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Integrated Optimization of Location, Design, and Operation of Renewable Energy Systems for Urban Microgrids


Integrated Optimization of Location, Design, and Operation of Renewable Energy Systems for Urban Microgrids

Shirzadi, Navid (2023) Integrated Optimization of Location, Design, and Operation of Renewable Energy Systems for Urban Microgrids. PhD thesis, Concordia University.

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The building sector of urban areas plays a crucial role in carbon emissions and climate change. Distributed generation using clean energies could help to reduce emissions. Furthermore, urban microgrids increase the reliability of power supply since most of the power outages are created in the grid distribution system and transmission lines. However, a cost-effective design and operation of an urban microgrid poses challenges, such as limited space for installing the renewable components, especially in populated areas, the uncertainty of renewable resources, and the resiliency of the designed microgrid in case of not having access to the central grid. Therefore, this thesis was initiated with the objective of developing a comprehensive method for the efficient design of an urban microgrid. The developed framework consists of three main modules. The first module aims at designing an energy system for a community microgrid by sizing and finding the optimum configuration of the energy system. To resolve the spatial issue problem in urban areas, regional renewable generation is proposed in this research where clean energy is produced outside of the populated area as a virtual power plant in relation to the microgrid. A mapping model is also developed to select the best location for installing components outside the microgrid. The mapping model is connected with the optimization model to automatically generate the best configuration and location of regional generation based on several aspects of each zone. The second module deals with renewable resources and electrical load demand uncertainties and tries to reduce them by forecasting strategies. Since renewable resources such as solar irradiance and wind speed are not predictable using just historical data, hybridized numeric weather prediction (NWP) and deep learning models are offered to tackle the drawback. The last module proposes a solution to ensure resilience against power supply failures in electricity grids caused by extreme weather conditions, unavailability of generation capacities, and transmission components problems.
The discussed models were applied to one of Concordia University's largest buildings in downtown Montreal, Canada. The results show a significant improvement in the environmental aspect of the regional generation if the existing gas boiler would be substituted by electric boilers and heat pumps (using generated renewable electricity outside of microgrid), preventing emissions of about 4233 tons CO2 and 5.3 tons NOX per year. Using a proposed tariff structure beneficial to both the customer and utility, the resulting levelized cost of energy is about 5.3 Cents per kilowatt hour, i.e., lower than the current rate of about 6 Cents per kWh. Using the second module’s proposed hybrid models for renewable resources and electrical load demand prediction of the case study was also helpful by considerably bringing down the error. Finally, operation dispatch scenarios are developed to reinforce the system’s resiliency in severe conditions for the case study in the third module. A mixed-integer linear programming (MILP) approach is employed to identify global optimum dispatch solutions based on the next 48 h plans for different seasons to formulate a whole-year operational model. The results show that the loss of power supply probability (LPSP), as an indicator of resiliency, could be lowered to near zero while minimizing operational cost using the proposed optimal load (derived from critical load).

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Building, Civil and Environmental Engineering
Item Type:Thesis (PhD)
Authors:Shirzadi, Navid
Institution:Concordia University
Degree Name:Ph. D.
Program:Civil Engineering
Date:6 February 2023
Thesis Supervisor(s):Eicker, ursula and nasiri, fuzhan
ID Code:991953
Deposited By: Navid Shirzadi
Deposited On:21 Jun 2023 14:42
Last Modified:21 Jun 2023 14:43
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