A TSO-DSO Coordination Framework via Analytical Representation and Monetization of PQV-Based Distribution System Flexibility

TL;DR

This paper tackles privacy concerns in TSO-DSO coordination by introducing a privacy-preserving, analytical representation of distribution system flexibility. It develops a $3$-D $PQV$ Feasible Operating Region (FOR) that accounts for voltage variability and heterogeneous FPU characteristics, constructed via AC-OPF-based sampling using a two-stage BBPS and Fibonacci Direction Sampling strategy, and represented by an implicit polynomial FOR with a separate quadratic cost function over the $P$-$Q$-$V$ domain. The DS models are fed to the TSO as $FOR_j(oldsymbol{x}_j)\le 0$ and $C_j(oldsymbol{x}_j)$ defined on non-sensitive coupling variables $oldsymbol{x}_j=[p_j,q_j,v_j]^ op$, enabling single-round coordination where the TSO selects a PCC point on the FOR and each DSO dispatches locally. Case studies on meshed DSs up to $533$ buses show near-zero average cost deviations ($\, ext{≤ }0.058 ext{}{ ext{,}}$) and substantial computational speedups (up to $58 ext{}{ ext{)}}$ compared to standard AC-OPF, demonstrating a scalable, privacy-preserving pathway to leveraging DS flexibility in large-scale power systems.

Abstract

As the role of distribution system (DS) flexibility in transmission system operator (TSO) network management becomes increasingly vital, data privacy concerns hinder seamless interoperability. The notion of the feasible operating region (FOR), defined in the PQ domain, has emerged as a promising privacy-preserving approach. However, effectively leveraging FOR in TSO operations remains challenging due to three key factors: its accurate determination in large-scale, meshed DS networks; its tractable analytical representation; and its economic valuation. In the present paper, we propose a novel AC optimal power flow (OPF)-based method to construct a three-dimensional PQV-FOR, explicitly accounting for voltage variability and diverse flexibility-providing unit (FPU) characteristics. The construction process employs a two-stage sampling strategy that combines bounding box projection and Fibonacci direction techniques to efficiently capture the FOR. We then introduce an implicit polynomial fitting approach to analytically represent the FOR. Furthermore, we derive a quadratic cost function over the PQV domain to monetize the FOR. Thus, the proposed framework enables single-round TSO-DSO coordination: the DSO provides an analytical FOR and cost model; the TSO determines operating point at the point of common coupling (PCC) within the FOR-based AC-OPF; and the DSO computes FPU dispatch by solving its local OPF, without computationally intensive disaggregation or iterative coordination. Case studies on meshed DS with up to 533 buses, integrated into TS, demonstrates the method's efficiency compared to standard AC-OPF. On average, the proposed approach yields negligible cost deviations of at most 0.058% across test cases, while reducing computation times by up to 58.11%.

Figures (5)

• Figure 1: Schematic representation of the proposed method. For clarity, only a single DS is illustrated; however, the framework supports multiple DSs. The results obtained at each step are highlighted in red.
• Figure 2: The sampling procedure with a) BBPS b) FDS c) LHS. For clarity in the illustration, arrows are indicated only one side.
• Figure 3: Single line diagrams of the DSs and characteristics of the DGs.
• Figure 4: a) Dataset generated using BBPS b) Dataset generated using FDS c) Constructed FOR functions (with z-score normalization) d) Cost distribution obtained via LHS. Active and reactive power are expressed in MW and MVAR, respectively, while voltage is expressed in p.u.
• Figure 5: Histogram of total cost and computational time differences, with AC-OPF as the reference, for integrated TS-DS. Each TS is combined with three analytically represented DSs (Case 33bw, Case 136, and Case 533).