Abstract:The transition to a sustainable bio‐based economy is perceived as a valid path towards low‐carbon development for emerging economies that have rich biomass resources. In the case of Colombia, the role of biomass has been tackled through qualitative roadmaps and regional climate policy assessments. However, neither of these approaches has addressed the complexity of the bio‐based economy systematically in the wider context of emission mitigation and energy and chemicals supply. In response to this limitation, w… Show more
“…The second family comprises bottom up energy system optimization models (ESOMs). Younis et al [24] developed a detailed representation of biomass value chains for the Colombian energy system, including advanced biorefinery, bioenergy combined with carbon capture and storage (BECCS), and (bio)chemicals options. This framework is referred to as TIMES-CO-BBE (an offspring of MARKAL Colombia [25,26]).…”
Section: Case Studymentioning
confidence: 99%
“…Both models lack a detailed representation of load following constraints or cycling costs, which are often represented in high technical detail operational power system models (e.g., unit commitment models) [3]. h The main input parameters to TIMES-CO-BBE are based on Younis et al [24] (see the datasets in Supplementary S1 [34,35]). For consistency, the input data to both models are harmonized.…”
“…The stylized time slices have been adjusted to allow for comparison between alternative time slicing systems. Moreover, Younis et al [24] used exogenous grid penetration constraints for VRES based on power system studies in the literature [29,30]. These constraints are disregarded in this analysis because the system integration of VRES is explicitly addressed.…”
“…The demand is determined by exogenous socio-economic drivers of energy services, for example, lighting, cooking, heating/cooling, and machine drive in four main sectors, namely, commercial and others, industrial, residential, and transport and also the demand for base chemicals, edible sugars, and oils, which are relevant for the bioeconomy. The main constraints include techno-economic learning rates and operational limits of technologies, the availability and cost of resource extraction and/or trade, emission mitigation target, and the subsurface storage potential of carbon dioxide [24] (see Supplementary S1).…”
Section: Times-co-bbe Energy System Optimization Modelmentioning
confidence: 99%
“…In this way, electricity could compete with other energy carriers, such as biomass, fossil fuels, and hydrogen. More details of the model are published elsewhere [24].…”
Section: Times-co-bbe Energy System Optimization Modelmentioning
The large-scale integration of variable renewable energy sources into the energy system presents techno–economic challenges. Long–term energy system optimization models fail to adequately capture these challenges because of the low temporal resolution of these tools. This limitation has often been addressed either by direct improvements within the long–term models or by coupling them to higher resolution power system models. In this study, a combined approach is proposed to capitalize on the advantages and overcome the disadvantages of both methods. First, the temporal resolution of an energy model was enhanced by approximating the joint probability of the electricity load and the supply of intermittent sources. Second, the projected electricity mix was simulated by a power model at an hourly resolution. This framework was used to analyze mid–century deep decarbonization trajectories for Colombia, subject to future uncertainties of hydroclimatic variability and the development of the bioeconomy. The direct integration method is found to consistently reduce the overestimation of the feasible penetration of VRES. However, its impact is marginal because of its inability to assess the short–term operation of the power system in detail. When combined with the soft–linking method, the reliable operation of the power system is shown to incur an additional overhead of 12–17% investment in flexible generation capacity, 2–5% of the annual energy system cost, and a 15–27% shortfall in achieving the aspired GHG mitigation target. The results obtained by combining both methods are found to be closer to the global optimum solution than using either of these methods individually.
“…The second family comprises bottom up energy system optimization models (ESOMs). Younis et al [24] developed a detailed representation of biomass value chains for the Colombian energy system, including advanced biorefinery, bioenergy combined with carbon capture and storage (BECCS), and (bio)chemicals options. This framework is referred to as TIMES-CO-BBE (an offspring of MARKAL Colombia [25,26]).…”
Section: Case Studymentioning
confidence: 99%
“…Both models lack a detailed representation of load following constraints or cycling costs, which are often represented in high technical detail operational power system models (e.g., unit commitment models) [3]. h The main input parameters to TIMES-CO-BBE are based on Younis et al [24] (see the datasets in Supplementary S1 [34,35]). For consistency, the input data to both models are harmonized.…”
“…The stylized time slices have been adjusted to allow for comparison between alternative time slicing systems. Moreover, Younis et al [24] used exogenous grid penetration constraints for VRES based on power system studies in the literature [29,30]. These constraints are disregarded in this analysis because the system integration of VRES is explicitly addressed.…”
“…The demand is determined by exogenous socio-economic drivers of energy services, for example, lighting, cooking, heating/cooling, and machine drive in four main sectors, namely, commercial and others, industrial, residential, and transport and also the demand for base chemicals, edible sugars, and oils, which are relevant for the bioeconomy. The main constraints include techno-economic learning rates and operational limits of technologies, the availability and cost of resource extraction and/or trade, emission mitigation target, and the subsurface storage potential of carbon dioxide [24] (see Supplementary S1).…”
Section: Times-co-bbe Energy System Optimization Modelmentioning
confidence: 99%
“…In this way, electricity could compete with other energy carriers, such as biomass, fossil fuels, and hydrogen. More details of the model are published elsewhere [24].…”
Section: Times-co-bbe Energy System Optimization Modelmentioning
The large-scale integration of variable renewable energy sources into the energy system presents techno–economic challenges. Long–term energy system optimization models fail to adequately capture these challenges because of the low temporal resolution of these tools. This limitation has often been addressed either by direct improvements within the long–term models or by coupling them to higher resolution power system models. In this study, a combined approach is proposed to capitalize on the advantages and overcome the disadvantages of both methods. First, the temporal resolution of an energy model was enhanced by approximating the joint probability of the electricity load and the supply of intermittent sources. Second, the projected electricity mix was simulated by a power model at an hourly resolution. This framework was used to analyze mid–century deep decarbonization trajectories for Colombia, subject to future uncertainties of hydroclimatic variability and the development of the bioeconomy. The direct integration method is found to consistently reduce the overestimation of the feasible penetration of VRES. However, its impact is marginal because of its inability to assess the short–term operation of the power system in detail. When combined with the soft–linking method, the reliable operation of the power system is shown to incur an additional overhead of 12–17% investment in flexible generation capacity, 2–5% of the annual energy system cost, and a 15–27% shortfall in achieving the aspired GHG mitigation target. The results obtained by combining both methods are found to be closer to the global optimum solution than using either of these methods individually.
Feedstock resources for renewable natural gas (RNG) production by biological (e.g., anaerobic digestion) and thermochemical (e.g., gasification) conversion methods in Hawaii have been reviewed. Statewide estimates of RNG production potential from urban resources (wastewater, existing landfills, foodwaste, construction and demolition waste (CDW), and municipal solid waste) total 8860 TJ year À1 . Honolulu has the largest resource base for these urban waste streams. Underutilized agricultural land resources in the state could support substantial RNG production from dedicated energy crops (260-520 GJ ha À1 year À1 ), although agronomic suitability of specific candidate energy crops would need to be evaluated and confirmed.
Purpose of Review
This paper reviews recent literature on the combined use of bioenergy with carbon capture and storage (BECCS) in the industries of steel, cement, paper, ethanol, and chemicals, focusing on estimates of potential costs and the possibility of achieving “negative emissions”.
Recent Findings
Bioethanol is seen as a potential near-term source of negative emissions, with CO2 transport as the main cost limitation. The paper industry is a current source of biogenic CO2, but complex CO2 capture configurations raise costs and limit BECCS potential. Remuneration for stored biogenic CO2 is needed to incentivise BECCS in these sectors. BECCS could also be used for carbon–neutral production of steel, cement, and chemicals, but these will likely require substantial incentives to become cost-competitive. While negative emissions may be possible from all industries considered, the overall CO2 balance is highly sensitive to biomass supply chains. Furthermore, the resource intensity of biomass cultivation and energy production for CO2 capture risks burden-shifting to other environmental impacts.
Summary
Research on BECCS-in-industry is limited but growing, and estimates of costs and environmental impacts vary widely. While negative emissions are possible, transparent presentation of assumptions, system boundaries, and results is needed to increase comparability. In particular, the mixing of avoided emissions and physical storage of atmospheric CO2 creates confusion of whether physical negative emissions occur. More attention is needed to the geographic context of BECCS-in-industry outside of Europe, the USA, and Brazil, taking into account local biomass supply chains and CO2 storage siting, and minimise burden-shifting.
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