Neha Patankar

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.

Multi-day energy storage (MDS), a subset of long-duration energy storage, may become a critical technology for the decarbonization of the power sector, as current commercially available Lithium-ion battery storage technologies cannot cost-effectively shift energy to address multi-day or seasonal variability in demand and renewable energy availability. MDS is difficult to model in existing energy system planning models (such as electricity system capacity expansion models (CEMs)), as it is much more dependent on an accurate representation of chronology than other resources. Techniques exist for modeling MDS in these planning models; however, it is not known how spatial and temporal resolution affect the performance of these techniques, creating a research gap. In this study we examine what spatial and temporal resolution is necessary to accurately capture the full value of MDS, in the context of a continent-scale CEM. We use the results to draw conclusions and present best practices for modelers seeking to accurately model MDS in a macro-energy systems planning context. Our key findings are: (1) modeling MDS with linked representative periods is crucial to capturing its full value, (2) MDS value is highly sensitive to the cost and availability of other resources, and (3) temporal resolution is more important than spatial resolution for capturing the full value of MDS, although how much temporal resolution is needed will depend on the specific model context.

GenX is a highly-configurable, open source electricity resource capacity expansion model that incorporates several state-of-the-art practices in electricity system planning to offer improved decision support for a changing electricity landscape. GenX is a constrained linear or mixed integer linear optimization model that determines the portfolio of electricity generation, storage, transmission, and demand-side resource investments and operational decisions to meet electricity demand in one or more future planning years at lowest cost, while subject to a variety of power system operational constraints, resource availability limits, and other imposed environmental, market design, and policy constraints. GenX features a modular and transparent code structure developed in Julia + JuMP. The model is designed to be highly flexible and configurable for use in a variety of applications from academic research and technology evaluation to public policy and regulatory analysis and resource planning.

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 10-15% 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 (22-25%). 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.