Technical-economic feasibility and simulation of ÉTS' electricity saving with photovoltaic system and batteries
Bibliographic record
Abstract
The goal of this Master’s thesis is to make a technical-economic feasibility study and a yearly simulation of École de Technologie Supérieure’s electricity expenses and savings with and without a Solar Photovoltaic System, Battery Energy Storage System, financial incentives or demand response, taking into account the Quebec and Ontario electricity rates in 2017. \n \nSix scenarios are proposed and simulated in a 5-minute interval with 2017 ÉTS power data, Montreal’s solar irradiance and temperature and all Quebec and Ontario’s electricity charges for a 5 MW customer as ÉTS. All simulations were obtained with Matlab SimScape Power System, where each system was analyzed separately and/or together, as followed: \n \n0) Baseline Scenario: ÉTS in Ontario and Quebec with electricity standard rates; \n1) ÉTS in Ontario with financial incentive and in Quebec with demand response; \n2) ÉTS in Ontario with financial incentive and Photovoltaic Arrays (150kW) as well as in Quebec with standard rates and PV Arrays; \n3) ÉTS in Ontario with financial incentive, PV Arrays (150 kW) and Batteries (250 kW) as well as in Quebec with demand response, PV Arrays and Batteries; \n4) ÉTS in Ontario with financial incentive and Batteries (250 kW) as well as in Quebec with demand response and Batteries; \n5A / 5B) ÉTS in Ontario with financial incentive, batteries with Time of Use pricing (250 kW) (5A) and also with Photovoltaic Panels (150 kW) (5B); \n6) ÉTS in Ontario with financial incentive, PV Arrays (500 kW) and Batteries (250 kW) as well as in Quebec with demand response, PV Arrays and Batteries; \n \nFirst, the simulation showed that PV System reduced the 5 MW peak power and energy consumption in both provinces, while battery energy storage system reduced the peak power and allowed the participation in the demand response program, GDP, in Quebec. Secondly, Energy and Power costs represented around 93% and 7% of a yearly bill in ON, while 61% and 39% in QC, respectively. Also, the simulation results indicated that the electricity rate variance between both provinces is huge, where it is around four times more expensive in ON than in QC, around 0.27 $/kWh and 0.057 $/kWh, respectively. The simulation also showed that the price per kWh was reduced up to 13.77% in ON from 0.3025 $/kWh to 0.2608 $/kWh and up to 6.08% in QC, from 0.0571 $/kWh to 0.0536 $/kWh, after adding PV systems, Batteries and financial incentives or demand response program. \n \nFurthermore, the simulation indicated a yearly energy saving of 412.77 MWh from MATLAB SimScape Power System and 260.53 MWh from RETScreen for electricity exported to the grid, by using a 150 kW PV Arrays (3% of 5MW from ÉTS). Also, 1,376.95 MWh yearly energy savings from MATLAB and 868.42 MWh from RETScreen, by using a 500 kW PV Arrays (10% of 5MW from ÉTS). The variance around 50% lower on RETScreen indicates a more accurate method for energy saving, with some different input data (solar irradiance and temperature) and higher loss coefficient on RETScreen. \n \nThe technical-economic feasibility indicated that a Solar PV System is economically feasible in Ontario, where an annual saving of $151,574.73 was reached for a 150 kW MPP, with a $873,050 investment, high IRR, high NPV and low payback period. Based on this potential electricity saving using a PV System, a random higher MPP of 500 kW was simulated, where an annual saving of $493,515.15 for $2,773,435 Investment, higher NPV, higher IRR and a lower payback than 150 kW were reached, due to economies of scale. No PV System was economically feasible in QC, due to its low electricity price. Also, the 250 kW BESS is not economically feasible neither in ON nor in QC, due to a high investment, low annual energy saving and a high payback period. \n \nIn Ontario, ÉTS should utilize a system of 500 kW of Photovoltaic System or higher, without any batteries, in order to achieve a considerable reduction in the yearly bill and on the price per kWh, with a low payback period and high IRR and NPV. Also, the participation of the Wholesale market held by IESO through bids to reduce the electricity cost/kWh, pay the GA by consumption or PDF (9% of yearly bill reduction) and participate of the Demand Response Auction by IESO. \n \nIn Quebec, ÉTS should utilize a 250 kW BESS without any Photovoltaic Arrays, to reduce the peak power over 5 MW and participate of the GDP’s demand response program by HQ, but only if there is a financial incentive or donation to acquire the equipment from a supplier or the government, as it is going to occur to ÉTS. Otherwise, neither Photovoltaic system nor batteries banks are recommended.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".