This paper suggests a model for optimization of societal carbon footprints one person at a time through the decentralization of electricity use and accounting. Our model describes steps involved with developing a decentralized accounting system considering electricity as a “credit product”. While describing the basic characteristics of both schemes, we also emphasize capabilities of the proposed model for reducing carbon footprints from other societal choices, for example, purchasing water (energy-water nexus), managing waste, or designing sustainable transportation systems. A simple yet complex model involved with familiar societal financial systems’ rules and routines is proposed for achieving a resilient, sustainable, and prosperous future. The proposed model calls for creating a dynamic society (as a system) that can be efficiently adopted to take on challenges threatening the function, survival, and future developments of the societies.
The focus of this paper is on the estimation of building roof area for solar PV systems, potential solar PV installed capacity and power generation in Vasteras, a typical Swedish municipality. The following sectioning of different building types has been investigated: Residence, Industry, Social function, Business, Economy, Complement, and Others. In the following sections, an estimate of available roof area potential is calculated by considering factors such as rooftop orientation, shadows and obstacles. With appropriate rooftops covered with commercial solar cells, combined with the average solar irradiation, PV panel efficiencies and other system parameters, the total solar power potential PV peak power output from the region is considered and the potential annual energy production is calculated. This will give a picture of how much the electricity demand will be met by rooftop PV deployment. We get those new understanding of roof area distribution and potential outputs that can largely help solar energy policy formulation in the city.
Surface polaritonic modes like surface plasmon polaritons (SPPs) and surface phonon polaritons (SPhPs) at interfaces can significantly enhance near-field radiative transfer between nanostructured surfaces. In this work, we study the near-field heat transfer between graphene/SiC composite nanostructures. It is demonstrated that thermally excited SPPs and SPhPs in such composite nanostructures lead to a significant enhancement in nearfield heat transfer rate. To further analyze the underlying mechanisms, we calculate energy transmission coefficients and obtain the near-field dispersion relations. The dispersion relations of composite nanostructures are substantially different from those of isolated graphene or SiC films, which are due to the strong coupling effects between different polaritonic modes. We further identify four pairs of strongly coupled polaritonic modes with considerable Rabi frequencies, which mainly contribute to the enhancement in near-field heat transfer. This work provides a route to utilize strongly coupled surface polaritonic modes to manipulate near-field heat transfer, which has potential applications in waste heat recovery and heat management.
A variety of energy-saving concepts that exploit the special characteristics of electric drives are introduced. The confluence of three emerging concepts in transportation, namely electric drives, autonomous driving, and networked vehicles, enables the optimization of transportation efficiency in a way that drastically changes modern transportation, especially for passenger and commercial road vehicles. The paper addresses both, urban as well as highway driving situations and the associated optimization problems. It is shown that if the only term in the cost function is transportation energy, and all other conditions are formulated as constraints, quite substantial energy cost reductions are possible. Exploiting weather and environmental conditions is another important topic that is analyzed. The paper illustrates key ideas via several simulation examples.
This paper presents an innovative hybrid and compact organic Rankine cycle (ORC) system for micro combined heat and power utilizing solar thermal energy and natural gas. The ORC investigated in this work consists of heat sources, an evaporator, a scroll expander, a condenser, a fluid pump, a recuperator, a control unit and solar heater simulator. The experimental ORC unit has an electric power capacity of 1100 We. Its advantages include short start-up time, clean and automatic operation, low maintenance and very low noise level. It is well suited for hybrid solar and natural gas energy systems. A mathematical model was established to assess the thermodynamic performance of the ORC system under various conditions. Power output and thermal efficiency were calculated using the established model.
Refrigeration is a new field for wave rotor technology in recent years. Unsteady condensation in the wave rotor, which could affect the performance of the machine, is still a challenge. In this paper, the movement of condensed droplets in the wave rotor is investigated first and the existence of evaporation, which is responsible for the low efficiency of the wave rotor, is confirmed. Then characteristics of the flow field in the wave rotor involving condensation are obtained by numerical simulation using a condensation model that describes both nucleation and growth of droplets and an evaporation model that computes growth of droplets reversely. Finally, two important operating parameters, pressure and relative humidity of high-pressure inlet, are investigated to find out their influences towards the refrigeration performance under different condensation conditions by combining experiment results and numerical simulation. The results of this paper could help better understand unsteady condensation in wave rotor and improve design accuracy significantly.
This work presents a non-equilibrium kinetic model to characterize foamy oil and gas/oil two-phase flow in heavy oil and propane system from pressure depletion tests. Good agreement between experiments data and simulation results are obtained in terms of production data as well as pressure distribution. The following parameters are tuned in the history match process, including k values, gas-liquid relative permeability curves, and reaction frequency factors. The simulation results suggest that bubbles pass through pore throat smoothly and have low dissolve rate in oil phase at low pressure drop rate, which results in high gas recovery factor and low oil recovery factor. Gas bubbles expand to a larger size and block the pore throat when increasing pressure drop rate to intermediate pressure depletion rate. At this range of pressure drop rate, foamy oil and gas/oil flow characterization is influenced by both gas bubbles evolve and dissolve process, which results in low gas recovery and high oil recovery. Continue to increase the pressure drop rate could cause gas bubbles to evolve faster than dissolve back and shorten production period, which results in a relatively low gas recovery as well as low oil recovery. The simulation work presented in this paper successfully characterized foamy oil behavior in the porous media for heavy oil/propane system. The innovative methodology presented in this work could be used as a general method to characterize foamy oil flow in heavy oil/propane system.
The grid is getting complex with the addition of DERs and storage at the edge. The edge management needs to be easy and intuitive. At EQuota, we develop an analytics-driven and smart-edge-device-enabled digital solution. Compared with current technologies or practices, our solution is able to sense better, think smarter and act with ease to allow high level of scalability by Plug & Play IoT devices and analytics-enabled operation flexibility, operation optimization accommodating constraints, and market dynamics.
Policy makers are tasked with selecting, designing, and implementing policies to support the transition to a sustainable power system. As part of the task they often turn to models to quantify and compare the options available to them. In this work we investigate the importance of representing a wide range of economic and physical sources of uncertainty in the modelling used to evaluate different decarbonization policies. We investigate six different energy policies across three different methods for incorporating uncertainty into decision making models in a Portugal based case study. We find that the method for incorporating uncertainty into the model used to evaluate policies leads to differences in the resulting capacity expansion plan, in the ability to meet carbon intensity targets, and in the abatement costs of policies. Policies designed in a deterministic way can result in significant violations of the emission target and the expected costs be more than double those estimated in a deterministic way. We also find that the six policies appear roughly equivalent when analysed by the deterministic model but perform very differently when uncertainty is considered potentially biasing decision makers that ignore uncertainty. Finally, we demonstrate that the simplified inclusion of uncertainty, such as scenario analysis, often underestimates the carbon reduction effect of policies and can over or under estimate costs.