Tractable Stochastic Unit Commitment for Large Systems During Predictable Hazards

Abstract

Many foreseeable natural hazards, including extreme weather events, lead to an outage of multiple transmission lines. Although such outages can be predicted in advance, there is a great deal of uncertainty in these predictions. To appropriately use the failure estimations in power system scheduling, this paper formulates a stochastic unit commitment (SUC) problem with explicit modeling of the predicted outages. The formulated problem, however, is extremely computationally-demanding, as the uncertainty is placed on the binary status of transmission lines. This paper, then, develops a computationally efficient algorithm to solve the formulated SUC for large-scale systems. The algorithm employs generation shift factors to enable rapid calculation of power flows. Additionally, flow canceling transactions are used to model multiple line outages without having to recalculate shift factors. Finally, critical constraints are iteratively detected and added to the problem. This approach substantially reduces the size of the problem, which helps computational tractability. The effectiveness of the developed algorithm is demonstrated through simulation studies on the Texas 2000-bus test system. The algorithm is used to minimize the lost load during a hypothetical hurricane. The results show that the algorithm is computationally tractable and can effectively identify a preventive dispatch, leading to a substantial reduction in power outages.

Deterministic proxies for stochastic unit commitment during hurricanes

“…However, as the objective of this paper is to compare methods regarding their results and expectations, the formulation and algorithms are not discussed in detail in this paper. Interested readers are referred to [20][21][22] for further discussions of the mathematical modelling and algorithms.…”

“…Considering that our test‐cases are defined based on large‐scale networks, the formulation of the SUC and scenario creation methods are the main challenges in this approach. In this paper, the authors used the complete form of SUC formulation based on power transfer distribution factors as described in [22], and the scenario generation method referred to as “multidimensional scenario selection (MDSS),” which is presented in [20]. In MDSS, multiple dimensions of the available data regarding each uncertainty such as failure chance, transmission capacity, and operation point are taken into account to generate the most efficient set of scenarios.…”

“…In MDSS, multiple dimensions of the available data regarding each uncertainty such as failure chance, transmission capacity, and operation point are taken into account to generate the most efficient set of scenarios. The compact form of SUC, as in [22], is presented here to discuss the key model components briefly. The objective function, which should be minimised, represents the expected cost under all the modelled scenarios, including operating costs and penalty for load shedding and over‐generation: …”