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Decision Making in Energy Systems

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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "C: Energy Economics and Policy".

Deadline for manuscript submissions: closed (30 May 2022) | Viewed by 20954

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Special Issue Editors

Prof. Dr. Olivier Bahn

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Guest Editor

GERAD and Department of Decision Sciences, HEC Montréal, Montréal, Canada
Interests: operational research; mathematical models in economy/energy/environment; climate changes; energy policies

Dr. Azadeh Maroufmashat

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Guest Editor

Department of Decision Sciences, HEC Montréal, Montréal, Canada
Interests: energy system; optimization; power to gas; hydrogen economy; inetgarted modeling
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Special Issue Information

Dear Colleagues,

With an expected increase in energy demand levels, together with large-scale integration of renewable energy with existing energy systems and recent government policies regarding climate change goals, system modelling has become an important instrument to find robust, reliable, and affordable near-zero emission layouts for energy systems. Access to large amounts of data (big data), together with progress in machine learning approaches combined with modern optimization techniques, opens up new perspectives in decision making for the design and deployment of low-carbon energy systems. This should bring important insights for policy-makers.

This Special Issue encourages researchers to address the following themes through scientific and multidisciplinary knowledge of machine learning, optimization, and system modelling. We therefore invite relevant papers on innovative technical developments, including reviews, case studies, and assessments.

We welcome contributions including but not limited to the following themes:

Data-driven energy management strategies;
Data-driven forecasting models to help to manage intermittent renewable energy;
Optimization of energy systems using machine learning and deep learning approaches;
Smart energy systems;
Decision-making approaches for addressing energy management in specific energy sectors (transportation, residential, etc.);
System dynamic modelling-based approach to address climate and energy transition issues;
Technical, environmental, and economic perspectives of energy systems;
Health impact assessment of energy systems;
Renewable energy and climate policies.

Prof. Dr. Olivier Bahn
Dr. Azadeh Maroufmashat
Guest Editors

Manuscript Submission Information

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Keywords

mathematical modelling
big data valuation
machine learning
optimization
policy analysis
energy systems

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Published Papers (7 papers)

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Research

18 pages, 1211 KiB

Open AccessArticle

Energy Transition Pathways for Deep Decarbonization of the Greater Montreal Region: An Energy Optimization Framework

by Sajad Aliakbari Sani, Azadeh Maroufmashat, Frédéric Babonneau, Olivier Bahn, Erick Delage, Alain Haurie, Normand Mousseau and Kathleen Vaillancourt

Energies 2022, 15(10), 3760; https://doi.org/10.3390/en15103760 - 20 May 2022

Cited by 6 | Viewed by 2675

Abstract

More than half of the world’s population live in cities, and by 2050, it is expected that this proportion will reach almost 68%. These densely populated cities consume more than 75% of the world’s primary energy and are responsible for the emission of around 70% of anthropogenic carbon. Providing sustainable energy for the growing demand in cities requires multifaceted planning approach. In this study, we modeled the energy system of the Greater Montreal region to evaluate the impact of different environmental mitigation policies on the energy system of this region over a long-term period (2020–2050). In doing so, we have used the open-source optimization-based model called the Energy–Technology–Environment Model (ETEM). The ETEM is a long-term bottom–up energy model that provides insight into the best options for cities to procure energy, and satisfies useful demands while reducing carbon dioxide (CO₂) emissions. Results show that, under a deep decarbonization scenario, the transportation, commercial, and residential sectors will contribute to emission reduction by 6.9, 1.6, and 1 million ton CO₂-eq in 2050, respectively, compared with their 2020 levels. This is mainly achieved by (i) replacing fossil fuel cars with electric-based vehicles in private and public transportation sectors; (ii) replacing fossil fuel furnaces with electric heat pumps to satisfy heating demand in buildings; and (iii) improving the efficiency of buildings by isolating walls and roofs. Full article

(This article belongs to the Special Issue Decision Making in Energy Systems)

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26 pages, 8085 KiB

Open AccessArticle

Deliberation Platform for Energy Transition Policies: How to Make Complex Things Simple

by Andra Blumberga, Armands Gravelsins and Dagnija Blumberga

Energies 2022, 15(1), 90; https://doi.org/10.3390/en15010090 - 23 Dec 2021

Cited by 8 | Viewed by 3395

Abstract

The energy transition from inefficient fossil-based to sustainable energy systems can face various lock-ins. There are no pathways that are free of stress. However, many routes are possible. A good understanding of the dynamic behavior of systems is crucial, and proper support tools are needed to assess the outcomes of every selected pathway. This study aims to develop an Internet-based interface tool for the national energy simulation model as a tool for a “hybrid forum”; study energy transition lock-ins in one of the Eastern European countries; and apply the interface tool to study different pathways to Latvia’s climate and energy goals. System dynamics are used to reach the goals of the study. A causal loop diagram is applied to study feedback loops and lock-ins, a stock-and-flow structure is used to build a simulation model, and a user interface tool is built on top of it. The results show that the developed interface tool is user-friendly and can be used as a discussion platform. The results from the case study reveal how the soft power of Russia can lock in the energy transition in Eastern European countries by creating policy choices with additive effects and what pathways towards energy transition can be used to lock-out. Full article

(This article belongs to the Special Issue Decision Making in Energy Systems)

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31 pages, 1233 KiB

Open AccessArticle

Robust Enough? Exploring Temperature-Constrained Energy Transition Pathways under Climate Uncertainty

by Claire Nicolas, Stéphane Tchung-Ming, Olivier Bahn and Erick Delage

Energies 2021, 14(24), 8595; https://doi.org/10.3390/en14248595 - 20 Dec 2021

Cited by 3 | Viewed by 2273

Abstract

In this paper, we study how uncertainties weighing on the climate system impact the optimal technological pathways the world energy system should take to comply with stringent mitigation objectives. We use the TIAM-World model that relies on the TIMES modelling approach. Its climate module is inspired by the DICE model. Using robust optimization techniques, we assess the impact of the climate system parameter uncertainty on energy transition pathways under various climate constraints. Unlike other studies we consider all the climate system parameters which is of primary importance since: (i) parameters and outcomes of climate models are all inherently uncertain (parametric uncertainty); and (ii) the simplified models at stake summarize phenomena that are by nature complex and non-linear in a few, sometimes linear, equations so that structural uncertainty is also a major issue. The use of robust optimization allows us to identify economic energy transition pathways under climate constraints for which the outcome scenarios remain relevant for any realization of the climate parameters. In this sense, transition pathways are made robust. We find that the abatement strategies are quite different between the two temperature targets. The most stringent one is reached by investing massively in carbon removal technologies such as bioenergy with carbon capture and storage (BECCS) which have yields much lower than traditional fossil fuelled technologies. Full article

(This article belongs to the Special Issue Decision Making in Energy Systems)

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17 pages, 1415 KiB

Open AccessArticle

Interpretable Forecasting of Energy Demand in the Residential Sector

by Nikos Sakkas, Sofia Yfanti, Costas Daskalakis, Eduard Barbu and Marharyta Domnich

Energies 2021, 14(20), 6568; https://doi.org/10.3390/en14206568 - 12 Oct 2021

Cited by 9 | Viewed by 2200

Abstract

Energy demand forecasting is practiced in several time frames; different explanatory variables are used in each case to serve different decision support mandates. For example, in the short, daily, term building level, forecasting may serve as a performance baseline. On the other end, we have long-term, policy-oriented forecasting exercises. TIMES (an acronym for The Integrated Markal Efom System) allows us to model supply and anticipated technology shifts over a long-term horizon, often extending as far away in time as 2100. Between these two time frames, we also have a mid-term forecasting time frame, that of a few years ahead. Investigations here are aimed at policy support, although in a more mid-term horizon, we address issues such as investment planning and pricing. In this paper, we develop and evaluate statistical and neural network approaches for this mid-term forecasting of final energy and electricity for the residential sector in six EU countries (Germany, the Netherlands, Sweden, Spain, Portugal and Greece). Various possible approaches to model the explanatory variables used are presented, discussed, and assessed as to their suitability. Our end goal extends beyond model accuracy; we also include interpretability and counterfactual concepts and analysis, aiming at the development of a modelling approach that can provide decision support for strategies aimed at influencing energy demand. Full article

(This article belongs to the Special Issue Decision Making in Energy Systems)

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25 pages, 1676 KiB

Open AccessArticle

Optimal Decision Making in Electrical Systems Using an Asset Risk Management Framework

by David L. Alvarez, Diego F. Rodriguez, Alben Cardenas, F. Faria da Silva, Claus Leth Bak, Rodolfo García and Sergio Rivera

Energies 2021, 14(16), 4987; https://doi.org/10.3390/en14164987 - 13 Aug 2021

Cited by 6 | Viewed by 2848

Abstract

In this paper, a methodology for optimal decision making for electrical systems is addressed. This methodology seeks to identify and to prioritize the replacement and maintenance of a power asset fleet optimizing the return of investment. It fulfills this objective by considering the risk index, the replacement and maintenance costs, and the company revenue. The risk index is estimated and predicted for each asset using both its condition records and by evaluating the consequence of its failure. The condition is quantified as the probability of failure of the asset, and the consequence is determined by the impact of the asset failure on the whole system. Failure probability is estimated using the health index as scoring of asset condition. The consequence is evaluated considering a failure impact on the objectives of reliability (energy not supplied -ENS), environment, legality, and finance using Monte Carlo simulations for an assumed period of planning. Finally, the methodology was implemented in an open-source library called PywerAPM for assessing optimal decisions, where the proposed mathematical optimization problem is solved. As a benchmark, the power transformer fleet of the New England IEEE 39 Bus System was used. Condition records were provided by a local utility to compute the health index of each transformer. Subsequently, a Monte Carlo contingency simulation was performed to estimate the energy not supplied for a period of analysis of 10 years. As a result, the fleet is ranked according to risk index, and the optimal replacement and maintenance are estimated for the entire fleet. Full article

(This article belongs to the Special Issue Decision Making in Energy Systems)

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16 pages, 2454 KiB

Open AccessArticle

Decarbonizing the Chilean Electric Power System: A Prospective Analysis of Alternative Carbon Emissions Policies

by Frédéric Babonneau, Javiera Barrera and Javiera Toledo

Energies 2021, 14(16), 4768; https://doi.org/10.3390/en14164768 - 5 Aug 2021

Cited by 14 | Viewed by 2718

Abstract

In this paper, we investigate potential pathways for achieving deep reductions in CO₂ emissions by 2050 in the Chilean electric power system. We simulate the evolution of the power system using a long-term planning model for policy analysis that identifies investments and operation strategies to meet demand and CO₂ emissions reductions at the lowest possible cost. The model considers a simplified representation of the main transmission network and representative days to simulate operations considering the variability of demand and renewable resources at different geographical locations. We perform a scenario analysis assuming different ambitious renewable energy and emission reduction targets by 2050. As observed in other studies, we show that the incremental cost of reducing CO₂ emissions without carbon capture or offset alternatives increases significantly as the system approaches zero emissions. Indeed, the carbon tax is multiplied by a factor of 4 to eliminate the last Mt of CO₂ emissions, i.e., from 2000 to almost 8500 USD/tCO₂ in 2050. This result highlights the importance of implementing technology-neutral mechanisms that help investors identify the most cost-efficient actions to reduce CO₂ emissions. Our analysis shows that Carbon Capture and Storage could permit to divide by more than two the total system cost of a 100% renewable scenario. Furthermore, it also illustrates the importance of implementing economy-wide carbon emissions policies that ensure that the incremental costs to reduce CO₂ emissions are roughly similar across different sectors of the economy. Full article

(This article belongs to the Special Issue Decision Making in Energy Systems)

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26 pages, 7218 KiB

Open AccessArticle

Recoupling Climate Change and Air Quality: Exploring Low-Emission Options in Urban Transportation Using the TIMES-City Model

by Jonas Forsberg and Anna Krook-Riekkola

Energies 2021, 14(11), 3220; https://doi.org/10.3390/en14113220 - 31 May 2021

Cited by 8 | Viewed by 3178

Abstract

Fossil fuels in transportation are a significant source of local emissions in and around cities; thus, decarbonising transportation can reduce both greenhouse gases (GHGs) and air pollutants (APs). However, the degree of these reductions depends on what replaces fossil fuels. Today, GHG and AP mitigation strategies are typically ‘decoupled’ as they have different motivations and responsibilities. This study investigates the ancillary benefits on (a) APs if the transport sector is decarbonised, and (b) GHGs if APs are drastically cut and (c) the possible co-benefits from targeting APs and GHGs in parallel, using an energy-system optimisation model with a detailed and consistent representation of technology and fuel choices. While biofuels are the most cost-efficient option for meeting ambitious climate-change-mitigation targets, they have a very limited effect on reducing APs. Single-handed deep cuts in APs require a shift to zero-emission battery electric and hydrogen fuel cell vehicles (BEVs, HFCVs), which can result in significant upstream GHG emissions from electricity and hydrogen production. BEVs powered by ‘green’ electricity are identified as the most cost-efficient option for substantially cutting both GHGs and APs. A firm understanding of these empirical relationships is needed to support comprehensive mitigation strategies that tackle the range of sustainability challenges facing cities. Full article

(This article belongs to the Special Issue Decision Making in Energy Systems)

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