Track: Doctoral Dissertation Research Presentation Competition

Abstract

Abstract

Urban energy systems are responsible for the majority of the energy consumption around the world and they play an important role in energy issues such as economic security and climate change. Furthermore, there are growing concerns about the depletion of fossil fuel resources and the negative effect they have had on the ecosystem. To this end, a holistic and integrated approach is necessary for the optimal design and operation of urban energy systems that makes expressive betterment in energy efficiency and environmental impact. The aim of this research is to develop an optimum methodology for communities in the context of energy hub. The energy hub is a novel concept to systematically address energy requirements. Future of urban energy systems rely on the transition to cleaner fuels such as hydrogen and renewable energy sources like solar and wind. The energy hub concept has the potential to reduce the negative environmental impacts, by enabling the benefits of renewable power and clean energy technologies. The potential to connect different sectors of the urban community and its optimization is of particular interest in the energy hub network modeling concept as it will also lead to socio-economic and environmental benefits.

A general framework is developed in order to study the application of energy hubs and an associated network model to determine the optimal design and operation of the distributed energy systems (DES) in urban areas according to different competitive objectives. A novel multi-objective optimization approach based on the augmented epsilon constraint technique is employed to carry out this work.  Regarding the proposed framework, the analysis is carried out with the development of a multi period mixed integer multi objective optimization based model which is developed in General Algebraic Modeling Software (GAMS). In this work, the optimal configuration (type and number) and operation of DES are investigated in a network of energy hubs, where hubs can exchange the surplus energy between one another. As an illustrative example, the proposed model is applied to an urban area in the province of Ontario, Canada. Different scenarios are defined to investigate the effect of DES such as energy storage systems and network of energy on the configuration and operation of the system.

• The simultaneous consideration of DES, storage technologies and network of energy exchange between hubs (scenario 4) results in system autonomy and consequently, installation of more DES. Moreover, comparing the scenario results, considering the DES and energy exchange between hubs can lower the total annual cost of the system by 10%, while the greenhouse gas emission increases by 66% with respect to scenario 1(base case). Since the emission factor of the electricity grid is quite low in Ontario, employing more DES results in the increment of CO2 emission.
• The adoption of DES results in a significance increase of the natural gas consumption and reduction of electricity in different scenarios.
• It is worth noting that the consideration of storage systems (scenario2) and energy network (scenario3) have a positive influence on the adoption of the DES in the system.
• From an economic point of view, DES such as combined heat and power systems (CHP) based on gas engine (ICE)  and thermal energy storage  systems are the most suitable DES for adoption in the optimal system, while the renewable DES are not. 
• The contribution of DES in the optimal system configuration and operation decreases by increasing the significance of CO2 emission.  This occurs due to the lower emission factor of Ontario’s utility grid with respect to that of natural gas. It indicates that decision makers should take in to account the emission factors of the grid electricity prior to commitment to the DES cost.
• Conducting the sensitivity analysis show that doubling the electricity tariff rate results in 75% increase of cost, while an increase in price of natural gas has no significant effect on cost. It demonstrates that the cost is more sensitive to the electricity tariff rate rather than natural gas price for this specific case study.

Future work will extend the study to include sensitivity analysis jurisdictions with different emissions factors for the electricity grid, time of day emissions factors, as well as different thermal and electrical loads profiles based on different types of installations for the energy hubs.  This future work will include hydrogen as an energy vector, the ‘power to gas’ energy storage concept and facilities with hydrogen demand e.g., fleets of vehicles, or fork lifts.