Wind power integration in electrical networks with transmission congestion: operational complications and efficient solutions

Nature's ecological and geophysical balance, security of supply, and access to affordable electrical energy are threatened by increasing reliance on diminishing fossil fuels. These threats have motivated the consideration of alternative energy options, most notably renewables. Thus, as a clean and relatively free energy resource fairly abundant in many areas of the world, wind has gained significant attention. Variability and uncertainty of wind power, however, bring about operational challenges such as frequent cycling and suboptimum operation of conventional generating units, more reserve requirement and reduced capacity factor as well as very high or very low (even negative) energy prices due to out of merit dispatch. The more congested the power grid is, the more severe the challenges are. The added variability and uncertainty also introduce complexities into simulation tools used to study these challenges and, most notably, into unit commitment. A major concern here is the added computational burden due to the numerous probable wind power scenarios that must be examined when scheduling the ensemble of generating units for the short-term (day-ahead) operation. The thesis first studies the short-term techno-economic complications of wind integration in congested power grids using both theory and simulations. A Trans-Canadian Grid is introduced as a potential real-world example of a nation-wide balancing area whose various benefits include facilitating large-scale wind power integration. The thesis then presents two efficient variations of the unit commitment with wind power generation; (i) reduced security-constrained unit commitment or R-SCUC and (ii) generalized sigma or G-Sigma unit commitment. The first variation is based on the concept of Loadability Sets and reduces the computational burden significantly by doing away with the reserve-deployment generation variables under each individual scenario. The second variation extends the traditional 3σ approach based on the extreme levels of the single-dimensional system residual demand (demand minus wind power) to one based on the extreme levels of the random multi-dimensional bus residual demands or, alternatively, the random multidimensional system residual demand and line power flows. Core to both of these variations are transmission constraints, an aspect not treated in earlier efforts.

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