Hourly solar and photovoltaic (PV) forecasts for horizons between 0 and 48 h ahead were developed using Environment Canada's Global Environmental Multiscale model. The motivation for this research was to explore PV forecasting in Ontario, Canada, where feed‐in tariffs are driving rapid growth in installed PV capacity. The solar and PV forecasts were compared with irradiance data from 10 North‐American ground stations and with alternating current power data from three Canadian PV systems. A 1‐year period was used to train the forecasts, and the following year was used for testing. Two post‐processing methods were applied to the solar forecasts: spatial averaging and bias removal using a Kalman filter. On average, these two methods lead to a 43% reduction in root mean square error (RMSE) over a persistence forecast (skill score = 0.67) and to a 15% reduction in RMSE over the Global Environmental Multiscale forecasts without post‐processing (skill score = 0.28). Bias removal was primarily useful when considering a “regional” forecast for the average irradiance of the 10 ground stations because bias was a more significant fraction of RMSE in this case. PV forecast accuracy was influenced mainly by the underlying (horizontal) solar forecast accuracy, with RMSE ranging from 6.4% to 9.2% of rated power for the individual PV systems. About 76% of the PV forecast errors were within ±5% of the rated power for the individual systems, but the largest errors reached up to 44% to 57% of rated power. © Her Majesty the Queen in Right of Canada 2011. Reproduced with the permission of the Minister of Natural Resources Canada.

No abstract

No abstract

No abstract

No abstract

Hourly electric power demand data in Toronto from 2000 to 2006 was analyzed along with coincident, simulated hourly photovoltaic (PV) power generation to quantify PV capacity value on a year-round basis. Three different methods commonly employed by electric utilities were used to assess PV capacity value, and their results were compared. The first method is the Garver approximation to effective load carrying capability (ELCC), which served as a benchmark for capacity value. The other two methods equate PV capacity value with the capacity factor during ''peak demand intervals'': for method 2 the interval includes all hours with loads within a given per cent deviation from the peak load; for method 3, a fixed ''on-peak'' interval of 11-17 h in June-August is used. Methods 2 and 3 yielded PV capacity values of about 40%, in agreement with the results of the Garver approximation at low grid penetration. This is considerably higher than the yearly PV capacity factor of about 12%, and is in good agreement with previous studies. Capacity value varies significantly from year to year: for instance, values from method 1 at low grid penetration levels range from 30% (year 2000) to 44% (year 2006). Yearly variations in capacity value appear correlated with variations in the demand summer to winter peak ratio, reflecting the fact that PV capacity value is strongly linked to its capacity to reduce peak demand (''peak shaving'') during the summer.

During periods of low electricity demand, particularly when demand drops below baseload supply levels, a system operator can encounter difficulties in efficiently dispatching its generating units. Referred to as 'minimum generation conditions (MGC),' these states are troublesome because they can lead to increased greenhouse gas emissions, depressed electricity prices, or additional barriers to renewables integration. This work explores the potential of using electric water heaters (EWHs), in a demand response (DR) role, to mitigate the number and severity of these MGCs. A detection method for finding MGCs is first applied to the system in Ontario, Canada. At 2018 renewables target levels, it was found that most MGCs would occur in the early morning of spring and fall. To significantly address this issue next generation EWHs employing DR would need a deadband of 10 • C to enable the 800+ MW required.Index Terms-load management, renewable energy, smart grids, surplus baseload, water heating TABLE I NOMENCLATURE Sets and Parameters α 0 (t) Probability of q(t) changing from 0 to 1 (fraction) α 1 (t) Probability of q(t) changing from 1 to 0 (fraction) aRate of heat loss to surroundings (through insulation) (per min) A Rate of temperature loss when hot water is drawn ( • C/min) R Rate of temperature gain when element is on (Variables m(t) State of heating element (0-off, 1-on) q(t) State of water extraction (0-off, 1-on) x(t) Hot water temperature ( • C)

No abstract