This paper solves the problem of dynamic pricing strategy in an urban integrated energy-traffic system. A three-stage incentive scheme has been introduced to enhance operational profits and mitigate the impact of system uncertainties. Pricing strategies in the three stages, namely day-ahead equilibrium references, hour-level prices, and surge prices, are used to incentivize human users. The proposed framework could yield a set of charging and service prices for IETS, which could improve in overall revenue and security.
Realization methods for energy optimal urban driving are the prime focus of this paper. Insights into the real-time generation of such trajectories as well as “in traffic” methods for their execution are provided. This includes the usage of piecewise linear approximations of typical urban speed profiles, urban platooning, filtering of trajectories, and modifying initial accelerations on the trajectory implementation side. On the computation side, precomputed trajectories, and closed-form approximated solutions are investigated.
This paper presents a comprehensive study of various sources of methane emissions, assess the impact of each source on emissions, and their dependency to throughput, time, and events. The analysis builds upon prior work  positing that a cause-based, marginal approach to estimating methane emission impacts of change in natural gas use was more accurate than assuming that methane emissions vary one-for-one with throughput. The results show that there are many components in the natural gas system that emit the same amount of methane to the atmosphere regardless of their operational mode; meaning some emissions sources have no or only partial dependence on throughput. As a result, reducing natural gas consumption in the future will not yield a directly proportional reduction in the methane emissions. The results of this study will be used in future works to build a model using the marginal emission methodology to estimate the change in methane emissions of natural gas systems as system throughput changes. It is believed that the results of this study will help energy policymakers to understand better the effect of policies aimed at reducing natural gas use on greenhouse gas (GHG) emissions and where such policies should be applied.
The outcome of possible changes in interlinked industrial energy systems is hard to predict, especially in retrofit scenarios. This results in investment decisions under uncertainties. In this paper, a new combined optimization approach is presented, that aims to support decision-making in these cases. The approach links models for optimal design of supply systems and heat exchanger networks with operational constraints and is specifically designed for retrofit applications. It is formulated as one combined mixed integer linear programming (MILP) problem. The results of the presented approach are demonstrated using a case study representing a typical industrial process. The optimal solution shows a cost-effective way for a transition to more efficient use of energy and an increased share of renewable sources.
CO2 Plume Geothermal (CPG) is a carbon neutral renewable electricity generation technology where geologic CO2 is circulated to the surface to directly generate power and then is reinjected into the deep subsurface. In contrast to traditional water geothermal power generation with an Organic Rankine Cycle (ORC), CPG has fewer system inefficiencies and benefits from the lower viscosity of subsurface CO2 which allows power generation at shallower depths, lower temperatures, and lower reservoir transmissivities.
In this paper, we modify our existing geothermal electricity models by: 1) replacing TOUGH2 reservoir simulations with analytic solutions for a 5-spot reservoir impedance, and 2) including heat loss to the surrounding rock using a semi-analytical heat transfer solution. We report the results of 3050 simulations in a single plot, showing the power generation of both direct CPG systems and indirect water geothermal systems for depths between 1 and 7 km and reservoir transmissivities between 102 and 105 mD-m (10-13 and 10-10 m3).
This work aims to study and analyze the potential and impact on CO2 avoided emissions from now to 2030, of scaling up an innovative hybrid solar technology to satisfy the energy demand of the Food and Beverage (F&B) industrial sector in Mexico. Positive and negative economic growth (including the recession caused by the COVID-19 pandemic) is considered as well as three technological scenarios: large, medium, and short technology deployment (BTD, MTD, and LTD). The study is based on a top-down and a button-up approach to determine F&B sector final energy consumption growth and share of primary and secondary energy sources. The results show that the proposed LCPV/T technology has an excellent decarbonization potential in the studied sector, achieving GHG emissions reduction of up to 51.7% and 34.10% from the levels of 2018 for the negative and positive economic growth, respectively.
Optimal design and control are coupled tasks for enhancement of energy system performances. Model based dynamic optimization can enhance system performances significantly. However, it is difficult to obtain the optimal structure and control solution for a complicated nonlinear physical system model simultaneously, thus we proposed a coupled dynamic optimization method for structure and control co-optimization and implement it successfully for a transcritical CO2 ejector expansion heat
pump coupled with hot and cold thermal energy storages during energy charging process. A complicated nonlinear dynamic system model with genetic algorithm were used to obtain the structure and control co-optimization solution of the coupled system during energy charging. The structures of gas cooler, evaporator, hot and cold thermal storage tanks were optimized, and dynamic optimal control strategy was obtained for energy charging process. Compared to the constant control parameter strategy, the overall coefficient of performance can be increased by 21.1%. The performances can be enhanced more significantly if the water temperature at the hot tank outlet remains at a low level, i.e. the charging time reduced. This study would be helpful for a structure and control co-optimization of other dynamic energy systems.
According to the Financial Times the steel industry emissions accounted for 7-9% of total GHG emissions worldwide in 2019. The main share is directly related to the use of fossil coke and coal as fuels and reducing agents. About four solutions can be adopted to address such issue: direct reduction with hydrogen or syngas, electric arc furnaces, carbon capture and storage and use of biofuels (so-called “biocarbon”). These solutions can also be integrated. We propose applying innovative methods to produce biocarbon by pelletizing biocarbon with pyrolysis oil and reheating it at high temperatures to obtain materials with sufficient hardness, reduced porosity and reduced reactivity. The upgrade takes biocarbon closer to the requirements usually applied to metallurgical coke. We present also the results of technical and economic analysis plus environmental analysis on the expected final use of biocarbon in steel industry.
An H-channel microfluidic device system was built to simulate the dead-end structures in subsurface environments. The accessibility to these restriction regions where significant amount of oil and contaminants may be trapped is challenging. Hence, large amount of unnecessary chemicals might be required for remediation and enhanced oil recovery (EOR) applications, making the process expensive and environmentally unfavorable. In this work, we demonstrate the ability of salinity gradients that naturally exist in the subsurface environment to target-migrate nano-capsules in porous and fractured rock formations. Our results demonstrate the concept and provide evidence of the potential of utilizing existing chemical and thermal gradients to enable autonomous and sustainable migration of nano-capsules into constricted regions in the subsurface environment for more efficient and environmentally-friendly subsurface remediation and energy harvesting applications.