This paper 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. . This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ 3
MethodsKey aspects of the modeling effort are described in this section. We begin by describing Tools for Energy Model Optimization and Analysis (Temoa), the open source energy system optimization model and the South Sudan input dataset used to conduct this work. Next, we describe Method of Morris, a sensitivity analysis technique that allows us to identify the input parameters with the largest effect on total system cost. Then we describe the stochastic model formulation, the method by which generator damage is estimated, and the metrics used to assess the cost of conflict uncertainty. The appendix provides additional detail on technology specifications, demand projection, and the estimation of damages. (Temoa) is an open source, Python-based framework to conduct energy systems analysis. The core component of Temoa is a bottom-up, technology rich energy system optimization model (ESOM). The Temoa model formulation is similar to the MARKAL/TIMES model generators (Fishbone and Abilock, 1981), MESSAGE (Messner andStrubegger, 1995), and OSeMOSYS (Howells et al., 2011). Technologies are represented by a set of engineering-economic parameters, and linked together in an energy system network through a user-specified series of commodity flows. The model employs linear optimization to minimize the system-wide cost of energy supply over the user-defined time horizon by optimizing the installed capacity and utilization of energy technologies. Several constraints ensure appropriate system performance, including energy supply sufficient to meet demand, energy balance at both the process and system-wide levels, and operating limits on baseload plants. The complete algebraic formulation of Temoa is published (Hunter et. al., 2013), and the model source code is publicly available through a GitHub repository (TemoaProject, 2018).
Tools for Energy Model Optimization and Analysis (Temoa) Tools for Energy Model Optimization and Analysis
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