As future energy systems become more decentralised due to the integration of renewable energy resources and storage technologies, several autonomous energy management and peer-to-peer trading mechanisms have been recently proposed for the operation of energy hub networks based on optimization and game theory. However, most of these strategies have been tested either only in simulated environments or small prosumer units as opposed to larger energy hubs. This simulation reality gap has hindered large-scale implementation and practical application of these method. In this paper, we aim to experimentally validate the performance of a novel multi-horizon distributed model predictive controller for an energy hub network by implementing the controller on a complete network of hubs comprising of a real energy hub inter-faced with multiple virtual hubs. The experiments are done using two different network topologies and the controller shows promising results in both setups.

Energy hubs convert and distribute energy resources by combining different energy inputs through multiple conversion and storage components. The optimal operation of the energy hub exploits its flexibility to increase the energy efficiency and reduce the operational costs. However, uncertainties in the demand present challenges to energy hub optimization. In this paper, we propose a stochastic MPC controller to minimize energy costs using chance constraints for the uncertain electricity and thermal demands. Historical data is used to build a demand prediction model based on Gaussian processes to generate a forecast of the future electricity and heat demands. The stochastic optimization problem is solved via the Scenario Approach by sampling multi-step demand trajectories from the derived prediction model. The performance of the proposed predictor and of the stochastic controller is verified on a simulated energy hub model and demand data from a real building.

We propose a type of non-cooperative game, termed multi-cluster aggregative game, which is composed of clusters as players, where each cluster consists of collaborative agents with cost functions depending on their own decisions and the aggregate quantity of each participant cluster to modeling large-scale and hierarchical multi-agent systems. This novel game model is motivated by decision-making problems in competitive-cooperative network systems with large-scale nodes, such as the Energy Internet. To address challenges arising in seeking Nash equilibrium for such network systems, we develop an algorithm with a hierarchical communication topology which is a hybrid with distributed and semi-decentralized protocols. The upper level consists of cluster coordinators estimating the aggregate quantities with local communications, while the lower level is cluster subnets composed of its coordinator and agents aiming to track the gradient of the corresponding cluster. In particular, the clusters exchange the aggregate quantities instead of their decisions to relieve the burden of communication. Under strongly monotone and mildly Lipschitz continuous assumptions, we rigorously prove that the algorithm linearly converges to a Nash equilibrium with a fixed step size.We present the applications in the context of the Energy Internet. Furthermore, the numerical results verify the effectiveness of the algorithm.

Abstract: Several autonomous energy management and peer-to-peer trading mechanisms for future energy markets have been recently proposed based on optimization and game theory. In this paper, we study the impact of trading prices on the outcome of these market designs for energy-hub networks. We prove that, for a generic choice of trading prices, autonomous peerto-peer trading is always network-wide beneficial but not necessarily individually beneficial for each hub. Then, we propose a scalable and privacy-preserving price-mediation algorithm that provably converges to a profile of such prices. Numerical simulations on a 3-hub network show that the proposed algorithm can indeed incentivize active participation of energy hubs in autonomous peer-to-peer trading schemes.