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Carvallo, C., Jalil-Vega, F., & Moreno, R. (2023). A multi-energy multi-microgrid system planning model for decarbonisation and decontamination of isolated systems. Appl. Energy, 343, 121143.
Abstract: Decarbonising and decontaminating remote regions in the world presents several challenges. Many of these regions feature isolation, dispersed demand in large areas, and a lack of economic resources that impede the development of robust and sustainable networks. Furthermore, isolated systems in the developing world are mostly based on diesel generation for electricity, and firewood and liquefied petroleum gas for heating, as these options do not require a significant infrastructure cost. In this context, we present a stochastic multi-energy multi-microgrid system planning model that integrates electricity, heat and hydrogen networks in isolated systems. The model is stochastic to capture uncertainty in renewable generation outputs, particularly hydro and wind, and thus design a multi-energy system proved secured against such uncertainty. The model also features two distinct constraints to limit the emissions of CO2 (for decarbonisation) and particulate matter (for decontamination), and incorporates firewood as a heating source. Moreover, given that the focus is on low-voltage networks, we introduce a fully linear AC power flow equations set, allowing the planning model to remain tractable. The model is applied to a real-world case study to design a multi-energy multi-microgrid system in an isolated region in Chilean Patagonia. In a case with a zero limit over direct CO2 emissions, the total system's cost increases by 34% with respect to an unconstrained case. In a case with a zero limit over particulate matter emissions, the total system's cost increases by 189%. Finally, although an absolute zero limit over both, particulate matter and direct CO2 emissions, leads to a total system's cost increase of 650%, important benefits in terms of decarbonisation and decontamination can be achieved at marginal cost increments.
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Ferrada, F., Babonneau, F., Homem-de-Mello, T., & Jalil-Vega, F. (2023). The role of hydrogen for deep decarbonization of energy systems: A Chilean case study. Energy Policy, 177, 113536.
Abstract: In this paper we implement a long-term multi-sectoral energy planning model to evaluate the role of green hydrogen in the energy mix of Chile, a country with a high renewable potential, under stringent emission reduction objectives in 2050. Our results show that green hydrogen is a cost-effective and environmentally friendly route especially for hard-to-abate sectors, such as interprovincial and freight transport. They also suggest a strong synergy of hydrogen with electricity generation from renewable sources. Our numerical simulations show that Chile should (i) start immediately to develop hydrogen production through electrolyzers all along the country, (ii) keep investing in wind and solar generation capacities ensuring a low cost hydrogen production and reinforce the power transmission grid to allow nodal hydrogen production, (iii) foster the use of electric mobility for cars and local buses and of hydrogen for long-haul trucks and interprovincial buses and, (iv) develop seasonal hydrogen storage and hydrogen cells to be exploited for electricity supply, especially for the most stringent emission reduction objectives.
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Guevara, E., Babonneau, F., Homem-de-Mello, T., & Moret, S. (2020). A machine learning and distributionally robust optimization framework for strategic energy planning under uncertainty. Appl. Energy, 271, 18 pp.
Abstract: This paper investigates how the choice of stochastic approaches and distribution assumptions impacts strategic investment decisions in energy planning problems. We formulate a two-stage stochastic programming model assuming different distributions for the input parameters and show that there is significant discrepancy among the associated stochastic solutions and other robust solutions published in the literature. To remedy this sensitivity issue, we propose a combined machine learning and distributionally robust optimization (DRO) approach which produces more robust and stable strategic investment decisions with respect to uncertainty assumptions. DRO is applied to deal with ambiguous probability distributions and Machine Learning is used to restrict the DRO model to a subset of important uncertain parameters ensuring computational tractability. Finally, we perform an out-of-sample simulation process to evaluate solutions performances. The Swiss energy system is used as a case study all along the paper to validate the approach.
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Vargas-Ferrer, P., Alvarez-Miranda, E., Tenreiro, C., & Jalil-Vega, F. (2022). Assessing flexibility for integrating renewable energies into carbon neutral multi-regional systems: The case of the Chilean power system. Energy Sustain. Dev., 70, 442–455.
Abstract: Reducing emissions from power systems requires enhancing the penetration of non-conventional renewable energy sources (NCRE) in the generation mix. However, such penetration requires high levels of operational flexibility in order to ensure an adequate balance between generation and demand. Concentrating solar power plants with thermal storage (CSP-TES) and battery energy storage systems (BESS) have shown to possess technical characteristics compatible with such high flexibility requirements. However, due to the high capital costs of these technologies, decision-makers must seek for cost-effective configurations and operation modes. This study presents the development of a methodological framework for designing the long-term transition of a multi-regional energy system towards a low carbon emission system. The sought system is characterized by a high penetration of NCRE, and the use of CSP-TES, BESS and electricity transmission settings for providing effective levels of operational flexibility. For this, the transformation of the Chilean electricity system between the years 2018-2050 is studied, using a tailored modification of the well-known OSeMOSYS optimization tool for energy systems planning. The main results indicate that by 2050, and considering a baseline scenario defined for 2016, for most of the scenarios studied the renewable electricity generation would be at least a 90 % and CO2 emissions would be 75 % lower. Furthermore, it is shown that providing operational flexibility to the system requires a mixed generation from hydroelectric reservoirs, CSP-TES plants, BESS, pumped-storage hydropower and natural gas generators. The obtained results allow planning the capacity and operation of CSP and BESS plants, which are adapted to the future flexibility requirements of the Chilean electric power system. Incentive policies like stimuli to growth BESS, would favor primarily the photovoltaic growth of the system at the expense of CSP-TES capacity, while CSP-TES growth incentives would maintain photovoltaic generation levels, but would decrease Wind and natural gas generation.
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Zavala, C., Babonneau, F., & Homem-de-Mello, T. (2023). Measuring the impact of regional climate change on heating and cooling demand for the Chilean energy transition. J. Clean. Prod., 428, 139390.
Abstract: The regional impact of climate change on heating and cooling demand is important to consider when designing optimal long-term energy policies. Several studies have addressed this issue, but either at a very aggregated level or without optimizing the whole energy system. The aims of this paper are to fill this gap in a generic way and to assess the impact of climate change on heating and cooling energy demands for residential and commercial sectors at the regional and nodal levels in the context of Chile's energy transition. We propose a methodology based on high resolution climate simulations for the Representative Concentration Pathways (RCP) RCP 2.6 and RCP 8.5 scenarios. First, a statistical analysis is performed to estimate the long-term trends of so-called heating and cooling degree-days and their impact on final regional energy demands. Then, demand pathways in the energy transition are assessed using a multi-sectoral energy planning model. Numerical experiments using data from Chile show an overall positive economic impact of climate change (limited to heating and cooling demands) for the energy system, with a significant decrease in heating demand compared to a limited increase in cooling requirements. For the RCP 8.5 scenario, cost reductions reach 2.1% of the total discounted system cost on the 2020-2050 period mainly due to a significant decrease of gas consumption for heating. This research highlights the importance for policymakers to consider climate change in efficient energy policies.
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