Risk Inherent in Matching Unreliability With Uncertainty

Risk Inherent in Matching Unreliability With Uncertainty

Roy L. Nersesian (Monmouth University, USA) and Joe McManus (Monmouth University, USA)
DOI: 10.4018/978-1-5225-4754-9.ch005
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Solar and wind are unreliable sources of energy. Several years ago, there was an eclipse over Europe during calm weather reducing renewable (wind and solar) power to nil – without 100% backup, the lights would have gone out. Electricity demand is uncertain, but its uncertainty can be bracketed within known parameters based on an analysis of past demand. Meeting uncertain demand with reliable supply (fossil fuel, nuclear, hydro except in dry seasons) is the normal course of business for an operating utility. Matching up unreliable supply with uncertain demand is a newly emerging trend with the advent of renewables. At first, when solar and wind made minute contributions to satisfying electricity demand, the challenge was manageable. The challenge is becoming more prominent with the growth in the contribution of solar and wind to electricity supply. This chapter describes the risk of matching unreliability with uncertainty via a simulation of a utility with a notable commitment to renewables. Upon measuring risk, means to mitigate that risk will be covered.
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Reliability involves the “capacity to produce collective outcomes of a certain minimum quality repeatedly” (Hannan & Freeman, 1984, p. 153). Within the specific context of power systems reliability has been defined as “the existence of sufficient facilities to supply system loads” (Akhavein & Porkar, 2017, p. 332). The literature on reliability emphasizes that power systems are expected to satisfy load demands under all possible circumstances. As a result, reliable systems must take into account unpredictable stochastic factors that create uncertainty for supply (Lin, et al., 2014). Such systems must generate adequate power to meet aggregate demand in a secure manner that avoids or limits disruptions (Zhou, Jin & Fan, 2016). Traditionally, the literature has focused upon disruptions that result from component failures or human error (Goodwin & Strang, 2012). However, the introduction of renewable energy capacity on a large scale has created reliability concerns specific to these alternative means of power generation. Complicating the situation, as power systems evolve, reliability grows more difficult to evaluate as risks are typically assessed through parameters derived from historical data that may not reflect newer system dynamics (Li, 2014).

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