Gumbel Copula Based Aggregated Net Load Forecasting For Modern Power Systems

What is it about?

Wind and solar has major share among the growing renewable penetration, due to their extensive availability and improved technologies. Wind and solar generation are highly uncertain and intermittent as compared to system load. Increased number of such uncertain and intermittent variables necessitates complex multivariate operational strategies for system operation. Compilation of different uncertain and intermittent variables such as load, wind and solar generation, to a single uncertain variable called net load, reduces system operational planning complexity. Net load is the difference between total load and renewable gen- eration. Thus, conventional generation units have to be scheduled for net load. Prior knowledge about net load can help optimum operational planning such as generation scheduling and power system flexibility estimations. There have been significant advance- ment in load and renewable generation forecasting over last decades. Still there is little attention towards net load forecasting. This paper proposes a novel net load forecasting model using Gumbel copula based joint probability distribution for load, wind and solar generation forecasting error aggregation. Gumbel copula covers all extreme forecasting errors due to max-stable property. Proposed model uses modified Grey index models for forecasting. Results show that proposed model has strong potential in very short term net load forecasting.

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Why is it important?

Wind and solar has major share among the growing renewable penetration, due to their extensive availability and improved technologies. Wind and solar generation are highly uncertain and intermittent as compared to system load. Increased number of such uncertain and intermittent variables necessitates complex multivariate operational strategies for system operation. Compilation of different uncertain and intermittent variables such as load, wind and solar generation, to a single uncertain variable called net load, reduces system operational planning complexity. Net load is the difference between total load and renewable gen- eration. Thus, conventional generation units have to be scheduled for net load. Prior knowledge about net load can help optimum operational planning such as generation scheduling and power system flexibility estimations. There have been significant advance- ment in load and renewable generation forecasting over last decades. Still there is little attention towards net load forecasting. This paper proposes a novel net load forecasting model using Gumbel copula based joint probability distribution for load, wind and solar generation forecasting error aggregation. Gumbel copula covers all extreme forecasting errors due to max-stable property. Proposed model uses modified Grey index models for forecasting. Results show that proposed model has strong potential in very short term net load forecasting.

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