Unique Value of ReEDS
Spatial Resolution and Variability Consideration
The Regional Energy Deployment System (ReEDS) model has singular capabilities that differentiate it from other models and that make it uniquely suitable for certain types of analyses. While ReEDS can model all types of power generators and fuels—coal, gas, nuclear, renewables—it was designed primarily to address considerations for integrating renewable electric technologies into the power grid. In particular, it was designed to address the variable resource issues associated with solar and wind power as well as the remote nature of many of the best wind resources and their need for transmission. These capabilities require the two primary structural elements of ReEDS—a multiplicity of regions and a sequential-solve formulation.
The high spatial resolution in ReEDS allows the model to better explore implications of the spatial mismatch between resource and load: the model can directly account for both the transmission requirements associated with developing a remote wind or solar site, the value of the resource quality, and geographic diversity associated with that site. ReEDS allows the different characteristics to play off one another and development decisions are co-optimized considering all relevant factors.
An important consideration regarding the integration of wind and solar technology is how variability and uncertainty of output from those sources will impact the operation and reliability of the system into which it will be integrated. ReEDS uses a statistical methodology to quantify the impacts of variability along multiple dimensions, allowing representation of those effects throughout the scenario. Because those effects are highly non-linear and dependent on both the characteristics of the potential development site and the balance-of-system, ReEDS recomputes the variability parameters between each two-year optimization. Along with improving ReEDS' representation of variable renewables, this approach also allows endogenous learning, dynamic interactions with other models, limited foresight, price penalties for rapid growth, and other non-linear effects.
A side-effect of the high spatial resolution of ReEDS is its ability to represent the transmission network with reasonable fidelity. With 134 nodes connected by 308 lines (representing aggregations of physical transmission lines), ReEDS can allow the transfer of power and reserves around the network, connecting remote generators and loads subject to physical limitations. ReEDS uses a linear approximation of DC-power flow to govern the distribution of flows across the network.
These capabilities in ReEDS allow the examination of a broad range of policy and R&D investment scenarios without artificially constraining the outcomes as done in many other models. The flexibility inherent to that approach allows ReEDS to analyze a wide variety of scenarios resulting in a diversity of possible electric-system regimes. Policies investigated include: clean energy strategies, renewable portfolio standards, carbon caps and taxes, environmentally-driven early coal retirements, and transmission siting restrictions. ReEDS has also been used extensively to examine how R&D investment success can impact market penetration of renewable power systems and other electric-sector technologies.
While many models take advantage of geographic information system (GIS) databases of existing generation and transmission capacity, load centers, political boundaries, etc., ReEDS goes a step further and uses GIS capabilities to drive the effective spatial resolution of certain aspects of ReEDS to a sub-region level. For example, ReEDS uses GIS to develop supply curves of wind resources within each of the model's 358 resource regions. For each region and class of wind resource, ReEDS creates a supply curve that captures the cost of connecting that class of wind resource to nearby transmission lines while taking into account the individual wind resources within the region and the line's location and transmission capacity.
Coupling with Production-Cost Models
ReEDS' capacity-expansion capabilities can be coupled with production-cost models like GridView (from ABB) or PLEXOS (Energy Exemplar) to further resolve generation and transmission details. ReEDS scenario outputs can be used to define the infrastructure for the production cost model, which can verify the ReEDS operational decisions and provide additional operational detail. GridView and PLEXOS scenarios not only ensure that there are adequate generation resources to support loads and the variable resources of wind and solar, but can also ensure that the transmission system is adequate to handle the resulting flows.
While charts and tables are helpful, visualizing geospatial results provides a whole new level of appreciation for system operation and the value of the spatially-defined capacity expansion estimates. Along with ReEDS-output-based maps, the outputs of GridView or PLEXOS can be returned to the GIS system to show system-level operation—both generation and transmission flows—at very fine spatial and temporal detail.