Decision support for strategic energy planning: A robust optimization framework

Moret, Stefano; Babonneau, Frédéric Louis François; Bierlaire, Michel

doi:10.1016/j.ejor.2019.06.015

Cited by 67 publications

(29 citation statements)

References 49 publications

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“…Accounting for uncertainties in energy system long-term planning is crucial [50] and challenging given inaccurate forecasts and scarcity of data [24,28]. To address this challenge, Moret et al [24] developed a methodology to define ranges of parameter uncertainties.…”

Section: Uncertainty Characterisationmentioning

confidence: 99%

“…To assess the importance of the electrofuels in a defossilised energy system, this work gives the results of an uncertainty quantification performed on a whole-energy model, EnergyScope Typical Days (EnergyScope TD) [27]. It optimises the investment and operation strategies to meet the end-use demand of the system (i.e., electricity, heat, and mobility) and minimise its total annual cost [28]. Based on previous research on the Belgian energy system [29] and uncertainty characterisation [24], this analysis applies the polynomial chaos expansion (PCE) method [30].…”

Section: Introductionmentioning

confidence: 99%

“…PCE provides a computationally efficient alternative to the Monte Carlo simulation for uncertainty quantification to address the "curse of dimensionality" [31]. Given limited information about the uncertainty of the parameters for long-term energy planning models [28], PCE builds a surrogate model in a computationally efficient way [32]. Based on this surrogate model, this paper highlights the most-impacting parameters through their sensitivity indices (i.e., Sobol' indices) and extracts valuable statistical moments (e.g., mean-µ or variance-σ 2 ) of the total cost of the system.…”

Section: Introductionmentioning

confidence: 99%

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The Role of Electrofuels under Uncertainties for the Belgian Energy Transition

et al. 2021

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Wind and solar energies present a time and space disparity that generally leads to a mismatch between the demand and the supply. To harvest their maximum potentials, one of the main challenges is the storage and transport of these energies. This challenge can be tackled by electrofuels, such as hydrogen, methane, and methanol. They offer three main advantages: compatibility with existing distribution networks or technologies of conversion, economical storage solution for high capacity, and ability to couple sectors (i.e., electricity to transport, to heat, or to industry). However, the level of contribution of electric-energy carriers is unknown. To assess their role in the future, we used whole-energy system modelling (EnergyScope Typical Days) to study the case of Belgium in 2050. This model is multi-energy and multi-sector. It optimises the design of the overall system to minimise its costs and emissions. Such a model relies on many parameters (e.g., price of natural gas, efficiency of heat pump) to represent as closely as possible the future energy system. However, these parameters can be highly uncertain, especially for long-term planning. Consequently, this work uses the polynomial chaos expansion method to integrate a global sensitivity analysis in order to highlight the influence of the parameters on the total cost of the system. The outcome of this analysis points out that, compared to the deterministic cost-optimum situation, the system cost, accounting for uncertainties, becomes higher (+17%) and twice more uncertain at carbon neutrality and that electrofuels are a major contribution to the uncertainty (up to 53% in the variation of the costs) due to their importance in the energy system and their high uncertainties, their higher price, and uncertainty.

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Section: Uncertainty Characterisationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Role of Electrofuels under Uncertainties for the Belgian Energy Transition

et al. 2021

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“…The problem is modelled using second order cone programming and semidefinite programming. Moret et al (2019) propose a robust optimization framework considering multiple uncertain parameters in objective function and constraints. The model and methods are applied to the case study of a national energy system.…”

Section: Literature Reviewmentioning

confidence: 99%

A stochastic programming model for an energy planning problem: formulation, solution method and application

et al. 2021

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The paper investigates national/regional power generation expansion planning for medium/longterm analysis in the presence of electricity demand uncertainty. A two-stage stochastic programming is designed to determine the optimal mix of energy supply sources with the aim to minimise the expected total cost of electricity generation considering the total carbon dioxide emissions produced by the power plants.Compared to models available in the extant literature, the proposed stochastic generation expansion model is constructed based on sets of feasible slots (schedules) of existing and potential power plants. To reduce the total emissions produced, two approaches are applied where the first one is performed by introducing emission costs to penalise the total emissions produced. The second approach transforms the stochastic model into a multi-objective problem using the -constraint method for producing the Pareto optimal solutions. As the proposed stochastic energy problem is challenging to solve, a technique that decomposes the problem into a set of smaller problems is designed to obtain good solutions within an acceptable computational time. The practical use of the proposed model has been assessed through application to the regional power system in Indonesia. The computational experiments show that the proposed methodology runs well and the results of the model may also be used to provide directions/guidance for Indonesian government on which power plants/technologies are most feasible to be built in the future. Keywords Energy planning • Stochastic programming • Multi-objective optimization 1 IntroductionGlobal electricity production has grown continuously year by year since 1974. According to International Energy Agency (www.iea.org), world gross electricity production has increased from 6,299 to 26,730 TWh between 1974 and 2018 with an average annual growth rate of 3.3 %. Electricity generation can be divided into three types, namely fossil fuel-based, nuclear, and renewable energy power generation. The fossil fuelbased plants include coal, petroleum, and natural gas which produce large quantity of cheap energy with greenhouse gas (GHG) emissions. The renewable energy consists of hydro, solar, wind, geothermal and biomass which generate clean energy (without GHG emissions or potentially carbon neutral in the case of biomass). However, the cost and feasibility of renewable energy are highly dependent on the potential of regions and its weather conditions (Thangavelu et al, 2015). The strategic energy planning includes the medium-to long-term expansion of the electricity generating capacity. Cost effective and low emission energy

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“…Mohammad and Mishra [21] used PDFs for modelling the uncertainties of the introduced problem to coordinate wind power and demand response with the aim of minimising the operation cost in day‐ahead electricity markets. Moret et al [22] employed a robust method to model the uncertain parameters for strategic energy planning. Robust optimisation, among all the suggested methods, proved to be a powerful tool in solving optimisation problems in uncertain environments [23].…”

Section: Introductionmentioning

confidence: 99%

Flexible two‐stage robust model for moving the transmission and reactive power infrastructures expansion planning towards power system integration of renewables

Olfatinezhad¹,

Vahidinasab²,

Ahmadian³

et al. 2020

IET Renewable Power Generation

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This study proposes a two-stage robust optimisation framework to model uncertainties in construction cost, load and renewable power generations (RPGs) in the coordinated transmission and reactive power infrastructure expansion planning problem. The proposed planning model, based on the network AC model and its voltage constraints, is represented as a mixedinteger linear programing problem with the aim of minimising: (i) the construction costs of lines, (ii) installation cost of reactive power resources and (iii) active load shedding as RPGs curtailment. Using the suggested robust optimisation approach, the best expansion plan is obtained even in the worst case of the defined uncertainty sets and the plan robustness level is controlled via parameter definition for the individual uncertainty sets. The Benders decomposition algorithm is used to solve the two-stage problem where the plan security level is also taken into consideration using the N−1 contingency criterion. The standard Garver 6-bus system and the IEEE 24-bus reliability test system are used to validate the proposed robust framework.

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Decision support for strategic energy planning: A robust optimization framework

Cited by 67 publications

References 49 publications

The Role of Electrofuels under Uncertainties for the Belgian Energy Transition

The Role of Electrofuels under Uncertainties for the Belgian Energy Transition

A stochastic programming model for an energy planning problem: formulation, solution method and application

Flexible two‐stage robust model for moving the transmission and reactive power infrastructures expansion planning towards power system integration of renewables

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