scholarly journals Interaction between atmospheric model resolution and energy system model topology

2021 ◽  
Author(s):  
Matthias Zech ◽  
Oriol Raventós ◽  
Ontje Lünsdorf ◽  
Lueder von Bremen
Keyword(s):  
Renewable Energy ◽  
Energy System ◽  
Atmospheric Model ◽  
System Model ◽  
Weather Data ◽  
Model Resolution ◽  
Energy System Model ◽  
Grid Points ◽  
The Impact ◽  
Model Topology

<p>With the increasing penetration of renewable energy capacities in the European energy system, the electricity generators have shifted from centralized power plants to decentralized, weather-dependent wind turbines and photovoltaic systems. Energy system models now rely on skillful weather data to estimate renewable energy feedins on electricity bus levels. These feedins are usually calculated by bilinearly interpolating the closest atmospheric model grid points to the electricity network bus locations. This comes to the cost of averaging multiple atmospheric model grid points reducing overall atmospheric model variability. In addition, electricity grids are often modeled in clustered representations in terms of number of lines and buses. The number of buses is usually much smaller than the number of atmospheric model grid points and therefore some grid points and their characteristics may not be taken into account in highly clustered networks. So far, this interconnection between atmospheric model resolution and electricity grid topology has not been widely investigated.</p><p>This study approaches the question if and to what extent the atmospheric model resolution affects the energy system model results. The regional reanalysis COSMO-REA6 is used as a reference data and its resolution is artificially reduced. This allows to compare the loss of information (mainly variability) due to a lower grid point resolution. The weather data is then used within different energy system network topologies to determine the corresponding renewable energy feedins at bus levels. A subsequent optimal power flow model estimates the impact on energy system metrics as storage usage and economic dispatch costs to further understand the relationship between atmospheric model resolution and energy system model topology. This study provides useful insights to choose the appropriate resolution of the atmospheric model input given an energy system model. </p>

scholarly journals Novel Energy System Design Workflow for Zero-Carbon Energy District Development

2021 ◽  
Vol 3 ◽  
Author(s):  
Bahador Samadzadegan ◽  
Soroush Samareh Abolhassani ◽  
Sanam Dabirian ◽  
Saeed Ranjbar ◽  
Hadise Rasoulian ◽  
...  
Keyword(s):  
Renewable Energy ◽  
Energy Systems ◽  
Energy System ◽  
Building Energy ◽  
System Model ◽  
Energy System Model ◽  
Heating And Cooling ◽  
Urban Energy ◽  
Zero Carbon

The growing urban population globally leads to higher greenhouse gas (GHG) emissions and stress on the electricity networks for meeting the increasing demand. In the early urban design stages, the optimization of the urban morphology and building physics characteristics can reduce energy demand. Local generation using renewable energy resources is also a viable option to reduce emissions and improve grid reliability. Notwithstanding, energy simulation and environmental impact assessment of urban building design strategies are usually not done until the execution planning stage. To address this research gap, a novel framework for designing energy systems for zero-carbon districts is developed. An urban building energy model is integrated with an urban energy system model in this framework. Dynamic prediction of heating and cooling demand and automatic sizing of different energy system configurations based on the calculated demands are the framework's primary capabilities. The workability of the framework has been tested on a case study for an urban area in Montreal to design and compare two different renewable energy systems comprising photovoltaic panels (PV), air-source, and ground source heat pumps. The case study results show that the urban building energy model could successfully predict the heating and cooling demands in multiple spatiotemporal resolutions, while the urban energy system model provides system solutions for achieving a zero-carbon or positive energy district.

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