in our model setup (up to ∼43%–45%). Given the coarse representation of transmission networks in our modeling, this outcome likely overstates the real-world importance of storage co-location with VREs. However, it highlights how siting storage in grid-constrained locations can maximize the value of storage and reduce transmission expansion.