Independent systems operators are mainly tasked with scheduling power generation in power plants so as to meet energy markets’ demand on a daily basis. Technically, this process in power systems’ operations is called the unit commitment (UC) and is dynamically implemented in the form of a day-ahead 24-h time frame. Generally, the objective of UC is to minimize the operating cost while satisfying physical and regulatory constraints of the units and grid. This makes the UC a challenging non-convex optimization problem. With recent advents in grid interconnectivity and addition of renewable energy systems such as wind and solar, unprecedented complexity and confounding factors in the UC problem have emerged.
The penetration of renewable energy sources has thus necessitated for thorough UC planning due to the inherent uncertainty and variability of such sources. This shortfall can however be addressed in several ways, namely: fuzzy programming, stochastic programming, and robust optimization. When compared with other uncertainty handling approaches for UC, robust optimization has been extensively used to address uncertainty in the UC problem. As such, it has drawn the attention of many power system researchers and much has been published about it.
Robust unit commitment (RUC) results in a higher degree of flexibility and provides a stronger layer of protection against uncertainty for decision makers of the power systems. Therefore, it is imperative that the works done under RUC be meticulously reviewed so as to shed greater light on existing modeling approaches, definitions of uncertainty, and developed solution methods. In a recent research paper published in Journal, Current Sustainable/Renewable Energy Reports, Setareh Torabzadeh (PhD candidate) at the University of North Carolina-Charlotte together with Dr. Mohammad Javad Feizollahi from the Georgia State University and Dr. Seyedamirabbas Mousavian at Clarkson University critically reviewed the application of various robust optimization models.
Their expert opinion review focused on shedding greater light on the variety of the uncertainty definitions in literature, classifying the sources of uncertainty in the RUC problem, and studying the modeling approaches as well as the proposed solution algorithms. To realize their goals, the research trio presented a comprehensive review of the state-of-the-art literature on the RUC problem for handling high vagueness of electricity markets while producing conservative solutions. All in all, hybrid decomposition-based algorithms were tested as effective methods for handling the complexity of the RUC problems.
One of their observations was that the stage-based, the two-stage in particular, was a popular and effective modeling approach in the RUC problems. In fact, the stage-based modeling approach was seen capable of incorporating any source of uncertainty from different components of the electricity markets in its formulation, typically in the form of budgeted uncertainty sets. Additionally, they highlighted an interesting observation that was; majority of literature defined an uncertainty set for various market parameters by proposing interval estimation of uncertainties using previous knowledge of the uncertain parameter behavior.
In summary, the study explored existing literature of the RUC problem and provided a review of the works done from three perspectives: modeling approaches, definitions of uncertainty, and developed solution methods. Generally, they pointed out that most studies only considered uncertainties of market parameters such as renewable generation, market demand, and cost and price. Overall, they pointed out that exploration of uncertainty estimation techniques that offer more flexibility -such as chance-constraint modeling- presented a promising research direction for the RUC problem, particularly in the presence of higher degrees of uncertainty.
S. Torabzadeh, M. J. Feizollahi, S. Mousavian. Robust Unit Commitment and the Promise of Higher Reliability in Electricity Markets. Current Sustainable/Renewable Energy Reports. September 2019, Volume 6, Issue 3, page 90–99.