Modelling and model assessment of grid based Multi-Energy Systems

Two main strategies should be implemented to decarbonise the energy sector: substituting fossil fuels with renewable energies, and increasing system efficiency. Both strategies pose challenges for today's energy systems and their operators, because renewable energy is mainly decentralized, not always predictable, and introduces a degree of volatility into grids. Multi-energy systems, which incorporate multiple energy sectors, allow flexibility options to be used across energy carriers and thus further increase system flexibility. In addition, these multi-energy systems can also improve the overall energy efficiency. They enable cascaded energy use and allow for seasonal storage between different energy carriers. A comprehensive system modelling framework should consider all profound interactions between relevant system control variables. The aim of this proposed paper is to show the correlation between major aspects of grid based MES and how they can be combined in a system modelling framework

Open-Source, Open-Architecture SoftwarePlatform for Plug-InElectric Vehicle SmartCharging in California

This interdisciplinary eXtensible Building Operating System–Vehicles project focuses on controlling plug-in electric vehicle charging at residential and small commercial settings using a novel and flexible open-source, open-architecture charge communication and control platform. The platform provides smart charging functionalities and benefits to the utility, homes, and businesses.This project investigates four important areas of vehicle-grid integration research, integrating technical as well as social and behavioral dimensions: smart charging user needs assessment, advanced load control platform development and testing, smart charging impacts, benefits to the power grid, and smart charging ratepayer benefits

Assessing Evapotranspiration Estimates from the Global Soil Wetness Project Phase 2 (GSWP-2) Simulations

Abstract and PDF report are also available on the MIT Joint Program on the Science and Policy of Global Change website (http://globalchange.mit.edu/).We assess the simulations of global-scale evapotranspiration from the Global Soil Wetness Project Phase 2 (GSWP-2) within a global water-budget framework. The scatter in the GSWP-2 global evapotranspiration estimates from various land surface models can constrain the global, annual water budget fluxes to within ±2.5%, and by using estimates of global precipitation, the residual ocean evaporation estimate falls within the range of other independently derived bulk estimates. However, the GSWP-2 scatter cannot entirely explain the imbalance of the annual fluxes from a modern-era, observationally-based global water budget assessment, and inconsistencies in the magnitude and timing of seasonal variations between the global water budget terms are found. Inter-model inconsistencies in evapotranspiration are largest for high latitude inter-annual variability as well as for inter-seasonal variations in the tropics, and analyses with field-scale data also highlights model disparity at estimating evapotranspiration in high latitude regions. Analyses of the sensitivity simulations that replace uncertain forcings (i.e. radiation, precipitation, and meteorological variables) indicate that global (land) evapotranspiration is slightly more sensitive to precipitation than net radiation perturbations, and the majority of the GSWP-2 models, at a global scale, fall in a marginally moisture-limited evaporative condition. Finally, the range of global evapotranspiration estimates among the models is larger than any bias caused by uncertainties in the GSWP-2 atmospheric forcing, indicating that model structure plays a more important role toward improving global land evaporation estimates (as opposed to improved atmospheric forcing).NASA Energy and Water-cycle Study (NEWS, grant #NNX06AC30A), under the NEWS Science and Integration Team activities

System Thinking Approach to Increase Eco-Friendly Maize Production to Support Food Security

Food security is a high-priority issue for sustainable global agricultural development both quantitatively and qualitatively. Maize is one of the most important food crops in the world after rice and wheat. The biggest contribution of maize production in Indonesia comes from East Java Province, which is 25,60%. Maize production is changing due to increased consumer demand, increased input costs, concerns about food security, and environmental impacts. Climate conditions of marginal land areas (drought) and the use of chemical fertilizers continuously have negative impacts on the amount of production, food security, and soil quality (environment). This situation is dangerous because it will affect the income from farming. Based on these problems, in this study, a system dynamics prospective study will be conducted to develop a conceptual model (Causal Loop Diagram) to increase eco-friendly maize production to support food security. The results of the study are conceptual models that have some important information regarding internal and external factors that affect the productivity and production of eco-friendly maize. The conceptual model produced can be used by the government and stakeholders for decision-making in developing strategies and policies related to eco-friendly maize cultivation systems to support food security. Further research can be done by developing several scenarios to predict the state of the maize farming system in the future

Energy Policies for Passenger Motor Vehicles

This paper assesses the costs and effectiveness of several energy policies for light-duty motor vehicles in the United States, using the National Energy Modeling System (NEMS). The policies addressed are higher fuel taxes, tighter vehicle efficiency standards, and financial subsidies and penalties for the purchase of high- and low-efficiency vehicles (feebates). I find that tightening fuel-efficiency standards beyond those currently mandated through 2016, or imposing feebates designed to accomplish similar changes, can achieve by 2030 reductions in energy use by all light-duty passenger vehicles of 7.1 to 8.4 percent. A stronger feebate policy has somewhat greater effects, but at a significantly higher unit cost. High fuel taxes, on the order of $2.00 per gallon (2007$), have somewhat greater effects, arguably more favorable cost-effectiveness ratios, and produce their effects much more quickly because they affect the usage rate of both new and used vehicles. Policy costs vary greatly with assumptions about the reason for the apparent myopia commonly observed in consumer demand for fuel efficiency, and with the inclusion or exclusion of ancillary costs of congestion, local air pollution, and accidents.Fuel efficiency; Light-duty vehicles; Energy policy; Greenhouse gases; Feebate; Fuel tax

Impacts of plug-in hybrid vehicles and combined heat and power technologies on electric and gas distribution network losses

Distribution network operators (DNOs) require strategies that can offset the tradeoffs new embedded technologies have on their assets. This paper employs modelling to show that through control device manipulation, gas and electric (G&E) network operators can influence savings in energy losses under the presence of plug-in hybrid vehicles (PHEVs) and combined heat and power technologies (CHPs). An integrated gas and electric optimal power flow (OPF) tool is introduced to undertake various case studies. The OPF tool evaluates the technical impacts experienced in the networks when DNOs apply a "plug and forget" operation strategy and then compares the results against a "loss minimisation" strategy. Results show the benefits in applying different strategies are more considerable in electric networks than in gas networks. The study corroborates that an integrated G&E analysis offers a fresh perspective for stakeholders in evaluating energy service networks performance under different operation strategies

Modeling, Simulation and Optimization of Nuclear Hybrid Energy Systems Using OpenModelica and RAVEN

Nuclear hybrid energy systems (NHES) are a viable option to combine renewable energy sources, such as wind, with a less fluctuating energy source. Given recent development and their inherent safer and modular design, small modular reactors (SMRs), which are smaller versions of a nuclear reactor, can play an instrumental role in complementing renewables and supporting carbon-free power sectors in the coming decades. With increasing population and demand for clean water, Freeze desalination (FD), which uses freezing to separate water from salt, is a possible way to produce clean water while converting excess power from a SMR into stored thermal energy in an ice water tank. The stored thermal energy can then be used during peak hours to boost the power generation by improving the efficiency of the Rankine cycle of the SMR. Reverse osmosis (RO), which uses membrane and high water pressure to separate water from salt, is another possible way to use excess power generation to efficiently produce clean water. This paper uses OpenModelica, an open source software package, to model two types of NHES to produce clean energy and water, both powered by SMRs and wind turbines. The first system uses FD and the second system uses RO to generate clean water. RAVEN and TEAL, an economic analysis plugin for RAVEN, are used to optimize both systems for two case locations, Salt Lake City, Utah and San Diego, California. The results from the two cases show that for water prices less than $1.50 per m3, the FD system would be more economic. Since the RO system produced much more clean water, as water prices rise above$1.50 per m3, it becomes more advantageous to use the RO system, assuming that there are no negative impacts to increased water storage. The FD system is able to use the stored thermal energy to boost the power production by 12% during peak hours by increasing the efficiency of the Rankine cycle by 2%. This allows less capital investment on SMR/wind turbines, as well as less penalty due to mismatch of energy production and demand

Multi-Order Modeling of Linear Magnetic Motor System

Numerical simulations have been proven to be a powerful tool for predicting, testing, and validating the capabilities of new designs. However, given the high demand for simulating extremely complicated geometries and nonlinear physical phenomena, simulations can often be significantly time consuming. Consequently, the development of high-precision reduced-order models becomes indispensable to reduce computational time. In this study, we simplified and characterized an industrial motion system based on linear magnetic motor technology using accurate full 3-D numerical model. The system behavior was explored through various scenarios, including extreme conditions, to gain a deeper understanding of its thermal behavior during operation. The simulation results were then compared with experimental measurements. To achieve model order reduction, the initial and boundary conditions, along with temperature distributions derived from the simulation results, were translated into excitations and outputs for constructing robust reduced-order models. Subsequently, the reduced order model was thoroughly tested and validated against new scenarios derived from the 3-D simulation results

Artificial Intelligence in Process Engineering

In recent years, the field of Artificial Intelligence (AI) is experiencing a boom, caused by recent breakthroughs in computing power, AI techniques, and software architectures. Among the many fields being impacted by this paradigm shift, process engineering has experienced the benefits caused by AI. However, the published methods and applications in process engineering are diverse, and there is still much unexploited potential. Herein, the goal of providing a systematic overview of the current state of AI and its applications in process engineering is discussed. Current applications are described and classified according to a broader systematic. Current techniques, types of AI as well as pre- and postprocessing will be examined similarly and assigned to the previously discussed applications. Given the importance of mechanistic models in process engineering as opposed to the pure black box nature of most of AI, reverse engineering strategies as well as hybrid modeling will be highlighted. Furthermore, a holistic strategy will be formulated for the application of the current state of AI in process engineering