Plug and Play Distributed Control of Clustered Energy Hub Networks

TL;DR

The paper tackles scalability and privacy challenges in coordinating distributed energy hubs with P2P trading by introducing a clustering-based plug-and-play framework. It combines a two-level Nash bargaining approach with distributed MPC: inter-cluster trades are determined at the cluster level via a dual-consensus ADMM, while intra-cluster hub dispatch and cost-sharing are solved with ADMM in a receding-horizon MPC. It also adds plug-and-play procedures for hub and cluster joins/leaves and a fair cost-balancing mechanism within clusters. Numerical results show the framework achieves near-optimal network costs compared to centralized MPC, while preserving privacy and enabling scalable adaptation to topology changes.

Abstract

The transition to renewable energy is driving the rise of distributed multi-energy systems, in which individual energy hubs and prosumers (e.g., homes, industrial campuses) generate, store, and trade energy. Economic Model Predictive Control (MPC) schemes are widely used to optimize operation of energy hubs by efficiently dispatching resources and minimizing costs while ensuring operational constraints are met. Peer-to-peer (P2P) energy trading among hubs enhances network efficiency and reduces costs but also increases computational and privacy challenges, especially as the network scales. Additionally, current distributed control techniques require global recomputation whenever the network topology changes, limiting scalability. To address these challenges, we propose a clustering-based P2P trading framework that enables plug-and-play operation, allowing energy hubs to seamlessly join or leave without requiring network-wide controller updates. The impact is restricted to the hubs within the affected cluster. The energy trading problem is formulated as a bi-level bargaining game, where inter-cluster trading commitments are determined at the cluster level, while energy dispatch and cost-sharing among hubs within a cluster are refined at the hub level. Both levels are solved in a distributed manner using ADMM, ensuring computational feasibility and privacy preservation. Moreover, we develop plug-and-play procedures to handle dynamic topology changes at both the hub and cluster levels, minimizing disruptions across the network. Simulation results demonstrate that the proposed bi-level framework reduces operational costs, and enables scalable energy management under plug-and-play operation.

Figures (11)

• Figure 1: A network of three interconnected energy hubs. Each hub can import energy from the electricity and gas grids, and can feed-in electricity to the electricity grid. Additionally, each hub can also trade electrical and thermal energy with the other hubs.
• Figure 2: An energy hub network with the (a) centralized framework and (b) proposed framework where the hubs are divided into clusters each with a virtual cluster coordinator. The red lines depict the inter-cluster communication between cluster coordinators and the blue lines indicate the intra-cluster communication between the cluster coordinator and the hubs in the cluster.
• Figure 3: An example of this complete optimization framework executed over an interval of 72h with $T_{\mathrm{cl}} = 12$h and $t_{\mathrm{rh}} = T_{\mathrm{hb}} = 6$h and a sampling time of 1h. The computation of the average $C^{\mathrm{avg},\star}_m$ fo every time interval is shown and the hub cost distribution is done at the end with $t_{\mathrm{f}} = 72$h.
• Figure 4: Comparison of the absolute and relative cost reduction achieved by the complete network and each cluster in the network using the standard and the weighted bargaining game formulations.
• Figure 5: Convergence results of the dual ascent ADMM algorithm used to solve the bargaining game. Evolution of (a) cluster energy trade $P_{m}^{\mathrm{bid}}$, (b) cluster cost bid $C_{m}^{\mathrm{bid}}$, (c) maximal residuals and (d) sum of cluster energy trades, $\sum_m P_{m}^{\mathrm{bid}}$ and the cluster costs, $\sum_m C_{m}^{\mathrm{bid}}$ as the number of iterations increase.