The Value of Multi-Stage Stochastic Programming in Risk-Averse Unit Commitment Under Uncertainty

Abstract

Day-ahead scheduling of electricity generation or unit commitment is an important and challenging optimization problem in power systems. Variability in net load arising from the increasing penetration of renewable technologies have motivated study of various classes of stochastic unit commitment models. In two-stage models, the generation schedule for the entire day is fixed while the dispatch is adapted to the uncertainty, whereas in multi-stage models the generation schedule is also allowed to dynamically adapt to the uncertainty realization. Multistage models provide more flexibility in the generation schedule, however, they require significantly higher computational effort than two-stage models. To justify this additional computational effort, we provide theoretical and empirical analyses of the value of multi-stage solution for risk-averse multi-stage stochastic unit commitment models. The value of multi-stage solution measures the relative advantage of multi-stage solutions over their two-stage counterparts. Our results indicate that, for unit commitment models, value of multi-stage solution increases with the level of uncertainty and number of periods, and decreases with the degree of risk aversion of the decision maker.

Co‐optimization of active power curtailment, load shedding and spinning reserve deficits through hybrid approach: Comparison of electrochemical storage technologies

“…The comprehensive formulation of the objective (1) can now be used to account for the total cost in all periods T. The systemwide constraints of power balance and spinning reserve are modelled by making use of the decision variables U t i and P t i . In Equation (2), it is ensured that the sum of the power produced from all committed units meets the net load demand (P t netD ) at each time-interval, considering the contribution of storage ( ∑ P t i − P t dis ) and renewable generation ( P t netD = P t D − P t RES ). Constraint (3) is used to guarantee the spinning reserve requirements SR t based on the maximum ramping capability of each unit (P t i,max_cap ) along with the direct (P t s ) and indirect (P t ch ) storage participation.…”

“…The simultaneous increase in electricity demand and reduction of conventional sources contribution in power generation create a lot of integration issues. The uncertainty and variability in net load caused by the increasing penetration of renewable generation undeniably disturb the overall system stability and reliability [2]. Hence, adequate operating reserves are required to cover the uncertainty caused by forecast errors, whereas sufficient ramping capability is necessary to address the variability issues which often occur at high time resolutions [3].…”

A Multi-stage Stochastic Programming Model for Adaptive Biomass Processing Operation under Uncertainty

“…Similarly, (Xie and Huang 2018) shows the VMS for strategic expansion of biofuel supply chain under uncertainty. (Mahmutogulları et al 2019) presents the VMS in risk-averse unit commitment under uncertainty, a daily problem that arises energy market participation. This paper contributes to the current multi-stage SP literature by demonstrates the VMS for the optimization of manufacturing and processing facilities.…”

Robust risk‐averse unit commitment with solar PV systems

“…Deterministic UC methods could result in expensive decisions or infeasible solutions for the high level of uncertainties in the network [2,3]. Over the last decade, stochastic optimisation [4][5][6][7], chance-constraint optimisation [8][9][10][11][12][13], and robust optimisation [2,14,15] models have been proposed for the UC to deal with intermittent RPG mainly wind generations.…”