This paper proposes a low-carbon operation model for an energy hub (EH) that combines the distributionally robust optimization (DRO) method with the Stackelberg game. Firstly, a bilevel single-leader-multi-follower Stackelberg game model is presented where the EH is the leader while users and electric vehicles (EVs) are regarded as two followers. Then, the Kullback-Leibler (KL) divergence-based DRO model is developed to deal with the uncertainty of renewable generation (RG) in the EH. Besides, Karush–Kuhn–Tucker (KKT) conditions, strong duality theory, and big- M approach are combined to transform the bilevel model into a single-level model. The reformulated single-level operation model is incorporated into the KL-based DRO approach. Furthermore, since the crafted column and constraint generation (C&CG) algorithm can prevent possible numerical problems caused by the exponential function and accelerate the solution speed, the crafted C&CG algorithm with linearization for the upper-level slave problem is proposed to iteratively solve the KL-based DRO integrated with Stackelberg game. Finally, numerical case studies are conducted with all simulation results confirming the effectiveness of the proposed model and method.

As the increasing complexity and uncertainty of integrated energy systems, the cooperative interaction under uncertainty between the energy hub (EH) and multi-energy users has become crucial for the efficient operation of energy systems. This paper proposes a unified collaborative and interactive framework to optimize operations between the EH and multi-energy users under uncertainty. Firstly, in the horizontal dimension, the stochastic distributionally robust chance-constrained (S-DRCC) optimization method with the Wasserstein metric is proposed to hedge against the uncertainty of EH considering the multiple scenarios and moment information of uncertain variables. Then in the vertical dimension, a bi-level Stackelberg game model is developed for EH and users to facilitate the collaborative interaction for multi-agents. Furthermore, several efficient methods are leveraged to reformulate the originally intractable framework into a tractable mixed-integer linear programming (MILP) model that can be directly solved using commercial solvers. Finally, the effectiveness of the proposed model and method are demonstrated by numerical case studies and corresponding simulation results.

In order to ensure the information security, most of the important information including the data of advanced metering infrastructure (AMI) in the energy internet is currently transmitted and exchanged through the intranet or the carrier communication. The former increases the cost of network construction, and the latter is susceptible to interference and attacks in the process of information dissemination. The blockchain is an emerging decentralized architecture and distributed computing paradigm. Under the premise that these nodes do not need mutual trust, the blockchain can implement trusted peer-to-peer communication for protecting the important information by adopting distributed consensus mechanisms, encryption algorithms, point-to-point transmission and smart contracts. In response to the above issues, this paper firstly analyzes the information security problems existing in the energy internet from the four perspectives of system control layer, device access, market transaction and user privacy. Then blockchain technology is introduced, and its working principles and technical characteristics are analyzed. Based on the technical characteristics, we propose the multilevel and multichain information transmission model for the weak centralization of scheduling and the decentralization of transaction. Furthermore, we discuss that the information transmission model helps solve some of the information security issues from the four perspectives of system control, device access, market transaction and user privacy. Application examples are used to illustrate the technical features that benefited from the blockchain for the information security of the energy internet.

To realize the coordinated planning of distribution system (DS) with multiple integrated energy microgrids (IEMs), this paper proposes a mixed game-based and carbon-oriented two-stage planning model for photovoltaic (PV) capacity and energy storage system (ESS). Firstly, the mixed game-based planning model is constructed by comprising the Stackelberg game with the nested cooperative game in the first stage. Specifically, the distribution system operator (DSO) and the IEMs alliance play a Stackelberg game as the leader and follower respectively, making strategic decisions on invested PV and ESS through interactive price and power. Within the alliance, multiple IEMs reach an agreement on trading power and price to fairly share cooperation benefits through cooperative game. Secondly, the demand response model that considers carbon emission flow (CEF) and tiered carbon price is presented to reduce carbon emissions in the second stage. Furthermore, the model reformulation and distributed algorithm are developed to protect the privacy of multi-agents. The bisection-based method and the prediction-correction-based alternating direction method of multipliers (PCB-ADMM) are leveraged to solve the mixed game model for improving convergence. Finally, several cases in the modified IEEE 33-bus DS with multiple IEMs verify the effectiveness of the proposed model and methods.

Waste-to-energy (WTE) is considered the most promising method for municipal solid waste treatment. An integrated energy system (IES) with carbon capture systems (CCS) and power-to-gas (P2G) can reduce carbon emissions. The incorporation of a “green-carbon” offset mechanism further enhances renewable energy consumption. Therefore, this study constructs a WTE-IES hybrid system, which conducts multi-dimensional integration of IES-WTP, CCS-P2G, photovoltaic (PV), wind turbine (WT), multiple energy storage technologies, and the “green-carbon” offset mechanism. It breaks through the limitations of traditional single-technology optimization and achieves the coordinated improvement of energy, environmental, and economic triple benefits. First, waste incineration power generation is coupled into the IES. A mathematical model is then established for the waste incineration and CCS-P2G IES. The CO₂ produced by waste incineration is absorbed and reused. Finally, the “green-carbon” offset mechanism is introduced to convert tradable green certificates (TGCs) into carbon emission rights. This approach ensures energy demand satisfaction while minimizing carbon emissions. Economic incentives are also provided for the carbon capture and conversion processes. A case study of an industrial park is conducted for validation. The industrial park has achieved a reduction in carbon emissions of approximately 72.1% and a reduction in the total cost of approximately 33.5%. The results demonstrate that the proposed method significantly reduces carbon emissions. The energy utilization efficiency and system economic performance are also improved. This study provides theoretical and technical support for the low-carbon development of future IES.