Dasaraden Mauree

Evaluating Error Propagation in Coupled Land-Atmosphere Models

AbstractThis study examines how land-use errors from the Land Transformation Model (LTM) propagate through to climate as simulated by the Regional Atmospheric Model System (RAMS). The authors conducted five simulations of regional climate over East Africa: one using observed land cover/land use (LULC) and four utilizing LTM-derived LULC. The study examined how quantifiable errors generated by the LTM impact typical land–climate variables: precipitation, land surface temperature, air temperature, soil moisture, and latent heat flux. Error propagation was not evident when domain averages for the land–climate variables of the yearlong simulation were examined. However, the authors found that spatial errors from the LTM propagate through in complex ways, temporally affecting the seasonal distributions of rainfall, surface temperature, soil moisture, and latent heat flux. In particular, rainy seasons exhibited greater precipitation in LTM-RAMS simulations than in the reference simulation and less precipitation...

Multi-scale modelling to evaluate building energy consumption at the neighbourhood scale

A new methodology is proposed to couple a meteorological model with a building energy use model. The aim of such a coupling is to improve the boundary conditions of both models with no significant increase in computational time. In the present case, the Canopy Interface Model (CIM) is coupled with CitySim. CitySim provides the geometrical characteristics to CIM, which then calculates a high resolution profile of the meteorological variables. These are in turn used by CitySim to calculate the energy flows in an urban district. We have conducted a series of experiments on the EPFL campus in Lausanne, Switzerland, to show the effectiveness of the coupling strategy. First, measured data from the campus for the year 2015 are used to force CIM and to evaluate its aptitude to reproduce high resolution vertical profiles. Second, we compare the use of local climatic data and data from a meteorological station located outside the urban area, in an evaluation of energy use. In both experiments, we demonstrate the importance of using in building energy software, meteorological variables that account for the urban microclimate. Furthermore, we also show that some building and urban forms are more sensitive to the local environment.

On the Coherence in the Boundary Layer: Development of a Canopy Interface Model

A 1D Canopy Interface Model (CIM) is developed to act as an interface between a meso-scale and a micro-scale atmospheric model and to better resolve the surface turbulent fluxes in the urban canopy layer. A new discretisation is proposed to solve the TKE equation finding solutions that remain fully concordant with the surface layer theories developed for neutral flows over flat surfaces. A correction is added in the buoyancy term of the TKE equation to improve consistency with the Monin-Obukhov surface layer theory. Obstacles of varying heights and dimensions are taken into account by introducing specific terms in the equations and by modifying the mixing length formulation in the canopy layer. The results produced by CIM are then compared with wind and TKE profiles simulated with a LES experiment and results obtained during the BUBBLE meteorological intensive observation campaign. It is shown that the CIM computations are in good agreement with the results simulated by the LES as well as the measurements from BUBBLE. The applicability of the correction term in an urban canopy layer and to further validate CIM in multiple stability conditions and various urban configurations is discussed.

Development of a multi-scale meteorological system to improve urban climate modeling

This study consisted in the development of a canopy model (CIM), which could be use as an interface between meso-scale models used to simulate urban climate and micro-scale models used to evaluate building energy use. The development is based on previously proposed theories and is presented in different atmospheric conditions, with and without obstable. It has been shown, for example, that to be in coherence with the Monin-Obukhov Similarity Theory, that a correction term has to be added to the buoyancy term of the T.K.E. CIM has also been coupled with the meteorological meso-scale model WRF. A methodology was proposed to take advantage of both models (one being more resolved, the other one integrating horizontal transport terms) and to ensure a coherence of the results. Besides being more precise than the WRF model at the same resolution, this system allows, through CIM, to provide high resolved vertical profiles near the surface.

Optimum design and control of grid integrated electrical hubs considering lifecycle cost and emission

Grid connected renewable energy systems are becoming popular due to reasons such as rapid escalation of energy prices, depletion of fossil fuel resources and pollutant emitted by conventional energy sources. Therefore, technologies for incorporating renewable energy technologies into the existing electricity grid needs to be researched more considering the changes in grid architecture. This study presents a novel method for optimum design and control of an Electric-Hub (EH) which consist of Solar PV panels, wind turbines, battery bank operating in a grid (low voltage) integrated mode. This study reports the simulation based optimization algorithm developed to obtain optimum system configuration and operation strategy considering two conflicting objectives; i.e. Levelized Energy Cost (LEC) and Leveliyed CO2 emission (LCO2). A detail energy flow model is developed to evaluate energy flow through wind turbines and SPV panels on hourly basis. Interaction with the battery bank and the Low-Voltage Grid (LVG) is determined using an expert system. Operating state of the system is determined based on renewable energy generation, Cost of Electricity (COE) in the LVG, state of charge of the battery bank. Subsequently, operating states of the expert system and configuration of the EH; i.e. type and capacity of SPV panels, wind turbines and battery bank is optimized using steady state ε-multi objective optimization technique. Seven Pareto solutions are selected at the end and analyzed the system configuration and control strategy.

Design Optimization of Electrical Hubs Using Hybrid Evolutionary Algorithm

Integration of non-dispatchable renewable energy sources such as wind and solar into the grid is challenging due to the stochastic nature of energy sources. Hence, electrical hubs (EH) and virtual power plants that combine non-dispatchable energy sources, energy storage and dispatchable energy sources such as internal combustion generators and micro gas turbines are getting popular. However, designing such energy systems considering the electricity demand of a neighborhood, curtailments for grid interactions and real time pricing (RTP) of the main utility grid (MUG) is a difficult exercise. Seasonal and hourly variation of electricity demand, potential for each nondispatchable energy source and RTP of MUG needs to be considered when designing the energy system. Representation of dispatch strategy plays a major role in this process where simultaneous optimization of system design and dispatch strategy is required. This study presents a bi-level dispatch strategy based on reinforced learning for simultaneous optimization of system design and operation strategy of an EH. Artificial Neural Network (ANN) was combined with a finite state controller to obtain the operating state of the system. Pareto optimization is conducted considering, lifecycle cost and system autonomy to obtain optimum system design using evolutionary algorithm.

Integration of outdoor human comfort in a building energy simulation database using CityGML Energy Ade

Handling data needed by Building Energy Simulation (BES) tools can be a tedious task, especially at the urban scale. Besides BES, users often have different needs (building energy use, human comfort, integration of renewables, urban planning…) in mind when using simulation tools, but often have access to the same dataset. To simplify and harmonize the process of obtaining a homogeneous dataset, we make use of a PostgreSQL database in the CityGML file format using the Energy Application Domain Extension (ADE), which can be accessed remotely to retrieve data. CityGML with Energy ADE is an open data model with the objective of having a common platform to store and exchange 3D information and energy data between municipalities, professionals and researchers. The structure of the CityGML covers the following modules: geometry, construction, occupancy and energy systems. However, in the CityGML structure an important parameter to describe the city livability is missing: the outdoor human comfort. Considering this, we propose to further develop the database, by adding outdoor human comfort parameters and results. A case study of the Ecole Polytechnique Federale de Lausanne (EPFL) campus will be set-up, stored in the database and simulated with the software CitySim. The resulting human comfort indices will further be sent back to the improved database for an offline analysis with GIS tools. With this new development, the CityGML with Energy ADE can benefit from information on the urban microclimate and its impact on people activities and wellbeing.

Multi-scale modelling to assess human comfort in urban canyons

As the impact of climate change progresses, heat waves are expected to increase significantly in the future. Coupled with the urban heat island effect, this will tend to have a major impact on the comfort of the inhabitants in urban areas. It is thus crucial to adopt the necessary sustainable measures and development scenarios to improve city liveability and human health. The main physical parameters that affect the outdoor human comfort are the air temperature, the relative humidity and the wind speed. Various tools, such as CFD or LES models, have been used in the past to evaluate these variables for the calculation of human comfort indices. These tools however are computationally too expensive and require extensive resources and data. Moreover, in our previous studies on the outdoor human comfort realized with the CitySim software, the meteorological variables were not linked to the urban form, geometry and roughness. To overcome these barriers, the CIM (Canopy Interface Model) was developed to calculate high-resolution vertical profiles of meteorological variables. The CitySim software to perform energy and temperature simulations then used these outputs. In this study, virtual pedestrians were located in two different areas of the EPFL campus, in Lausanne (Switzerland): a natural environment - characterized by clay soil and cherry trees - and an artificial environment, the new asphalt square near the SwissTech Convention Centre. The analysis carried out with the CitySim software compares the outdoor human comfort of pedestrian with the wind data from the traditional Meteonorm dataset, and the new CIM wind simulations. A sensitivity analysis of the results shows the difference between both simulations, quantifying the impact of the new wind model in the calculation of the indices.