Capacity response sets for security-constrained unit commitment with wind uncertainty

Abstract

Power systems face growing uncertainty from intermittent renewable resources. This uncertainty makes it harder to schedule generators efficiently because the economics depend on unknown outcomes. This research modifies security-constrained unit commitment to anticipate the cost of dispatching backup capacity if and when it is needed. The model uses capacity constraints to cover individual scenarios by defining scenario response sets, which are revised iteratively using a mixed-integer program.The approach provides a mathematical way to derive capacity requirements like those used today in CAISO and ISO-NE. Testing on the IEEE 73-bus test case demonstrates the capacity requirements are economical compared to other deterministic policies from the literature.

Review of planning methodologies used for determination of optimal generation capacity mix: the cases of high shares of PV and wind

“…What these studies are doing is testing the adequacy of the existing plan that is already determined by high-resolution models. These types of studies are found in [58,[149][150][151][152][153][154][155][156][157][158]. Following generation expansion plan and unit commitment analyses, then power system stability issues are analysed and studies, as done in [48,50,110,[159][160][161][162][163][164][165][166][167][168], must be performed.…”

Large-scale unit commitment under uncertainty: an updated literature survey

“…Some authors develop ad-hoc "decomposition like" schemes such as in [324], where a 2-stage stochastic UC is considered, but with a Benders-like ad-hoc procedure to re-enforce constraints in the master problem dealing with the optimal commitment decisions. In particular, the effect of scenarios are approximated by means of capacity constraints on "slow" units whose RHS is the amount of power required in that scenario, thereby including the uncertainty; conversely, "fast" units are allowed to change their commitment when uncertainty is revealed, which implies having integer variables in the scenario subproblems.…”

Impact of compressed air energy storage system into diesel power plant with wind power penetration

“…Unit minimum on and off time limits are expressed in (12) and (13) respectively. where T on i and T of f i are the minimum on/off time of unit i respectively; T U i and T D i are the hours of unit i must be initially on/off due to its minimum up/down time limits respectively; X on i and X of f i is number of hours of unit i has already been on/off prior to the first hour respectively.…”

“…Ref [10] proposed a model that includes integration of power-to-hydrogen to accommodate a high penetration of wind generation in which the excess wind generation is converting into hydrogen and stored for using later when needed. Ref [12] presented the modern bio-inspired algorithm called Grey Wolf Optimization (GWO) algorithm to solve the proposed problem which includes thermal generators integrated with WP and the optimization problem is formulated as UC model and the results show that the algorithm has an effective capability to obtain the economic benefits with good quality. The uncertainty nature of variable RES makes it difficult to schedule the power generator units efficiently because the system operators depend on variable outcomes.…”

“…The uncertainty nature of variable RES makes it difficult to schedule the power generator units efficiently because the system operators depend on variable outcomes. Authors in [12] modify SCUC to capacity constraints by defining scenario response sets for predict the economic cost of dispatching backup capacity when it is needed. The mathematical models and several approaches are developed for addressing renewable power generations effects and uncertainties [13].…”