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A Data-Driven Polynomial Chaos Expansion-Based Method for Microgrid Ramping Support Capability Assessment and Enhancement

Mohan Du, Xiaozhe Wang

TL;DR

This work tackles the challenge of assessing and enhancing microgrid ramping support capability (RSC) under uncertainty from renewable energy and EVs. It introduces a data-driven sparse polynomial chaos expansion (DDSPCE) surrogate to efficiently estimate the probabilistic RSC, without assuming specific marginal distributions, and uses Sobol' indices to identify dominant uncertainty sources. The dominant inputs guide a BESS scheduling strategy that smooths outputs and increases the RSC confidence, enabling online hour-by-hour RSC evaluation. Validation on a modified IEEE 33-bus microgrid shows the method achieves rapid (under 3 minutes) evaluation and enhancement, with accurate RSC distributions and substantial variance reduction from BESS deployment.

Abstract

Microgrids (MGs) are regarded as effective solutions to provide ramping support to the main grid during heavy-load periods. Nevertheless, the uncertain renewable energy sources (RES) and electric vehicles (EVs) integrated into an MG may affect the ramping support capability (RSC) of an MG. To address the challenge, this paper develops a data-driven sparse polynomial chaos expansion (DDSPCE)-based method to accurately and efficiently evaluate the hour-by-hour RSC of an MG. The DDSPCE model is further exploited to identify the most influential random inputs, based on which a scheduling method of BESS is developed to enhance the RSC of an MG. Simulation results in the modified IEEE 33-bus MG shows that the proposed method takes less than 3 minutes for evaluating and enhancing the hourly RSC.

A Data-Driven Polynomial Chaos Expansion-Based Method for Microgrid Ramping Support Capability Assessment and Enhancement

TL;DR

This work tackles the challenge of assessing and enhancing microgrid ramping support capability (RSC) under uncertainty from renewable energy and EVs. It introduces a data-driven sparse polynomial chaos expansion (DDSPCE) surrogate to efficiently estimate the probabilistic RSC, without assuming specific marginal distributions, and uses Sobol' indices to identify dominant uncertainty sources. The dominant inputs guide a BESS scheduling strategy that smooths outputs and increases the RSC confidence, enabling online hour-by-hour RSC evaluation. Validation on a modified IEEE 33-bus microgrid shows the method achieves rapid (under 3 minutes) evaluation and enhancement, with accurate RSC distributions and substantial variance reduction from BESS deployment.

Abstract

Microgrids (MGs) are regarded as effective solutions to provide ramping support to the main grid during heavy-load periods. Nevertheless, the uncertain renewable energy sources (RES) and electric vehicles (EVs) integrated into an MG may affect the ramping support capability (RSC) of an MG. To address the challenge, this paper develops a data-driven sparse polynomial chaos expansion (DDSPCE)-based method to accurately and efficiently evaluate the hour-by-hour RSC of an MG. The DDSPCE model is further exploited to identify the most influential random inputs, based on which a scheduling method of BESS is developed to enhance the RSC of an MG. Simulation results in the modified IEEE 33-bus MG shows that the proposed method takes less than 3 minutes for evaluating and enhancing the hourly RSC.
Paper Structure (6 sections, 13 equations, 4 figures, 1 table)

This paper contains 6 sections, 13 equations, 4 figures, 1 table.

Table of Contents

  1. Introduction
  2. Probabilistic RSC
  3. DDSPCE and DDSPCE-Based Sobol' Indices
  4. The DDSPCE-based Algorithm for RSC Assessment and Enhancement
  5. Simulation Results
  6. Conclusion

Figures (4)

  • Figure 1: Diagram of the test MG.
  • Figure 2: Comparison between the CDF of RSC obtained from MCS and DDSPCE in scenarios (a) 1:00 pre-smoothing and (b) 13:00 pre-smoothing and (c) 1:00 post-smoothing and (d) 13:00 post-smoothing.
  • Figure 3: Distributions of pre-/post-smoothing RSC in selected time slots.
  • Figure 4: Sobol' index for each random input and the dominant random variables in selected time slots.