Session: 08-03-01: 4E Analysis and Optimization of Thermodynamic Systems

Paper Number: 68722

Start Time: Wednesday, 05:40 PM

68722 - Sizing Optimization of District Energy Systems Considering Meteorological, Demand, and Electricity Emissions Uncertainties 

The increase in greenhouse gas emissions has catastrophic effects on human health, the ecosystem, and society. As a result, regions have developed emission mitigation policies to reduce emissions caused by human activities. One of the emission reduction plans by the United Nations Environment Programme is using district energy systems connected to renewables.

District energy systems can provide cooling, heating, and/or electricity needs of regions. They can be connected to different energy components to generate heating and/or electricity in a centralized location and distribute them to buildings.

Many studies have performed sizing optimization of district energy systems in the literature. Most of the studies neglect the changes in solar irradiance, wind speed, energy demands of buildings, and emissions associated with purchasing electricity from the grid over district energy systems' lifespan. However, weather parameters, energy demands, and emissions associated with purchasing electricity may change over the 30 to 40 years of operating a district energy system, and these changes can affect energy components' sizing in a district energy system.

Two-stage stochastic programming has been used to optimize the design of district energy systems considering uncertainties in the literature. Two-stage stochastic programming splits decision variables into two groups. The first group of decision variables are related to the design of district energy systems and are made before realizing uncertainties. The second group of decision variables are related to the operation planning of district energy systems and are made after realizing uncertainties.

In this study, a framework is developed to perform a two-stage stochastic multi-objective optimization of energy components' sizing to minimize cost and emissions for district energy systems considering weather intermittency, changes in energy demands, and changes in electricity emissions. A group of buildings at the University of Utah is used as the case study to test the optimization framework. This framework considers wind turbines, solar photovoltaics, grid, and combined heating and power system to provide the electricity demand of buildings. Heating demands are provided using natural gas boilers and the combined heating and power system.

This study is novel by forming an open-source framework, considering electricity emissions with more details compared to previous studies in the literature, and performing the optimization for a campus in the U.S.

In this study, scenarios are generated to show the weather intermittency, changes in the energy demands, and changes in the electricity emissions over a district energy system's lifespan.  To reduce the number of scenarios and computation time of optimization, principal component analysis and k-medoid clustering algorithm are employed as a scenario-reduction technique. Then, these reduced scenarios are used in the two-stage stochastic optimization of a district energy system, which is optimized using a non-dominated sorting genetic algorithm II. 

This study's final result is a Pareto front that represents the trade-off between the overall cost and emissions when the sizing of district energy systems is optimum to minimize cost and emissions considering the weather intermittency, changes in energy demands, and changes in electricity emissions.

Presenting Author: Zahra Ghaemi University of Utah

Authors:

Zahra Ghaemi University of Utah
Thomas T. D. Tran Indiana Tech
Amanda D. Smith University of Utah / Pacific Northwest National Laboratory