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Publication# Characterization of input uncertainties in strategic energy planning models

Michel Bierlaire, Victor Codina Gironès, François Maréchal, Stefano Moret

*Elsevier Sci Ltd, *2017

Article

Article

Résumé

Various countries and communities are defining strategic energy plans driven by concerns for climate change and security of energy supply. Energy models can support this decision-making process. The long-term planning horizon requires uncertainty to be accounted for. To do this, the uncertainty of input parameters needs to be quantified. Classical approaches are based on the calculation of probability distributions for the inputs. In the context of strategic energy planning, this is often limited by the scarce quantity and quality of available data. To overcome this limitation, we propose an application-driven method for uncertainty characterization, allowing the definition of ranges of variation for the uncertain parameters. To obtain a proof of concept, the method is applied to a representative mixed-integer linear programming national energy planning model in the context of a global sensitivity analysis (GSA) study. To deal with the large number of inputs, parameters are organized into different categories and uncertainty is characterized for one representative parameter per category. The obtained ranges serve as input to the GSA, which is performed in two stages to deal with the large problem size. The application of the method generates uncertainty ranges for typical parameters in energy planning models. Uncertainty ranges vary significantly for different parameters, from [-2%,2%] for electricity grid losses to [-47.3%, 89.9%] for the price of imported resources. The GSA results indicate that only few parameters are influential, that economic parameters (interest rates and price of imported resources) have the highest impact, and that it is crucial to avoid an arbitrary a priori exclusion of parameters from the analysis. Finally, we demonstrate that the obtained uncertainty characterization is relevant by comparing it with the assumption of equal levels of uncertainty for all input parameters, which results in a fundamentally different parameter ranking. (C) 2017 Elsevier Ltd. All rights reserved.

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Uncertainty

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Parameter

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Michel Bierlaire, Victor Codina Gironès, François Maréchal, Stefano Moret

Concerns related to climate change and security of energy supply are pushing various countries to define strategic energy plans. Strategic energy planning for national energy systems involves investment decisions (selection and sizing) for energy conversion technologies over a time horizon of 20-50 years. This long time horizon requires uncertainty to be accounted for. Long-term planning for energy systems is often based on deterministic economic optimization and forecasts of fuel prices. When fuel price evolution is underestimated, the consequence is a low penetration of renewables and more efficient technologies in favor of fossil alternatives. This work aims at overcoming this issue by assessing the impact of uncertainty on strategic energy planning decisions. A classification of uncertainty in national energy systems decision-making is performed. A Global Sensitivity Analysis (GSA) is performed in order to highlight the influence of the model uncertain parameters onto the energy strategy. Optimization under uncertainty is then applied to a general Mixed-Integer Linear Programming (MILP) problem having as objective the total annual cost and assessing as well the IPCC Global Warming Potential LCIA indicator (CO2-equivalent emissions). The application focuses on the case study of Switzerland. It is shown that in the uncertain domain investing in more efficient and cleaner technologies can be economically optimal.

2016, ,

Various countries and communities are defining strategic energy plans driven by concerns related to climate change and security of energy supply. Energy models are needed to support this decision-making process. The long time horizon inherent to strategic energy planning requires uncertainty to be accounted for. Most energy models available today are too complex or computationally expensive for uncertainty analyses to be carried out. This study proposes a concise multi-period Mixed-Integer Linear Programming (MILP) formulation for strategic energy planning under uncertainty. The modeling framework allows optimizing the energy system in a snapshot future year having as objective the total annual cost and assessing as well the global CO2-equivalent emissions. Key features of the model are a clear distinction both between demand and supply and between resources and technologies, a low computational time and a multiperiod resolution to account for issues related to seasonality and energy storage. The model is applied to a real case study and a Global Sensitivity Analysis (GSA) highlights the impact of uncertain parameters in energy planning.

Various countries and communities are defining or rethinking their energy strategy driven by concerns for climate change and security of energy supply. Energy models, often based on optimization, can support this decision-making process. In the current energy planning practice, most models are deterministic, i.e. they do not consider uncertainty and rely on long-term forecasts for important parameters. However, over the long time horizons of energy planning, forecasts often prove to be inaccurate, which can lead to overcapacity and underutilization of the installed technologies. Although this shows the need of considering uncertainty in energy planning, uncertainty is to date seldom integrated in energy models. The main barriers to a wider penetration of uncertainty are i) the complexity and computational expense of energy models; ii) the issue of quantifying input uncertainties and determining their nature; iii) the selection of appropriate methods for incorporating uncertainties in energy models. To overcome these limitations, this thesis answers the following research question How does uncertainty impact strategic energy planning and how can we facilitate the integration of uncertainty in the energy modeling practice? with four novel methodological contributions. First, a mixed-integer linear programming modeling framework for large-scale energy systems is presented. Given the energy demand, the efficiency and cost of energy conversion technologies, the availability and cost of resources, the model identifies the optimal investment and operation strategies to meet the demand and minimize the total annual cost or greenhouse gas emissions. The concise formulation and low computational time make it suitable for uncertainty applications. Second, a method is introduced to characterize input uncertainties in energy planning models. Third, the adoption of a two-stage global sensitivity analysis approach is proposed to deal with the large number of uncertain parameters in energy planning models. Fourth, a complete robust optimization framework is developed to incorporate uncertainty in optimization-based energy models, allowing to consider uncertainty both in the objective function and in the other constraints. To evaluate the impact of uncertainty, the presentation of the methods is systematically associated to their validation on the real case study of the Swiss energy system. In this context, a novelty is represented by the consideration of all uncertain parameters in the analysis. The main finding is that uncertainty dramatically impacts energy planning decisions. The results reveal that uncertainty levels vary significantly for different parameters, and that the way in which uncertainty is characterized has a strong impact on the results. In the case study, economic parameters, such as the discount rate and the price of imported resources, are the most impacting inputs; also, parameters which are commonly considered as fixed assumptions in energy models emerge as critical factors, which shows that it is crucial to avoid an a priori exclusion of parameters from the analysis. The energy strategy drastically changes if uncertainty is considered. In particular, it is demonstrated that robust solutions, characterized by a higher penetration of renewables and of efficient technologies, can offer more reliability and stability compared to investment plans made without accounting for uncertainty, at the price of a marginally higher cost.