Abstract: Over the past decade, the promotion of renewable energy projects in Chile, especially solar energy projects, has become increasingly important, as energy dependence from foreign fossil fuels has increased and concerns regarding climate change continue to grow, posing a significant challenge to the local economy. Even though recent developments toward a more sustainable energy matrix in Chile have significantly increased the investment in the solar energy sector, social and environmental fragilities, combined with the lack of well-functioning institutions and the historical marginalization of indigenous communities who have been affected by several energy projects, result in gradually increasing conflictive situations. Unless proper mechanisms are designed and implemented to rapidly and correctly address these challenges, Chile could miss the opportunities that solar energy projects can provide to the development of its communities and to the economic growth of its regions. This article studies solar photovoltaics planning in Chile, focusing on the recent developments and the main challenges ahead, and proposes policy recommendations for effectively addressing these challenges.

Abstract: To adequately meet the nutritional needs of broilers, it is necessary to know the values of apparent metabolizable energy corrected by the nitrogen balance (AMEn) of the feedstuffs. To determine AMEn values, biological assays, feedstuff composition tables, or prediction equations are used as a function of the chemical composition of feedstuffs, the latter using statistical methodologies such as multiple linear regression, neural networks, and Bayesian networks (BN). BN is a statistical and computational methodology that consists of graphical (graph) and probabilistic models of quantitative and/or qualitative variables. Ensembles of BN in the area of broiler nutrition are expected, as there is no research showing their AMEn prediction performance. The purpose of this article is to propose and use ensembles of hybrid Bayesian networks (EHBNs) and obtain prediction equations for the AMEn of feedstuffs used in broiler nutrition from their chemical compositions. We trained 100, 1,000, and 10,000 EHBN, and in this way, empirical distributions were found for the coefficients of the covariates (crude protein, ether extract, mineral matter, and crude fiber). Thus, the mean, median, and mode of these distributions were calculated to build prediction equations for AMEn. It is observed that the method for obtaining the coefficients of the covariates discussed in this article is an unprecedented proposal in the field of broiler nutrition. The data used to obtain the equations were obtained by meta-analysis, and the data for the validation of the equations were obtained from metabolic tests. The proposed equations were evaluated by precision measures such as the mean square error (MSE), mean absolute deviation (MAD), and mean absolute percentage error (MAPE). The best equations for predicting AMEn were derived from the mean or mode coefficients for the 10,000 EHBN results. In conclusion, the methodology used is a good tool to obtain prediction equations for AMEn as a function of the chemical composition of their feedstuffs. The coefficients were found to differ from those found by other methodologies, such as the usual neural network or multiple linear regressions. The field of broiler nutrition contributed with new equations and with a never-applied methodology and differentiated in obtaining its coefficients by empirical distributions.

Abstract: In the context of updating the 2015 Nationally Determined Contribution (NDC), the government of Chile has updated its estimates of compliance costs for a series of mitigation actions with an emphasis on the energy sector as the main source of its greenhouse gas emissions. Using the information developed in this process, we assess the impact on compliance costs of increasing the flexibility for sources by introducing different emissions trading schemes. For this we develop a detailed optimization model that represents the operational and investment decisions that could be taken by the energy generation, industrial and mining sectors if an Emissions Trading System (ETS) was implemented. An ETS with two cap and trade options is analysed together with an offset mechanism for sources not included in the ETS. Also, two policy goals are considered: a stringent 76% sectoral reduction goal in 2050 similar to Chile's current strict NDC, and a more lax 46% goal similar to Chile's initial 2015 NDC proposal. The results show that (i) cost reductions from increased flexibility for Chile's current strict NDC are significant, and that offsets can play an important role; (ii) the stringency of the reduction goal affects the magnitude of the cost savings related to flexibility and, surprisingly, total abatement costs are negative (i.e. there are benefits) for the 46% reduction goal. In this latter case, the most significant cost reductions result from compelling firms to comply with their allowances in each sector, not increased flexibility. These results highlight the policy relevance of case by case analysis using a modelling approach similar to the one we develop here. Key policy insights ETS implementation can help Chile meet its mitigation commitment for 2050. The compliance costs can vary significantly depending on the flexibility implemented in the emissions trading schemes. Optimization models can help decision-makers define the attributes of an ETS, such as the sectors that should participate, the cap, and the percentage of offsets. The proposed methodology also highlights and quantifies the offsets that can be acquired from sectors that are not part of an ETS, such as forestry, agriculture, and the waste sector. The possibility to acquire of offsets could reduce significantly the cost for industries that participate of an ETS.

Abstract: Residential firewood burning is the main source of PM2.5 emissions in southern and central Chile. In Chile, approximately 4000 premature deaths are observed each year due to air pollution. Mitigation policies aim to reduce dwellings' energy demand and foster cleaner but more expensive energy sources. Pre-existing energy poverty conditions are often overlooked in these policies, even though they can negatively affect the adoption of these measures. This article uses southern and central Chile as a case study to assess quantitatively different policy scenarios of PM2.5 emissions between 2017 and 2050, considering energy poverty-related effects. Results show that PM2.5 emissions will grow 16% over time under a business as usual scenario. If thermal improvement and stove/heater replacements are implemented, PM2.5 reductions depend on the scale of the policy: a 5%-6% reduction of total southern and central Chile PM2.5 emissions if only cities with Atmospheric Decontamination Plans are included; a 54%-56% reduction of PM2.5 emissions if these policies include other growing cities. Our study shows that the energy poverty effect potentially reduces the effectiveness of these measures in 25%. Consequently, if no anticipatory measures are taken, Chile's energy transition goals could be hindered and the effectiveness of mitigation policies to improve air quality significantly reduced.

Abstract: Due to the low-inertia and significant renewable generation variability in isolated microgrids, short time-scale fluctuations in the order of seconds can have a large impact on a microgrid's frequency regulation performance. In this context, the present paper presents a mathematical model for an Energy Management System (EMS) that takes into account the operational impact of the short-term fluctuations stemming from renewable generation rapid changes, and the role that renewable curtailment and batteries, including their degradation, can play to counter-balance these variations. Computational experiments on the real Kasabonika Lake First Nation microgrid and CIGRE benchmark test system show the operational benefits of the proposed EMS, highlighting the need to properly model short-term fluctuations and battery degradation in EMS for isolated microgrids with significant renewable integration.

Abstract: Thermal energy storage is an expanding field within the subject of renewable energy technologies. After a listing of the different possibilities available for energy storage, this paper provides a comparison of various materials for High Temperature Thermal Energy Storage (HTTS). Several attributes and needs of each solution are listed. One in particular is using the latent heat as one of the most efficient ways to store thermal energy. The mixture of phase change material (PCM) embedded in a metal foam is optimising the thermal properties of the material for latent heat energy storage. The results of previous studies show that mechanical and thermal properties of foam were extensively studied separately. This paper highlights the potential for an advanced study of thermo-mechanical properties of metal foams embedded with PCM. (c) 2012 Elsevier Ltd. All rights reserved.

Abstract: In order to improve the thermal efficiency of building thermal energy storage (TES) systems, the feasibility of using encapsulated phase change materials (EPCMs) as heat storage media is analysed in this work. Specifically, the finite element method is used to perform thermal behaviour analyses of several EPCMs. These analyses include technical and economic assessments in order to identify the best combination of PCM and shell material, using as main parameters: thermal energy storage, heat transfer rate, materials cost, among others. The results show that EPCMs composed by Na2SO4 center dot 6H(2)O as PCM and covered by stainless steel highlight as TES materials.

Abstract: Chile's 2050 energy policy ultimate goals are to produce a sustainable model of economic growth respectful of the environment where energy is produced efficiently and reliably. Renewable energy sources are considered the main drive for developing by 2050 at least 70% of the total energy in Chile. This study aims to provide a quantitative analysis for the selection of the most sustainable energy production methods using the compromise ranking method (VIKOR) that uses maximum group utility for the majority and a minimum of individual regret for the opponent. Since all evaluations are provided via intervals, the possible degree theory is used to compare them. Nine major criteria are critically used for this purpose and prioritized using analytical hierarchical process (AHP). Since Chile's energy production matrix still relies heavily on fossil fuels with major concerns of GHG emissions, all major potential energy sources in Chile are considered including ocean energy in addition to nuclear energy. This study shows that biomasses are the best compromise solution and that traditional and modern nuclear energy plants score consistently better than solar power. Large hydro power plants rank very high but in light of the social opposition present in the country, they might not be easy to build as hoped. Ocean power is far superior to geothermal energy and comparable to wind power and for this reason it should be considered together with nuclear power for the future Chilean energy matrix.

Abstract: Spain must reduce its energy consumption by 23% and achieve 100% renewable energy in electricity generation by 2030. This paper presents the current energy scenario en Spain, and the outlooks for different renewable options, with special focus on photovoltaic (PV) solar energy. In 2012, Spain was the number two European country in terms of installed rewnewable energy power. Solar PV technology has the potential to meet Spain's future energy demand and its associated environmental challenges. This paper gives a review of solar energy economy at global scale for both PV and thermal power technologies. The Spanish energy scenario shows actual trends and progress made by solar power. Economic concepts of levelised cost of electricity and grid parity are presented. The financial analysis shows that PV electricity achieves grid parity at a plant profitability rate up to 7.26%. (C) 2015 Elsevier Ltd. All rights reserved.

Abstract: Worldwide, environmental regulations such as Renewable Portfolio Standards (RPSs) are being broadly adopted to promote renewable energy investments. With corresponding increases in renewable energy deployments, there is growing interest in grid-scale energy storage systems (ESS) to provide the flexibility needed to efficiently deliver renewable power to consumers. Our contribution in this paper is to introduce a unified generation, transmission, and bulk ESS expansion planning model subject to an RPS constraint, formulated as a two-stage stochastic mixed-integer linear program (MILP) optimization model, which we then use to study the impact of co-optimization and evaluate the economic interaction between investments in these three asset classes in achieving high renewable penetrations. We present numerical case studies using the 24-bus IEEE RTS-96 test system considering wind and solar as available renewable energy resources, and demonstrate that up to $180 million/yr in total cost savings can result from the co-optimization of all three assets, relative to a situation in which no ESS investment options are available. Surprisingly, we find that co-optimized bulk ESS investments provide significant economic value through investment deferrals in transmission and generation capacity, but very little savings in operational cost. Finally, we observe that planning transmission and generation infrastructure first and later optimizing ESS investments as is common in industry captures at most 1.7% ($3 million/yr) of the savings that result from co-optimizing all assets simultaneously. (C) 2016 Elsevier Ltd. All rights reserved.