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Robust VAR Capability Curve of DER with Uncertain Renewable Generation

Aditya Shankar Kar, Kiran Kumar Challa, Alok Kumar Bharati, Ankit Singhal, Venkataramana Ajjarapu

TL;DR

The paper tackles the challenge of deriving a dispatchable VAR capability curve for DER-rich distribution systems under uncertain solar generation. It proposes a robust, probabilistic aggregation framework that preserves unbalance and losses by modeling solar uncertainty with a Gaussian reformulation of a chance-constrained OPF, yielding a dispatchable VAR capability region $[ar{Q}^{sub}_{deamnd}, \underline{Q}^{sub}_{deamnd}]$ with an associated probability. Key contributions include quantifying solar forecast errors from historical data, extending a 3-phase unbalanced LinDistFlow-based network model, and solving a CC-OPF to produce robust capability curves that can inform TSO planning and operation. The results on a 123-node test system demonstrate how the aggregated VAR capability shifts with solar uncertainty, providing actionable, probabilistic guidance for reactive power management and congestion relief in real-world grids.

Abstract

Active distribution system with high penetration of inverter based distributed energy resources (DER) can be utilized for VAR-related ancillary services. To utilize the DER flexibility, transmission system operator (TSO) must be presented with the aggregated DER flexibility of distribution system. However, the uncertainty in renewable generation questions the credibility of aggregated capability curve in practice. In this paper, we incorporate the uncertainty into aggregation process to develop a robust capability curve while preserving the real physics (unbalance and lossy nature) of distribution system. Statistical inference method is employed to quantify uncertainty in solar generation and quantified uncertainty is integrated into a chance constrained optimal power flow (OPF). It provides the grid operator with the dispatchable aggregated reactive power capability. The resulting capability curve with the associated probability can be harnessed by the TSO for decision making for both planning and operation.

Robust VAR Capability Curve of DER with Uncertain Renewable Generation

TL;DR

The paper tackles the challenge of deriving a dispatchable VAR capability curve for DER-rich distribution systems under uncertain solar generation. It proposes a robust, probabilistic aggregation framework that preserves unbalance and losses by modeling solar uncertainty with a Gaussian reformulation of a chance-constrained OPF, yielding a dispatchable VAR capability region with an associated probability. Key contributions include quantifying solar forecast errors from historical data, extending a 3-phase unbalanced LinDistFlow-based network model, and solving a CC-OPF to produce robust capability curves that can inform TSO planning and operation. The results on a 123-node test system demonstrate how the aggregated VAR capability shifts with solar uncertainty, providing actionable, probabilistic guidance for reactive power management and congestion relief in real-world grids.

Abstract

Active distribution system with high penetration of inverter based distributed energy resources (DER) can be utilized for VAR-related ancillary services. To utilize the DER flexibility, transmission system operator (TSO) must be presented with the aggregated DER flexibility of distribution system. However, the uncertainty in renewable generation questions the credibility of aggregated capability curve in practice. In this paper, we incorporate the uncertainty into aggregation process to develop a robust capability curve while preserving the real physics (unbalance and lossy nature) of distribution system. Statistical inference method is employed to quantify uncertainty in solar generation and quantified uncertainty is integrated into a chance constrained optimal power flow (OPF). It provides the grid operator with the dispatchable aggregated reactive power capability. The resulting capability curve with the associated probability can be harnessed by the TSO for decision making for both planning and operation.
Paper Structure (9 sections, 10 equations, 4 figures, 2 tables)

This paper contains 9 sections, 10 equations, 4 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Quantification of Uncertain Solar Generation
  3. System Modelling
  4. Description of Hardware and their Limits
  5. Linear Model
  6. Solar Generation with Associated Probability
  7. OPF Formulation
  8. Test System Description and Result
  9. Discussion and Conclusion

Figures (4)

  • Figure 1: Histogram and fitted normal distribution of the calculated error
  • Figure 2: Capability curve of inverter-based DER and Associated Uncertainty
  • Figure 3: (a) Day ahead load and solar generation forecast, (b) Generated solar profile with associated probability
  • Figure 4: VAR flexibility regions (F-R) of D-system with different probability