Quantum-Key-Distribution Authenticated Aggregation and Settlement for Virtual Power Plants

TL;DR

This work addresses securing end-to-end virtual power plant operations under scarcity of quantum keys by modeling QKD key supply and routing within a risk-aware, QoSec-constrained framework. It introduces a quantum-authenticated aggregation and settlement approach that couples security strategy, key flow, and latency into a key-budgeted risk minimization problem, solvable via offline scenario optimization and online dual-guided control. A price–threshold mechanism using shadow prices (MSV) directs key allocation to high-value classes, achieving reduced residual risk and SLA violations, especially under shocks to threat intensity or QKD yields. The framework demonstrates practical gains in key efficiency and robustness, providing a pathway to deploy QKD-enabled security in critical grid operations with explicit QoSec and latency guarantees.

Abstract

The proliferation of distributed energy resources (DERs) and demand-side flexibility has made virtual power plants (VPPs) central to modern grid operation. Yet their end-to-end business pipeline, covering bidding, dispatch, metering, settlement, and archival, forms a tightly coupled cyber-physical-economic system where secure and timely communication is critical. Under the combined stress of sophisticated cyberattacks and extreme weather shocks, conventional cryptography offers limited long-term protection. Quantum key distribution (QKD), with information-theoretic guarantees, is viewed as a gold standard for securing critical infrastructures. However, limited key generation rates, routing capacity, and system overhead render key allocation a pressing challenge: scarce quantum keys must be scheduled across heterogeneous processes to minimize residual risk while maintaining latency guarantees. This paper introduces a quantum-authenticated aggregation and settlement framework for VPPs. We first develop a system-threat model that connects QKD key generation and routing with business-layer security strategies, authentication strength, refresh frequency, and delay constraints. Building on this, we formulate a key-budgeted risk minimization problem that jointly accounts for economic risk, service-level violations, and key-budget feasibility, and reveal a threshold property linking marginal security value to shadow prices. Case studies on a representative VPP system demonstrate that the proposed approach significantly reduces residual risk and SLA violations, enhances key efficiency and robustness, and aligns observed dynamics with the theoretical shadow price mechanism.

Figures (8)

• Figure 1: Overall expected residual risk over time for all methods. Shaded bands indicate key-yield shocks and high attack-intensity windows; a twin y-axis overlays the attack intensity series to contextualize spikes.
• Figure 2: End-to-end latency violation rate over time. Shaded bands denote key-yield shocks; the dashed horizontal line marks an SLA reference (e.g., 5%).
• Figure 3: Illustrative risk--key consumption Pareto front. The "Proposed" sweep traces a frontier; single points mark comparator policies with small error bars. The arrow indicates the direction of improvement (lower risk with less key use).
• Figure 4: Node-by-time heatmap of key-pool occupancy under the Proposed policy. Darker regions indicate tighter availability during stress, exposing spatial--temporal heterogeneity and bottleneck nodes.
• Figure 5: Time series of aggregated shadow price and average marginal security value (left axis), with the share of strong strategies (S1+S2) on the right axis. Peak alignment evidences the price--threshold mechanism and adaptive reallocation.