Impact of operational constraints on generation portfolio planning with renewables

Increasing variable renewable generation penetrations will cause increased cycling operation for conventional generating plants. Not all of these plants are necessarily well suited to such operation. Traditional long-term generation planning frameworks often neglect these operational characteristics and therefore do not reflect the operational constraints and costs associated with cycling of generating plants. Using a detailed generation dispatch model in PLEXOS, this study assesses the potential impact of short-term operational constraints and costs on future `high renewable' generation portfolios obtained from a long-term portfolio planning framework. A case study of the Australian National Electricity Market (NEM) with different renewable penetrations, ranging from 15% to 85%, suggests that the technical and cost impacts associated with the operational constraints modelled are moderate even at high renewable penetrations. The extent of the impacts also depends particularly on the level of carbon price and the mix of generation technologies within the portfolios.

[1]  Iain MacGill,et al.  A Monte Carlo based decision-support tool for assessing generation portfolios in future carbon constrained electricity industries , 2012 .

[2]  Erik Ela,et al.  Evolution of Wholesale Electricity Market Design with Increasing Levels of Renewable Generation , 2014 .

[3]  D. Lew,et al.  The Western Wind and Solar Integration Study Phase 2 , 2013 .

[4]  M. O'Malley,et al.  Accommodating Variability in Generation Planning , 2013, IEEE Transactions on Power Systems.

[5]  Iain MacGill,et al.  Incorporating short-term operational plant constraints into assessments of future electricity generation portfolios , 2014 .

[6]  I. MacGill,et al.  Using renewables to hedge against future electricity industry uncertainties—An Australian case study , 2015 .

[7]  Debra Lew,et al.  Flexible Coal: Evolution from Baseload to Peaking Plant (Brochure) , 2013 .

[8]  Douglas Hilleman,et al.  Power Plant Cycling Costs , 2012 .

[9]  Bryan Palmintier,et al.  Heterogeneous unit clustering for efficient operational flexibility modeling , 2014, 2014 IEEE PES General Meeting | Conference & Exposition.

[10]  Drew Robb CCGT: Breaking the 60 per cent efficiency barrier , 2010 .

[11]  S. K. Soonee,et al.  Flexibility in 21st Century Power Systems , 2014 .

[12]  F. Leanez,et al.  Benefits of chronological optimization in capacity planning for electricity markets , 2012, 2012 IEEE International Conference on Power System Technology (POWERCON).

[13]  E. Lannoye,et al.  Evaluation of Power System Flexibility , 2012, IEEE Transactions on Power Systems.