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Quantifying and Optimizing the Time-Coupled Flexibilities at the Distribution-Level for TSO-DSO Coordination

Yilin Wen, Yi Guo, Zechun Hu, Gabriela Hug

TL;DR

The work tackles the challenge of coordinating DER-based flexibility across TSO-DSO interfaces by introducing a time-coupled cost quantification method applicable to both individual DERs and aggregators. It develops a linear, distribution-level DSO optimization to activate flexibility for energy arbitrage and reserves, and introduces Marginal Flexibility Prices (MFPs) to allocate TSO revenues back to aggregators while ensuring revenue adequacy and DSO non-profit operation. The approach captures the time-coupled nature of storage-like DERs, including EVs, BESS, and HPs, through a unified cost framework and a LinDistFlow-based network model, demonstrated on an IEEE-33 test system with realistic price data. Results confirm that MFP-based settlements can preserve revenue adequacy, incentivize truthful reporting of costs, and reveal the impact of aggregators’ flexibility costs on activation outcomes; limitations point to aggregator cost estimation and real-time disaggregation as avenues for future work.

Abstract

The flexibilities provided by the distributed energy resources (DERs) in distribution systems enable the coordination of transmission system operator (TSO) and distribution system operators (DSOs). At the distribution level, the flexibilities should be optimized for participation in the transmission system operation. This paper first proposes a flexibility quantification method that quantifies the costs of providing flexibilities and their values to the DSO in the TSO-DSO coordination. Compared with traditional power-range-based quantification approaches that are mainly suitable for generators, the proposed method can directly capture the time-coupling characteristics of DERs' individual and aggregated flexibility regions. Based on the quantification method, we further propose a DSO optimization model to activate the flexibilities from DER aggregators in the distribution system for energy arbitrage and ancillary services provision in the transmission system, along with a revenue allocation strategy that ensures a non-profit DSO. Numerical tests on the IEEE test system verify the proposed methods.

Quantifying and Optimizing the Time-Coupled Flexibilities at the Distribution-Level for TSO-DSO Coordination

TL;DR

The work tackles the challenge of coordinating DER-based flexibility across TSO-DSO interfaces by introducing a time-coupled cost quantification method applicable to both individual DERs and aggregators. It develops a linear, distribution-level DSO optimization to activate flexibility for energy arbitrage and reserves, and introduces Marginal Flexibility Prices (MFPs) to allocate TSO revenues back to aggregators while ensuring revenue adequacy and DSO non-profit operation. The approach captures the time-coupled nature of storage-like DERs, including EVs, BESS, and HPs, through a unified cost framework and a LinDistFlow-based network model, demonstrated on an IEEE-33 test system with realistic price data. Results confirm that MFP-based settlements can preserve revenue adequacy, incentivize truthful reporting of costs, and reveal the impact of aggregators’ flexibility costs on activation outcomes; limitations point to aggregator cost estimation and real-time disaggregation as avenues for future work.

Abstract

The flexibilities provided by the distributed energy resources (DERs) in distribution systems enable the coordination of transmission system operator (TSO) and distribution system operators (DSOs). At the distribution level, the flexibilities should be optimized for participation in the transmission system operation. This paper first proposes a flexibility quantification method that quantifies the costs of providing flexibilities and their values to the DSO in the TSO-DSO coordination. Compared with traditional power-range-based quantification approaches that are mainly suitable for generators, the proposed method can directly capture the time-coupling characteristics of DERs' individual and aggregated flexibility regions. Based on the quantification method, we further propose a DSO optimization model to activate the flexibilities from DER aggregators in the distribution system for energy arbitrage and ancillary services provision in the transmission system, along with a revenue allocation strategy that ensures a non-profit DSO. Numerical tests on the IEEE test system verify the proposed methods.
Paper Structure (16 sections, 2 theorems, 34 equations, 9 figures)

This paper contains 16 sections, 2 theorems, 34 equations, 9 figures.

Table of Contents

  1. Introduction
  2. Quantification of Flexibility Costs for Individual DERs
  3. Flexibility Cost Quantification based on Both Power and Energy Adjustment Ranges
  4. Specification of the Cost Coefficients for Typical DERs
  5. Electric Vehicles
  6. Battery Energy Storage Systems
  7. Heat Pumps
  8. Model and Costs of Aggregated Flexibility
  9. The DSO Model for Flexibility Activation and Revenue Allocation
  10. The DSO Optimization Model for Flexibility Activation
  11. Marginal Flexibility Prices for Revenue Allocation
  12. Case Studies
  13. Simulation Setup and Flexibility Cost Coefficients
  14. Results of Flexibility Activation and Revenue Allocation
  15. Impact of the Aggregators' Flexibility Costs on the Results
  16. ...and 1 more sections

Key Result

Proposition 1

$\exists \mathbf D \in \mathbb R^{T\times T}$, $\forall \Delta \hat{\boldsymbol{\theta}}\ge \mathbf 0$ and $\Delta \check{\boldsymbol{\theta}}\ge \mathbf 0$, if the indoor temperature profile $\boldsymbol{\theta}$ satisfies $-\Delta \check{\boldsymbol{\theta}} \le \Delta \boldsymbol{\theta}-\boldsym Specifically, $\mathbf D$ is an invertible matrix defined as: However, $\nexists \mathbf F \in \ma

Figures (9)

  • Figure 1: Framework of activating the flexibility of DERs to participate in the transmission system operation mousavi2021dsozhang2022optimalgivisiez2020reviewtalaeizadeh2023prioritization.
  • Figure 2: The flexibility in accumulated energy consumption of an EV and its associated cost coefficients.
  • Figure 3: The flexibility in accumulated energy consumption of a BESS and its associated cost coefficients.
  • Figure 4: The flexibility in accumulated energy consumption of an HP and its associated cost coefficients.
  • Figure 5: Flexibility cost coefficients of an aggregator.
  • ...and 4 more figures

Theorems & Definitions (2)

  • Proposition 1
  • Proposition 2