Browsing by Author "Shotorbani, AM"

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item: Article-Full-text
Community-level decentralized energy system planning under uncertainty: A comparison of mathematical models for strategy development
(Elsevier, 2021) Prabatha, T; Karunathilake, H; Shotorbani, AM; Sadiq, R; Hewage, K
Distributed energy systems renewable energy are one solution to the environmental and economic concerns of energy use. While energy planning and optimization have been conducted mainly as a mathematical exercise, practical approaches that incorporate the engineering realities and uncertainties are limited. Decision makers find challenges in community energy planning due to the lack of expertise, planning tools, and information. While a multitude of models and tools are currently available, there are no means of identifying the most appropriate or accurate methods, especially considering uncertainty. The main objective of this study is to compare and identify the strengths and limitations of various mathematical modelling techniques used in energy planning for grid connected renewable energy systems. As a case study demonstration, different multi-objective optimization techniques with and without uncertainty consideration (i.e. robust optimization, linear optimization, Taguchi Orthogonal Array method, and Monte Carlo simulation) were applied on a selected neighborhood in British Columbia. The optimization outcomes and the time and effort for evaluation were compared for the different methods. The findings indicate that robust optimization can be used to develop an uncertainty-based decision model. It significantly reduces evaluation time compared to the other methods. Although the presence of uncertainties can change the optimal configuration of a planned energy system, the assessment method itself does not significantly impact the outcomes. The findings of this study will enable the energy planners and researchers to compare different multi-objective optimization techniques, and to select the best for planning renewable energy projects, especially during the pre-project planning stage.
item: Article-Full-text
Mobile energy hub planning for complex urban networks: A robust optimization approach
(Elsevier, 2021) Khardenavis, A; Hewage, K; Perera, P; Shotorbani, AM; Sadiq, R
The electricity grid with a high penetration of renewable energy can enable travelers to travel free of emissions using state-of-the-art electric vehicles (EVs). Extensive electric vehicle demands at the peak-times, and an increase in electricity consumption due to population growth, have led to higher utility infrastructure investments. Mobile energy hubs i.e. clustered EVs parked in a dedicated location, can be used as an innovative demand-side management solution to reduce long-term utility infrastructure investments. They can store and release electricity to the grid based on consumer demand. However, a scientific planning approach for grid integration has been overlooked. Accordingly, this study proposes a comprehensive framework required to plan and develop mobile energy hubs based on optimization of life cycle cost, access distance and parking duration considering the temporal variation of EV recharging demands. The results of the study show that the framework developed can minimize lifecycle costs, and improve infrastructure utilization by accounting for the interests of all stakeholders. The total cost with the proposed robust optimization model under uncertainties of 50% is lesser than the robust cost calculated from a scenario-based approach. Furthermore, the developed framework is useful for recharging infrastructure planners to devise the deployment schedules and attract investors based on the economic viability of the planned strategies.