Using Analytics to Assess Station Thermal Risks Caused by Reverse Power Flow

With sufficient Distributed Generation (DG – embedded generation in Europe) installed on the feeders of a substation, reverse power flow may occur at the station when load is low. This is especially the case when large generators (such as wind farms) are connected on express feeders dedicated to their use.

Substations have been designed, rated and operated as step-down substation with power flowing from higher system voltage to lower system voltage. Some substation transformers also have dual secondary winding transformers that do not allow for reverse power flow conditions, as unequal reverse flow in the two secondary windings would cause overheating and potential failure of the transformer.

Utilities limit DG capacity downstream of a station to avoid excessive reverse-flow and to prevent overheating of substation transformers. For example, Hydro One requires that generation shall not exceed 60% of the maximum MVA rating of a single transformer plus minimum station load.

The (worst-case) engineering assumption is that maximum generation coincides with lowest load at a station. Is it the case? Some years ago, I ran a Monte-Carlo simulation between load and wind generation, based on theoretical distribution of both, but doubling the generation normally allowed. It found that generation would be excessive… less than 2% of the time (and not by much, and at a time when load is low and so are prices). Using actual smart meter data, it is now possible to actually know what is going on and better assess risks. For solar generation in hot climates, there is a negative correlation between load and generation – in other words, maximum generation does not happen in times of minimum load.

Even better: correlating with forecast weather data can assess whether reverse flow could be excessive in a few hours, and require large DGs to go off-line ahead of a problem (and this would not happen frequently). While I have not seen such an application, it is clearly in the realm of possibilities.

Deep analytics, used as a planning tool or in support of operations, enables safe integration of more distributed integration by managing thermal limit of station transformers operating in reverse flow.

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