scholarly journals Effects of Occupants and Local Air Temperatures as Sources of Stochastic Uncertainty in District Energy System Modeling

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2295
Author(s):  
Martín Mosteiro-Romero ◽  
Arno Schlueter
Keyword(s):  
System Modeling ◽  
Urban Climate ◽  
Energy System ◽  
Measured Temperature ◽  
Weather Station ◽  
Stochastic Uncertainty ◽  
Heating And Cooling ◽  
Air Temperatures ◽  
Energy System Modeling

Input uncertainty is one of the major obstacles urban building energy models (UBEM) must tackle. The aim of this paper was to quantify the effects of two of the main sources of stochastic uncertainty, namely building occupants and urban microclimate, on electrical and thermal supply system sizing at the district scale. In order to analyze the effects of the former, three different methods of occupant modeling were implemented in a UBEM. The effects of the urban heat island on system sizing were studied through the use of measured temperature data from a weather station in the case study district compared to measured data from a national weather station. The methods developed were used to assess the sizing and costs of centralized and decentralized technologies for a case study in central Zurich, Switzerland. The choice of occupant modeling approach was found to affect the district’s total annualized costs for space heating and cooling by ±5%, whereas for the costs of electricity the variation was ±8%. Regarding outdoor temperature, the effects on the heating demands proved be negligible, however the costs of the cooling alternatives were found to vary by about 4% at the district scale due to the effect of urban climate, for individual buildings this deviation was as high as 40%.

scholarly journals Quantifying the impact of urban climate by extending the boundaries of urban energy system modeling

2018 ◽  
Vol 222 ◽  
pp. 847-860 ◽  
Author(s):  
A.T.D. Perera ◽  
Silvia Coccolo ◽  
Jean-Louis Scartezzini ◽  
Dasaraden Mauree
Keyword(s):  
System Modeling ◽  
Urban Climate ◽  
Energy System ◽  
Urban Energy ◽  
Energy System Modeling ◽  
The Impact

scholarly journals Open Source Energy System Modeling Using Break-Even Costs to Inform State-Level Policy: A North Carolina Case Study

2019 ◽  
Vol 54 (2) ◽  
pp. 665-676 ◽  
Author(s):  
Binghui Li ◽  
Jeffrey Thomas ◽  
Anderson Rodrigo de Queiroz ◽  
Joseph F. DeCarolis
Keyword(s):  
North Carolina ◽  
Open Source ◽  
System Modeling ◽  
State Level ◽  
Energy System ◽  
Energy System Modeling

scholarly journals Understanding Technology, Fuel, Market and Policy Drivers for New York State’s Power Sector Transformation

2020 ◽  
Vol 13 (1) ◽  
pp. 265
Author(s):  
Mine Isik ◽  
P. Ozge Kaplan
Keyword(s):  
New York ◽  
Electricity Generation ◽  
System Modeling ◽  
New York State ◽  
Clean Energy ◽  
Energy System ◽  
Offshore Wind ◽  
Power Sector ◽  
Reduce Emissions ◽  
Energy System Modeling

A thorough understanding of the drivers that affect the emission levels from electricity generation, support sound design and the implementation of further emission reduction goals are presented here. For instance, New York State has already committed a transition to 100% clean energy by 2040. This paper identifies the relationships among driving factors and the changes in emissions levels between 1990 and 2050 using the logarithmic mean divisia index analysis. The analysis relies on historical data and outputs from techno-economic-energy system modeling to elucidate future power sector pathways. Three scenarios, including a business-as-usual scenario and two policy scenarios, explore the changes in utility structure, efficiency, fuel type, generation, and emission factors, considering the non-fossil-based technology options and air regulations. We present retrospective and prospective analysis of carbon dioxide, sulfur dioxide, nitrogen oxide emissions for the New York State’s power sector. Based on our findings, although the intensity varies by period and emission type, in aggregate, fossil fuel mix change can be defined as the main contributor to reduce emissions. Electricity generation level variations and technical efficiency have relatively smaller impacts. We also observe that increased emissions due to nuclear phase-out will be avoided by the onshore and offshore wind with a lower fraction met by solar until 2050.

scholarly journals Time series of heat demand and heat pump efficiency for energy system modeling

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Oliver Ruhnau ◽  
Lion Hirth ◽  
Aaron Praktiknjo
Keyword(s):  
Time Series ◽  
System Modeling ◽  
Energy System ◽  
Heat Pumps ◽  
Reanalysis Data ◽  
Coefficient Of Performance ◽  
Pump Efficiency ◽  
Water Heating ◽  
Heat Demand ◽  
Energy System Modeling

Abstract With electric heat pumps substituting for fossil-fueled alternatives, the temporal variability of their power consumption becomes increasingly important to the electricity system. To easily include this variability in energy system analyses, this paper introduces the “When2Heat” dataset comprising synthetic national time series of both the heat demand and the coefficient of performance (COP) of heat pumps. It covers 16 European countries, includes the years 2008 to 2018, and features an hourly resolution. Demand profiles for space and water heating are computed by combining gas standard load profiles with spatial temperature and wind speed reanalysis data as well as population geodata. COP time series for different heat sources – air, ground, and groundwater – and different heat sinks – floor heating, radiators, and water heating – are calculated based on COP and heating curves using reanalysis temperature data. The dataset, as well as the scripts and input parameters, are publicly available under an open source license on the Open Power System Data platform.

scholarly journals Beyond the Energy System: Modeling Frameworks Depicting Distributional Impacts for Interdisciplinary Policy Analysis

2020 ◽  
Vol 9 (1) ◽  
pp. 2000668
Author(s):  
Roland Cunha Montenegro ◽  
Panagiotis Fragkos ◽  
Audrey Helen Dobbins ◽  
Dorothea Schmid ◽  
Steve Pye ◽  
...  
Keyword(s):  
Policy Analysis ◽  
System Modeling ◽  
Energy System ◽  
Energy System Modeling ◽  
Distributional Impacts

scholarly journals Semi-Markov and hidden semi-Markov models of energy systems

2018 ◽  
Vol 58 ◽  
pp. 02023 ◽  
Author(s):  
Yuriy E. Obzherin
Keyword(s):  
Control Systems ◽  
Intelligent Control ◽  
Markov Models ◽  
System Modeling ◽  
Energy Systems ◽  
Energy System ◽  
Information Control ◽  
Efficiency Theory ◽  
Energy System Modeling ◽  
Semi Markov Processes

The problem of information control systems creation for energy systems and transition to intelligent control and engineering is one of the important problems of reliability and efficiency theory for energy systems. The solution of this problem is possible based on construction of mathematical models concerning different aspects of these systems structure and operation. The possibilities of application of semi-Markov processes with common phase space of states, hidden Markov and semi-Markov models for energy system modeling are considered in the paper.

An Agent Based Simulation for Building Energy System Modeling

2010 ◽  
Author(s):  
Mengqi Hu ◽  
Jin Wen ◽  
Fan Li ◽  
Moeed Haghnevis ◽  
Yasaman Khodadadegan ◽  
...  
Keyword(s):  
System Modeling ◽  
Storage System ◽  
Energy System ◽  
Energy Storage System ◽  
Building Energy ◽  
Agent Based Simulation ◽  
Agent Based ◽  
Time Price ◽  
Building Energy System ◽  
Energy System Modeling

Extensive research has been done on the centralized building energy system modeling and simulation. However the centralized structure is limited to study and simulate the energy interaction between different buildings at different locations. This paper reviews the building energy consumption model, energy storage system and energy generation system in the Net-zero buildings. Incorporate with the real-time price rate model, this paper develops an agent based simulation framework for distributed building energy system under uncertainty. Each sub system is developed as an agent in the simulation model, and a virtual decision agent is designed to simulate the operation strategy. The energy flow between different agents can be easily monitored from the simulation. The differences between on-peak and off-peak control are demonstrated from the simulation result.

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