TY - JOUR

T1 - Sensitivity analysis of factors relevant to extreme imbalance between procurement plans and actual demand

T2 - Case study of the Japanese electricity market

AU - Kaneko, Nanae

AU - Fujimoto, Yu

AU - Hayashi, Yasuhiro

N1 - Publisher Copyright:
© 2022 The Authors

PY - 2022/5/1

Y1 - 2022/5/1

N2 - In a liberalized power system, operators control the power system to compensate for imbalances, which are the differences between the scheduled power procurement in electricity markets and the actual power supply. Interpreting the influence of factors that affect extremely large imbalances (i.e., extreme imbalance events) enables power system operators to implement appropriate system operation plans. Conventionally, such imbalance analysis has focused only on a limited number of factors and described variation in imbalance by utilizing highly interpretable statistical models, assuming that the probability of imbalance events varies monotonically with changes in those factors. In particular, sensitivity analysis of such models can be a powerful tool to support the decision-making of system operators. However, when dealing with the increasingly complex behavior of markets involving many actors, a flexible statistical analysis framework is required to identify informative factors among the various observable quantities, and to describe nonmonotonic relationships when essentially necessary. This study focuses on the statistical behavior of the odds ratio of the extreme imbalance events by concentrating on an inherently large number of explanatory variables. The authors propose a model-based approach using a class of partially linear additive models and a variable selection method to reveal the statistical relationships between the relevant variables and extreme imbalance events. The framework further provides an analysis scheme to determine the response sensitivity of relevant variables to the odds ratio of extreme imbalance events. The usefulness of the framework was demonstrated by applying the approach to a real-world dataset collected in the Japanese electricity market system. The results show that the proposed approach based on partially linear additive models works well to describe the extreme imbalance events; the constructed models derive the interpretable sensitivity curves to clarify the impact of informative variables to extreme events, while identifying monotonicity/nonmonotonicity among variables.

AB - In a liberalized power system, operators control the power system to compensate for imbalances, which are the differences between the scheduled power procurement in electricity markets and the actual power supply. Interpreting the influence of factors that affect extremely large imbalances (i.e., extreme imbalance events) enables power system operators to implement appropriate system operation plans. Conventionally, such imbalance analysis has focused only on a limited number of factors and described variation in imbalance by utilizing highly interpretable statistical models, assuming that the probability of imbalance events varies monotonically with changes in those factors. In particular, sensitivity analysis of such models can be a powerful tool to support the decision-making of system operators. However, when dealing with the increasingly complex behavior of markets involving many actors, a flexible statistical analysis framework is required to identify informative factors among the various observable quantities, and to describe nonmonotonic relationships when essentially necessary. This study focuses on the statistical behavior of the odds ratio of the extreme imbalance events by concentrating on an inherently large number of explanatory variables. The authors propose a model-based approach using a class of partially linear additive models and a variable selection method to reveal the statistical relationships between the relevant variables and extreme imbalance events. The framework further provides an analysis scheme to determine the response sensitivity of relevant variables to the odds ratio of extreme imbalance events. The usefulness of the framework was demonstrated by applying the approach to a real-world dataset collected in the Japanese electricity market system. The results show that the proposed approach based on partially linear additive models works well to describe the extreme imbalance events; the constructed models derive the interpretable sensitivity curves to clarify the impact of informative variables to extreme events, while identifying monotonicity/nonmonotonicity among variables.

KW - Electricity market system

KW - Maximal information coefficient

KW - Partially linear additive model

KW - Procurement

KW - Sensitivity analysis

KW - Supply imbalance

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U2 - 10.1016/j.apenergy.2022.118616

DO - 10.1016/j.apenergy.2022.118616

M3 - Article

AN - SCOPUS:85126597682

SN - 0306-2619

VL - 313

JO - Applied Energy

JF - Applied Energy

M1 - 118616

ER -