Addressing conflict uncertainty in South Sudan
This project explores electricity planning strategies in South Sudan under future conflict uncertainty.
A stochastic energy system optimization model that explicitly considers the possibility of armed conflict leading to electric power generator damage is presented. Strategies that hedge against future conflict have the greatest economic value in moderate conflict-related damage scenarios by avoiding expensive near-term investments in infrastructure that may be subsequently damaged.
Model results show that solar photovoltaics can play a critical role in South Sudan’s future electric power system. In addition to mitigating greenhouse gas emissions and increasing access to electricity, this analysis suggests that solar can be used to hedge against economic losses incurred by conflict. While this analysis focuses on South Sudan, the analytical framework can be applied to other conflict-prone countries.
Links for Paper, Poster, Model, and Data
Modeling substitution effect between electricity and energy efficiency
Many policies aim to incentivize energy efficiency in order to meet environmental objectives. One of the objectives of Energy System Optimization Models (ESOMs) is to analyze the impact of these policies on the system.
A high level of technological detail in ESOMs, however, can be a challenge while modeling and analyzing energy efficiency policies. Moreover, consumers, who play a vital role while analyzing such policies are an important aspect of research in the development of ESOMs.
In this project, we build the Temoa-EE+ model where, on the production side, electric utilities have an option of investing in electricity generation and investing in energy efficiency as a way to satisfy future demands. While on the demand side, consumers meet their demand for energy services by purchasing electricity and energy efficiency from a supplier.
A wide range of consumer choices including substitutability between electricity and energy efficiency and productivity of energy efficiency can be analyzed with this model. Moreover, we develop a hypothetical test case to analyze welfare gain from efficiency crediting policy as compared to welfare gain from carbon tax policy.
Link for Model and Data
Impact of cost-related uncertainties on the US energy system
Energy system models often use forecasts of fuel prices and technology investment costs during the decision-making process to satisfy future projections of demand. Errors in the estimation of fuel price evolution, demand projections, and other future trajectories might over or underestimate the effects of policies on technology deployment.
In this study, we identify the most uncertain parameters and aims at overcoming the parametric uncertainty while modeling the temporal correlation. We provide a detailed methodology to characterize the uncertainty in the energy system. We consider the uncertainty regarding fuel prices and investment costs of the technologies by assessing the impact of uncertainty on energy planning decisions for the United States.
Link for Model
Land-use impact of renewable development on the American West
This study employs a spatially- temporally- and operationally-resolved electricity system capacity expansion model. We use the Modeling to Generate Alternatives (MGA) technique to generate a set of maximally different technology portfolios for 100% carbon-free electricity supply in the Western Interconnection all with similar system costs.
We consider the impacts of uncertainty in electricity demand growth due to electrification and availability of nascent long-duration energy storage and “clean firm” generation technologies (including hydrogen combustion, advanced nuclear, and natural gas plants with carbon capture and sequestration).
We focus in particular on strategies to minimize potential conflicts between wind and solar expansion and other land-use priorities in the American West.
Time-domain reduction techniques and alternative solutions in the power system models
With increasing renewable energy share in the system, geographic heterogeneity, and temporal variation in renewable resource profiles have made power system models computationally demanding.
To maintain computational tractability, power system models commonly reduce supply and demand time series data by aggregating the typical time periods using time series aggregation methodologies.
Simultaneously, power system models employ least-cost optimization methods that do not capture many real-world considerations such as heterogeneous stakeholder preferences, political considerations, and other non-modeled costs and constraints.
This study addresses the tradeoff between computational traceability and the ability to perform uncertainty analysis using the case study of the American West.
