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Estimating Risk-Aware Flexibility Areas for EV Charging Pools via Stochastic AC-OPF

Juan S. Giraldo, Nataly Banol Arias, Pedro P. Vergara, Maria Vlasiou, Gerwin Hoogsteen, Johann L. Hurink

TL;DR

The paper presents a two-stage stochastic AC-OPF framework that incorporates discrete, piecewise-linear utility functions for EV charging pools and defines risk-adjusted flexibility areas ${\mathcal R}_{s,t}$ to guarantee network security under uncertainty. By solving a day-ahead SOPF and deriving area bounds via the quantile of uncertain reserves, the DSO can procure flexibility while accounting for risk, and charging pools can adjust their energy-not-served in exchange for compensation. Numerical tests on a 34-node distribution system show that enabling flexibility reduces expected energy-not-served costs and that the risk parameter $\beta_{s,t}$ governs the trade-off between system security and pool revenue; probabilistic power-flow validation highlights the risk-revenue interplay and the need for appropriate market design. Overall, the approach offers a scalable, risk-aware mechanism for integrating EV charging flexibility into distribution networks with explicit consideration of network constraints and uncertainty.

Abstract

This paper introduces a stochastic AC-OPF (SOPF) for the flexibility management of electric vehicle (EV) charging pools in distribution networks under uncertainty. The SOPF considers discrete utility functions from charging pools as a compensation mechanism for eventual energy not served to their charging tasks. An application of the proposed SOPF is described where a distribution system operator (DSO) requires flexibility to each charging pool in a day-ahead time frame, minimizing the cost for flexibility while guaranteeing technical limits. Flexibility areas are defined for each charging pool and calculated as a function of a risk parameter involving the solution's uncertainty. Results show that all players can benefit from this approach, i.e., the DSO obtains a risk-aware solution, while charging pools/tasks perceive a reduction in the total energy payment due to flexibility services.

Estimating Risk-Aware Flexibility Areas for EV Charging Pools via Stochastic AC-OPF

TL;DR

The paper presents a two-stage stochastic AC-OPF framework that incorporates discrete, piecewise-linear utility functions for EV charging pools and defines risk-adjusted flexibility areas to guarantee network security under uncertainty. By solving a day-ahead SOPF and deriving area bounds via the quantile of uncertain reserves, the DSO can procure flexibility while accounting for risk, and charging pools can adjust their energy-not-served in exchange for compensation. Numerical tests on a 34-node distribution system show that enabling flexibility reduces expected energy-not-served costs and that the risk parameter governs the trade-off between system security and pool revenue; probabilistic power-flow validation highlights the risk-revenue interplay and the need for appropriate market design. Overall, the approach offers a scalable, risk-aware mechanism for integrating EV charging flexibility into distribution networks with explicit consideration of network constraints and uncertainty.

Abstract

This paper introduces a stochastic AC-OPF (SOPF) for the flexibility management of electric vehicle (EV) charging pools in distribution networks under uncertainty. The SOPF considers discrete utility functions from charging pools as a compensation mechanism for eventual energy not served to their charging tasks. An application of the proposed SOPF is described where a distribution system operator (DSO) requires flexibility to each charging pool in a day-ahead time frame, minimizing the cost for flexibility while guaranteeing technical limits. Flexibility areas are defined for each charging pool and calculated as a function of a risk parameter involving the solution's uncertainty. Results show that all players can benefit from this approach, i.e., the DSO obtains a risk-aware solution, while charging pools/tasks perceive a reduction in the total energy payment due to flexibility services.
Paper Structure (15 sections, 20 equations, 11 figures)

This paper contains 15 sections, 20 equations, 11 figures.

Table of Contents

  1. Introduction
  2. Problem Description
  3. Mathematical Models
  4. Charging tasks
  5. Charging pools
  6. Discrete utility functions
  7. Distribution Network Model
  8. Proposed SOPF Model and Estimation of Flexibility Areas
  9. Mathematical model
  10. Estimation of the flexibility areas
  11. Test System and Simulations
  12. Obtaining Flexibility Areas -- Day-ahead planning
  13. Validation of the Obtained Flexibility Areas with Probabilistic Power Flow -- Operation
  14. Impact of Flexibility Areas on the Total Payment of the Charging Pools
  15. Conclusions

Figures (11)

  • Figure 1: Interaction between DSO, charging pools, and charging tasks.
  • Figure 2: Representation of a utility function for a charging pool $s$ with $\kappa=3$.
  • Figure 3: 34-nodes test system including four charging pools.
  • Figure 4: Utility functions used by the charging pools.
  • Figure 5: Base case results for the planning horizon indicating congestion problems. (a) Lowest voltage magnitude. (b) Highest current magnitude.
  • ...and 6 more figures