This paper proposes a multi-energy trading framework for a hybrid-renewable-to-H2 provider (HP) to coordinate the interaction and trading of electricity and H2 while promoting the efficient accommodation of renewable energy resources (RESs). In this framework, the HP can harvest hybrid RESs for green H2 production based on electrochemical effects of biomass electrolysis, and procure stacked profits from both the electricity and H2 markets by the flexibility of electricity-H2 conversion. A Vickrey auction-based pricing mechanism is developed to determine the trading price and quantity of H2 while eliciting truthful offers and bids in a competitive H2 market. Then, a single-leader-multiple-follower Stackelberg game with an iterative solution algorithm is formulated to capture the interactions between the H2 auctioneer and hydrogen fueling stations (HFSs) for achieving the win-win goal. Furthermore, a hybrid-renewable-to-H2 production and control method is proposed for the HP to raise the production efficiency of green H2 and suppress large fluctuations in electrolysis current caused by RES uncertainties. Comparative studies have validated the superiority of the proposed methodology on economic performance and RES accommodation.

This article proposes a multilateral multienergy trading framework for synergetic hydrogen (H ₂ ) and electricity transactions among renewable-dominated hybrid charging stations (HCSs). In this framework, each autonomous HCS with various renewable energy resource (RES) endowment can harvest local renewables for internal green H ₂ and electricity generation to simultaneously meet demands of electric vehicles (EVs) and hydrogen-powered vehicles (HVs) from the transportation network. The surplus electricity/H ₂ production of the HCS is accommodated by external multilateral transactions to increase the additional profit. Besides, each HCS is modeled as a sustainable energy hub, and multiple hubs with multienergy transactions contribute toward a low-carbon energy-transport nexus. A partial differential equation model based on fluid dynamic theory is formed to capture the temporal and spatial dynamics of traffic flows for estimating the EV/HV loads at HCSs. Furthermore, a distributed multilateral pricing algorithm is developed to iteratively derive the optimal prices and quantities for transactive electricity and H ₂ . Comparative studies corroborate the superiority of the proposed methodology on economic merits and RES accommodation.

This paper proposes a customized incentive compatible mean field game (MFG) method for virtual power plant (VPP) with a large number of self-interest heterogeneous distributed energy resources (DERs) to participate in the real-time peak regulation. Firstly, an optimal chance-constrained peak-regulation bidding model of VPP considering the stochastic power flexibility is formulated, where inscribed pyramid approximation method is utilized to form a compact and concise dispatch region. Secondly, a customized MFG method with dynamic granulation division is proposed for encouraging very large-scale DERs to spontaneously respond to the peak regulation instructions from VPP while achieving dynamic allocation of peak-regulation revenue. Brouwer fixed-point theorem and contraction mapping theorem are used to prove the existence and uniqueness of the mean field equilibrium (MFE) of the formulated MFG, and ϵ-Nash property of MFE is validated based on the Lipschitz continuity condition. Furthermore, an accelerated decentralized solution algorithm is developed to rapidly search MFE, exhibiting good scalability. Comparative studies have validated the superiority of the proposed methodology on incentive compatibility and decomposition efficiency of the VPP's peak-regulation instructions.

This paper proposes a scalable mean field game (MFG) strategy for virtual power plants (VPPs) to coordinate large-scale electric vehicles (EVs) in providing secondary frequency regulation services. Firstly, in order to estimate the frequency regulation capability of the geographically dispersed EVs aggregated by the VPP, a joint data- and model-driven rolling evaluation approach based on a temporal multi-graph fusion neural network (TM-GCN) is designed, providing an essential basis for the subsequent optimal bidding and regulation signal decomposition. It is a challenging task to incentivize large-scale heterogenous and self-interested EVs to proactively response to the time-critical frequency regulation command. Therefore, a scalable MFG is formulated for the VPP to rapidly decompose the secondary frequency regulation signals and revenues among numerous EVs, effectively breaking through the curse of dimensionality. The existence and uniqueness of the mean field game equilibrium (MFGE) are validated utilizing the Brouwer fixed-point theorem and the contraction mapping theorem. Moreover, a distributed solution algorithm is developed to search for the MFGE, which exhibits good convergence performance across varying numbers of aggregated EVs. Comparative studies have validated the superiority of the proposed methodology in distributing secondary frequency regulation tasks across large EV fleets while maximizing VPP’s economic benefits.

This paper proposes a hybrid knowledge and data-driven predict-then-optimize paradigm for green hydrogen (H2) trading among renewable-dominated hydrogen refueling stations (HRSs). Firstly, a data-driven H2 load forecasting method is formulated where the key influencing features are captured by XGBoost and the Informer algorithm with encoder and decoder processes is utilized to generate the predicted time series of hydrogen load. Then, a bi-level hybrid knowledge and data-driven H2 trading model with rolling horizon optimization is proposed to determine the optimal trading quantity of H2 and dynamically optimize the transportation routes for the traded H2 based on the cell transmission model and traffic state. Moreover, a fully distributed solution algorithm is developed to decompose the complex multi-period H2 trading problem into local electricity and hydrogen dispatch subproblems of HRSs for efficiently obtaining the optimal H2 trading amount. Comparative studies have demonstrated the superior performance of the proposed methodology on the improvement of the distributed renewable energy accommodation and economic benefits for HRSs.