• Energy Research

Promoting energy efficiency in iron and steel production provides opportunities for mitigating environmental impacts from this energy-intensive industry. Energy efficiency technologies differ in investment costs, fuel-saving potentials, and environmental performance. Hence the decision-making of the adoption strategy needs to prioritize technological combinations concerning these multi-dimensional objectives. To address this problem, this study proposes a hybrid multi-criteria decision-support model for adopting energy efficiency technologies in the iron and steel industry. The modeling framework integrates a linear programming model that determines the optimal technology adoption rates based on the techno-economic, energy, and environmental performance details and an interactive multi-criteria model analysis tool for diverse modeling environments. A real case study was performed in which a total number of 56 energy efficiency technologies were investigated against various criteria concerning economics, energy, and environmental performances. The results examine the tradeoffs and synergies were examined with regard to seven criteria. A balanced solution shows that a total investment of 13.4 billion USD could save 2.51 Exajoule fuel consumption, cut 67.4 million tons (Mton) CO2 emissions, and reduce air pollution of 1.5 Mton SO2, 1.41 Mton NOx, and 0.86 Mton PM, respectively. The case study demonstrates the effectiveness and applicability of the proposed multi-criteria decision-making support framework.

Efforts to provide alternative resources and technologies for producing liquid fuel have recently been intensified. Different levels of dependence on oil imports and carbon prices have a significant impact on the composition of the cost-minimizing portfolio of technologies. Considering such factors, how should China plan its future liquid fuel industry? The model for supporting the technology portfolio and capacity configuration that minimizes the total system cost until 2045 is described in this study. The results obtained for different carbon prices and levels of dependence on oil import indicate that the oil-to-liquid fuel (OTL) will remain dominant in China's liquid fuel industry over the next three decades. If the carbon price is low, the coal-to-liquid fuel (CTL) process is competitive. For a high carbon price, the biomass-to-liquid fuel (BTL) technology expands more rapidly. The results also reveal that developing the BTL and CTL can effectively reduce the oil-import dependency; moreover, a high carbon price can lead to the CTL being replaced with the low-carbon technology (e.g., BTL). Improvement in energy raw material conversion and application of CO2 removal technologies are also effective methods to control carbon emissions for achieving the carbon emission goals and ultimately emission reduction targets.

To investigate the complex relationships among the energy-related challenges faced by humanity, we marry a large-scale energy systems model, MESSAGE, with a multi-criteria model analysis tool. Such an approach is applicable to other modelling frameworks and can significantly improve the analysis of multiple goals. We focus our study on nuclear power - a technology viewed differently by different stakeholders. We find that nuclear power plays an important role in global climate change mitigation efforts where energy security and affordability goals take precedence, but that the total amount of nuclear in the system is highly dependent on stakeholders' preferences. We also find synergies among climate mitigation and energy security goals, and also between these two goals and the reduced need for underground carbon storage.

Even though China has been increasingly producing liquid fuel from alternative resources in recent decades, crude oil to liquid fuel process (OTL) still has a dominant position in China's liquid fuel industry. Therefore, the uncertainty of oil prices would greatly impact this country's economic and environmental valuations of alternative liquid fuel technologies. The present study develops an optimization model to analyze the technology portfolio of the liquid fuel industry. Two significant elements constitute this model, namely, the deterministic optimization part and the robust model. They achieve the aims of minimizing the total cost and maximizing the tolerance of data uncertainty under an ellipsoidal uncertainty set. In addition, we also investigate the impact of the increase in carbon prices on the technology portfolio. The results show that alternative technologies will be rapidly developed from 2020 to 2050 under oil price uncertainty, especially coal to liquid fuel (CTL) technology, which can reduce the dependency on crude oil but can generate a large amount of carbon emissions. For reducing the CO2 emissions in the liquid fuel industry, carbon prices have been additionally considered in this research. The results show that the increase of carbon prices could substantially decrease CO2 emissions, but using carbon trading alone cannot achieve the peak of carbon emissions by 2030. Thus, various types of clean technologies, i.e., hydrogen, solar, and wind, should be widely used in energy systems.

There is a growing interest in the power-to-liquid (PTL) technology, especially in using electricity from renewable sources to generate H2, and then coupled with CO2 captured from various sources (e.g., coal-fired power plants) to produce liquid fuels (e.g., gasoline). As a negative emission technology, the product of PTL could be used in the internal combustion engine vehicles (ICEV) and thus cause limited shifts in current energy infrastructure and automobile industry compared with the electrification paths. However, it is still unknown whether the PTL technology could be adopted and contributed to reaching carbon neutrality in China's transportation sector. Against this, a novel model of the liquid fuel supply system considering multiple low-emission technologies, including PTL, is constructed to evaluate PTL's potential contribution and cost to the carbon-neutral target of China's transportation sector. Results show the following: First, PTL can achieve a maximum 93% carbon emission reduction compared with oil to liquid (OTL). Second, the most cost-effective deployment strategy for PTL is to increase the total cost by 5–10%. Third, international oil prices and technology-learning effects have significant impacts on the diffusion of PTL. Fourth, PTL can be a supplementary solution to achieve net-zero emissions in the transportation sector.

Emissions trading schemes have been widely implemented by many countries to enforce the “cap and trade” concept for mitigating CO2 emissions. Thus, the carbon price influences the manufacturing costs in all stages of production, recycling, and disposal. Consideration of the carbon price is especially important for the economic efficiency of the downstream manufacturing sectors, such as in plastic product manufacturing, to substantially reduce their costs through the design and management of networked supply chains, which results in purchasing feedstocks from different technological routes, as well as choosing plants, warehouses and various transportation modes with diverse CO2 emission intensities. Supporting the decision-making in such situations requires the integration of life cycle analysis and networked supply chain management methodologies with an analysis of the carbon-market uncertainties. Such approaches have not been sufficiently quantified in the existing literature. This study presents a stochastic mixed-integer linear programming model developed for polyvinyl chloride pipe manufacturing in China, which is used to evaluate the effects of the life cycle emissions of procurement on the whole supply chain under carbon market uncertainty. Our results illustrate that the carbon market uncertainty would not only significantly influence the carbon-intensive production sectors but also the downstream manufacturing sectors. The five scenarios with carbon price variation exhibit distinctively different choices in procurement and supply chain configurations, as well as in their performances regarding total emissions and associated costs.

The paper proposes a solution to the problem of distributing electricity originating from various sources. In the proposed model, each source has a different cost of acquisition and is characterized by varying energy efficiency factors. Additionally, in the case of renewable sources, the costs of storing energy are taken into consideration as well. This work presents a fair and cost-efficient approach to distributing the demands of energy providers. A model has been developed and verified for the purpose of corroborating the process.

Sustainable development objectives surrounding water and energy are interdependent, and yet the associated performance metrics are often distinct. Regional planners tasked with designing future supply systems therefore require multi-criteria analysis methods and tools to determine a suitable combination of technologies and scale of investments. Previous research focused on optimizing system development strategy with respect to a single design objective, leading to potentially negative outcomes for other important sustainability metrics. This paper addresses this limitation, and presents a flexible multi-criteria model analysis framework that is applicable to long-term energy and water supply planning at national or regional scales in an interactive setup with decision-makers. The framework incorporates a linear systems-engineering model of the coupled supply technologies and inter-provincial transmission networks. The multi-criteria analysis approach enables the specification of diverse decision-making preferences for disparate criteria, and leads to quantitative understanding of trade-offs between the resulting criteria values of the corresponding Pareto-optimal solutions. A case study of the water-stressed nation of Saudi Arabia explores preferences combining aspiration and reservation levels in terms of cost, water sustainability and electricity sector CO2 emissions. The analysis reveals a suite of trade-off solutions, in which potential integrated water-energy system configurations remain relatively ambitious from both an economic and environmental perspective. The results highlight the importance of identifying suitable tradeoffs between water and energy sustainability objectives during the formulation of coupled transformation strategies.