Optimization and allocation of spinning reserves in a low-carbon framework

Abstract

Low-carbon electric power systems are often characterized by high shares of renewables, such as wind power. The variable nature and limited predictability of some renewables will require novel system operation methods to properly size and costefficiently allocate the required reserves. The current state-of-theart stochastic unit commitment models internalize this sizing and allocation process by considering a set of scenarios representing the stochastic input during the unit commitment optimization. This results in a cost-efficient scheduling of reserves, while maintaining the reliability of the system. However, calculation times are typically high. Therefore, in this paper, we merge a stateof-the-art probabilistic reserve sizing technique and stochastic unit commitment model with a limited number of scenarios in order to reduce the computational cost. Results obtained for a power system with a 30% wind energy penetration show that this hybrid approach allows to approximate the expected operational costs and reliability of the resulting unit commitment of the stochastic model at roughly one thirtieth of the computational cost. The presented hybrid unit commitment model can be used by researchers to assess the impact of uncertainty on power systems or by independent system operators to optimize their unit commitment decisions taking into account the uncertainty in their system.

Endogenous probabilistic reserve sizing and allocation in unit commitment models: Cost-effective, reliable and fast

“…In this regard, novel power system operation methods will be needed to properly size and allocate operational reserves, in order to ensure a reliable and cost-efficient operation of the power system. Optimal reserve scheduling is the result of a tradeoff between (1) the cost of load shedding and curtailment of RES-based generation and (2) the cost of reserve allocation or procurement, i.e., the cost to have reserves available, and the cost of reserve activation or deployment, i.e., the cost of dispatching or activating these reserves [1], [2].…”

“…The direct representation of the uncertainty via a set of scenarios in the unit commitment (UC) model leads to an optimal trade-off between reliability and operational system cost [2]. By imposing a common UC schedule for all scenarios, the sizing of reserves is internalized in a SUC model.…”

“…First, the computational cost of solving such a SUC problem may be high and may strongly increase with the number of scenarios one considers, depending on the solution technique employed. In addition, solution stability requirements impose a lower limit on the number of scenarios one needs to use to ensure a meaningful solution of the SUC problem [2]. Improved model formulations [3], decomposition techniques and parallelization [4]- [8], advanced scenario reduction methods [9], [10], , relaxations of the problem [11] and hybrid UC formulations with additional, explicit reserve constraints [2] are used to reduce the computational burden of solving SUC problems.…”

“…In addition, solution stability requirements impose a lower limit on the number of scenarios one needs to use to ensure a meaningful solution of the SUC problem [2]. Improved model formulations [3], decomposition techniques and parallelization [4]- [8], advanced scenario reduction methods [9], [10], , relaxations of the problem [11] and hybrid UC formulations with additional, explicit reserve constraints [2] are used to reduce the computational burden of solving SUC problems. Especially scenario-based decomposition and parallelization may strongly reduce the computational effort associated with solving SUC problems, as it breaks, to some extent, the intimate relationship between the number of scenarios and the computational cost [6]- [8].…”

“…Alternative UC formulations, such as robust UC (RUC) models [16], interval unit commitment (IUC) models [17], chance-constrained UC formulations [18] and hybrid methods [2], have been suggested to approximate the performance of the SUC model at a reasonable computational cost. In RUC models potential realizations of wind power generation are represented as an interval around the central forecast, and thus one avoids assumptions regarding individual scenarios [19].…”