Inês Maria Alves

Abstract
This paper proposes a novel probabilistic model for quantifying the impact of demand flexibility (DF) on the long-term generation system adequacy via Sequential Monte Carlo Simulation (SMCS) method. Unlike load shedding, DF can be considered an important instrument to postpone bulk consumption from periods with limited reserves to periods with more generating capacity available, avoiding load shedding and increasing the integration of variable renewable generation, such as wind power. DF has been widely studied in terms of its contribution to the system's social welfare, resulting in numerous innovative approaches ranging from the flexibility modeling of individual electric loads to the definition of aggregation strategies for optimally deploying this lever in competitive markets. To add to the current state-of-the-art, a new model is proposed to quantify DF impact on the traditional reliability indices, such as the Loss of Load Expectation (LOLE) and the Expected Energy Not Supplied (EENS), enabling a new perspective for the DF value. Given the diverse mechanisms associated with DF of different consumer types, the model considers the uncertainties associated with the demand flexibility available in each hour of the year and with the rebound effect, i.e., the subsequent change of consumption patterns following a DF mobilization event. Case studies based on a configuration of the IEEE-RTS 79 test system with wind power demonstrate that the DF can substantially improve the reliability indices of the static and operational reserve while decreasing the curtailment of variable generation cause by unit scheduling priorities or by short-term generation/demand imbalances.

Abstract
The Sequential Monte Carlo Simulation (SMCS) is a powerful and flexible method commonly used for generating system adequacy assessment. By sampling outage events in sequence and their respective duration, this method can easily incorporate time-dependent issues such as renewable power production, the capacity of hydro units, scheduled maintenance, complex correlated load models, etc, and is the only method that provides probability distributions for the reliability indexes. Despite these advantages, the SMCS method requires considerably more simulation time than the Non-sequential Monte Carlo Simulation approach to provide accurate estimates for the reliability indexes. In an attempt to reduce the simulation time, the SMCS method has been implemented in parallel using a Graphics Processing Unit (GPU) to take advantage of the fast calculations provided by these computing platforms. Two parallelization strategies are proposed: Strategy A, which creates and evaluates yearly samples in a completely parallel approach and while the estimates of the reliability indexes are computed in the CPU; and Strategy B, which consists on concurrently sampling the outage events for the generating units while the state evaluation and the index estimation stages are executed in serial. Simulation results for the IEEE RTS 79, IEEE RTS 96, and the new IEEE RTS GMLC test systems, show that both implementations lead to a significant acceleration of the SMCS method while keeping all its advantages. In addition, it was observed that Strategy B results in less simulation time than Strategy A for generation system adequacy assessment. © 2020 IEEE.