Energy-Workload Coupled Migration Optimization Strategy for Virtual Power Plants with Data Centers Considering Fuzzy Chance Constraints

TL;DR

This paper introduces a novel energy-workload coupled migration optimization strategy for virtual power plants with data centers, utilizing fuzzy chance constraints and game theory to improve demand response and operational efficiency.

Contribution

It develops a game-based coupled migration framework, transforms fuzzy chance constraints into a solvable form, and proposes an improved profit allocation method for resource coordination.

Findings

Enhanced demand response curve tracking accuracy.

Reduced operational costs in simulated scenarios.

Effective resource coordination among VPPs and data centers.

Abstract

This paper proposes an energy-workload coupled migration optimization strategy for virtual power plants (VPPs) with data centers (DCs) to enhance resource scheduling flexibility and achieve precise demand response (DR) curve tracking. A game-based coupled migration framework characterized by antisymmetric matrices is first established to facilitate the coordination of cross-regional resource allocation between VPPs. To address the challenge posed to conventional probabilistic modeling by the inherent data sparsity of DC workloads, deterministic equivalent transformations of fuzzy chance constraints are derived based on fuzzy set theory, and non-convex stochastic problems are transformed into a solvable second-order cone program. To address the multi-player interest coordination problem in cooperative games, an improved Shapley value profit allocation method with the VPP operator as intermediary is proposed to achieve a balance between theoretical fairness and computational feasibility. In addition, the alternating direction method of multipliers with consensus-based variable splitting is introduced to solve the high-dimensional non-convex optimization problem, transforming coupled antisymmetric constraints into separable subproblems with analytical solutions. Simulations based on real data from Google's multiple DCs demonstrate the effectiveness of the proposed method in improving DR curve tracking precision and reducing operational costs.