Tom Molinski

PEV Charging Profile Prediction and Analysis Based on Vehicle Usage Data

Present-day urban vehicle usage data recorded on a per second basis over a one-year period using GPS devices installed in 76 representative vehicles in the city of Winnipeg, Canada, allow predicting the electric load profiles onto the grid as a function of time for future plug-in electric vehicles. For each parking occurrence, load profile predictions properly take into account important factors, including actual state-of-charge of the battery, parking duration, parking type, and vehicle powertrain. Thus, the deterministic simulations capture the time history of vehicle driving and parking patterns using an equivalent 10 000 urban driving and parking days for the city of Winnipeg. These deterministic results are then compared to stochastic methods that differ in their treatment of how they model vehicle driving and charging habits. The new stochastic method introduced in this study more accurately captures the relationship of vehicle departure, arrival, and travel time compared to two previously used stochastic methods. It outperforms previous stochastic methods, having the lowest error at 3.4% when compared to the deterministic method for an electric sedan with a 24-kWhr battery pack. For regions where vehicle usage data is not available to predict plug-in electric vehicle load, the proposed stochastic method is recommended. In addition, using a combination of home, work, and commercial changing locales, and Level 1 versus Level 2 charging rates, deterministic simulations for urban run-out-of-charge events vary by less than 4% for seven charging scenarios selected. Using the vehicle usage data, charging scenarios simulated have no significant effect on urban run-out-of-charge events when the battery size for the electric sedan is increased. These results contribute towards utilities achieve a more optimal cost balance between: 1) charging infrastructure; 2) power transmission upgrades; 3) vehicle battery size; and 4) the addition of new renewable generation to address new electric vehicle loads for addressing energy drivers.

Why utilities respect geomagnetically induced currents

It has been well known for more than 50 years that electric utilities in northern latitudes can have geomagnetically induced currents (GICs) flowing in their transmission lines and transformer ground points, and that these are caused by geomagnetic storms. Initially, these GICs were considered harmless and very little attention was paid to them. However, in the last 40 years it was realized that large GICs can flow in power systems and become problematic and even severe enough to cause a complete system shutdown. Utilities susceptible to GIC do not expect to rely on luck that the geomagnetic storm will not affect them, or if it does, the loading conditions at the time will allow enough margin to ride through it. This is precisely why many utilities today are studying the cause, effect, and mitigation of GICs and why utilities respect GICs. This paper presents a detailed discussion on how electric utilities are affected by GICs and what can be accomplished to mitigate the harmful effects.

Using Standard Deviation as a Measure of Increased Operational Reserve Requirement for Wind Power

The variability inherent in wind power production will require increased flexibility in the power system, when a significant amount of load is covered with wind power. Standard deviation (σ) of variability in load and net load (load net of wind) has been used when estimating the effect of wind power on the short term reserves of the power system. This method is straightforward and easy to use when data on wind power and load exist. In this paper, the use of standard deviation as a measure of reserve requirement is studied. The confidence level given by ±3–6 times σ is compared to other means of deriving the extra reserve requirements over different operating time scales. Also taking into account the total variability of load and wind generation and only the unpredicted part of the variability of load and wind is compared. Using an exceedence level can provide an alternative approach to confidence level by standard deviation that provides the same level of risk. The results from US indicate that the number of σ that result in 99% exceedence in load following time scale is between 2.3–2.5 and the number of σ for 99.7% exceedence is 3.4. For regulation time scale the number of σ for 99.7 % exceedence is 5.6. The results from the Nordic countries indicate that the number of σ should be increased by 67–100% if better load predictability is taken into account (combining wind variability with load forecast errors).

A fundamental approach to transformer thermal modeling. II. Field verification

For pt.I see ibid., vol.16, no.2, p.171-5 (2001). This paper has two main objectives. One is to show that the top oil rise thermal model proposed in part I is valid, for a large power transformer in service. The second is to show that there is a convenient way of estimating the parameters without removing the transformer from service. A Manitoba Hydro 250 MVA OFAF transformer was chosen and instrumented with data-gathering equipment. Two twenty-four hour test runs were performed, one in February of 1999 and the other in July of 1999. The most basic parameter to be determined was the rated top oil rise but also found were the top oil line constant and the nonlinearity exponent, commonly given the symbol n. The results are very positive.

Shielding grids from solar storms [power system protection]

The authors describe how geomagnetic disturbances are a real danger to some power grids and detail how being prepared for one requires an assessment of local conditions, as well as monitoring and warning systems.

Risk assessment using transformer loss of life data

The IEEE guide has evolved in sophistication for modeling transformer insulation life to models that include bottom and duct oil rises, fluid viscosities, and specific heats of materials as well as the parameters needed for the analysis shown (i.e., hot-spot gradient, oil rise over ambient, hot-spot time constant, top oil time constant, total harmonic losses, no load losses, exponential power of loss versus temperature, loading, and ambient temperature). Because data for these various inputs are missing and the loading history is difficult to retrieve, this article assesses risk without data for bottom and duct oil rises, fluid viscosities, or specific heat and does so without requiring any history of the load on the transformer. Previous IEEE guides have used equations similar to the ones used here but with differing parameters for aging rates, life end point criteria, and base lives, making management of the HVDC converter transformers in question difficult to assess. In evaluating risks, a program developed by Manitoba Hydro (TLD/spl trade/ Ver 1.0) for transformer loading is used, which incorporates a simple GUI (graphical user interface) and allows input data to be easily adjusted. The approach in the risk assessment presented here is to begin with an older IEEE C57.92-1981 guide and equate it to the per unit quantities of the most recent IEEE C57.91-1995 guide and then compare the present rate of loss of life to nominal. Only the thermal life of the insulation is considered in this analysis. Other forms of deterioration caused by aging, such as reduced dielectric strength or reduced mechanical strength, are factors in the overall transformer life, and the approach used here is limited to the transformer thermal insulation life.

Transient transformer overload ratings and protection

In this paper, standard loss of insulation life equations have been applied to step up transformers as part of the design of an intelligent relay that would calculate the time left before a possible trip (assuming that conditions remained constant) and should allow the operators to avoid tripping the transformer. The basic idea is that the transformer can withstand temperature in inverse proportion to time. The algorithm to derive the hot-spot temperature, the relationship of the hot-spot temperature to probe temperature, as well as the ability to provide operating guidelines for a derated transformer are shown. Revised transformer overload ratings as well as predictive protection can be obtained with an accurate thermal model of the limiting hottest spot temperatures. Based on the constant feedback from actual probe measurements, the thermal model is developed is detailed enough to consistently obtain very accurate results for various loads and seasons.

Modeling, Simulation and Control of Flat Panel Solar Collectors with Thermal Storage for Heating and Cooling Applications☆

Abstract The focus of the present study is on the transient modeling of components in a solar system, controls, and heating and cooling loads. The system consists of solar flat plates and a thermal storage tank, which could provide a portion of heating requirements of a social enterprise building located in Winnipeg, Manitoba, Canada. This solar system and a natural gas hot water heater may replace the low efficiency boiler that supplies low quality steam for heat throughout the building using radiators. Results of the simulation performed in Simulink supports the proper selection of solar system components and controls that will be optimized for the climatic attributes of Winnipeg.