The Impact of Uncertainty in National Energy Planning

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.

Strategic Energy Planning under Uncertainty: a Mixed-Integer Linear Programming Modeling Framework for Large-Scale Energy Systems

Michel Bierlaire, François Maréchal, Stefano Moret

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.

Elsevier2016

Characterization of input uncertainties in strategic energy planning models

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

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.

Elsevier Sci Ltd2017

Strategic energy planning for large-scale energy systems: A modelling framework to aid decision-making

Victor Codina Gironès, Daniel Favrat, François Maréchal, Stefano Moret

Concerns related to climate change and security of energy supply are pushing various countries to make strategic energy planning decisions. This requires the development of energy models to aid decision- making. Large scale energy models are often very complex and use economic optimization to define energy strategies. Thus, they might be black-boxes to public decision-makers. This work aims at over- coming this issue by proposing a new modelling framework, designed to support decision-makers by improving their understanding of the energy system. The goal is to show the effect of the policy and investment decisions on final energy consumption, total cost and environmental impact. The modelling approach and the model structure are described in detail. Final energy consumption is represented as the sum of three main components: heating, electricity and transportation. In this framework, a sequential modelling strategy allows the assessment of the competition between electricity and fuels in the heating and transportation sectors without increasing the model complexity. A monthly resolution is chosen in order to highlight seasonality issues of the energy system. Developed with the goal of being easily adaptable to any large-scale energy system, the modelling approach is currently implemented within an online energy calculator for the case of Switzerland.

2015

Characterization of input uncertainties in strategic energy planning models

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

Elsevier Sci Ltd2017