Integrated Optimization of Location, Design, and Operation of Renewable Energy Systems for Urban Microgrids
Bibliographic record
Abstract
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. \nThe 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).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".