As the global warming crisis becomes increasingly serious, decarbonized integrated electricity-gas system (IEGS) which can reduce CO ₂ emissions are gradually developed. However, with high proportion of renewable energy access, the inherent randomness and volatility bring great difficulties to energy scheduling optimization of decarbonized IEGS. In this article, for reducing CO ₂ emissions, meanwhile improving the utilization efficiency of wind power, a novel IEGS architecture with collaborative operation of power-to-gas (P2G), carbon capture system (CCS) and electric vehicles (EVs) is constructed. P2G is operated in a refined model combined with H ₂ storage and captured CO ₂ by CCS can be further consumed in reaction of P2G. Besides, EVs are innovatively adopted in IEGS as flexible energy resource to reduce the impact of wind power fluctuations. Additionally, a multi-step day ahead-intraday collaborative optimization framework is proposed to handle with the uncertainty of wind power, more accurate predicted wind power can be adopted under this framework. The objective function of constructed IEGS is minimize the total operating costs, which takes the CO ₂ processing costs and penalty costs of wind power deviations into consideration. Numerical studies are conducted with different cases, with the constructed structure and proposed multi-step optimization framework, the emissions of CO ₂ can be efficiently reduced and wind power utilization can be significantly improved, the total operating costs of IEGS can be reduced more than 20% compared with other cases, which demonstrates that the research of this article has better economic benefits and environmental friendliness.

The increasing penetration of electric vehicles (EVs) gradually evolves the urban transportation network (UTN) and power distribution network (PDN) from being independent to being coupled. To analyze the impact of drivers' behavior on the coupled network, and reveal the relationship between the two networks, a coupled traffic-distribution (CTD) model is proposed, in which the total traveling cost of UTN and the power service cost of PDN is minimized. In the CTD model, A stochastic user equilibrium traffic assignment problem (SUE-TAP) with elastic traffic demand and discrete path selection of drivers is formulated to capture the traffic flow distribution comprised of EVs and gasoline vehicles (GVs). An alternative current optimal power flow (ACOPF) model of PDN is utilized to provide the optimal charging price and scheduling plan. Besides, the SUE-TAP with nonlinear functions is difficult to solve, a novel piecewise linear approximation method is presented to deal with the nonlinear items of the SUE-TAP caused by drivers' traveling cost and traveling tendency incorporating perception. Incorporating the behavioral theory, a distributed coordination method derived from optimal condition decomposition is proposed for the coupled traffic-power network equilibrium. The proposed distributed scheme is examined using a coupled network, which consists of a 20-road UTN and a 33-bus PDN. Numerical results demonstrate the effectiveness and practicability of the proposed model and the distributed coordination method.

Distribution topology is oftentimes changed to cope with the development of local power load. Therefore, connectivity verification has become a critical task for optimal grid operation. In this paper, a novel cloud-edge collaboration approach is presented to identify outlier users and correct connections. Based on the smart meter voltage analytics, an affinity propagation clustering-based local outlier factor (AP-LOF) algorithm is proposed for the voltage outlier identification and verification of the edge transformer. Compared to traditional methods, it can effectively identify the outlier user groups with high internal voltage correlation. Besides, a recommendation mechanism is developed in the cloud center, which repositions the identified outlier users by coordinating the information exchange between the edge transformers and the cloud center. Numerical tests are conducted using the actual smart meter voltage data. The results show that the proposed AP-LOF algorithm exhibits a better performance, which is suitable for the identification of various outlier users. Compared to a centralized architecture, 66% savings in calculation time is achieved by the cloud-edge collaboration approach. It further demonstrates the effectiveness and practicability of the proposed method in terms of identification accuracy and verification efficiency.

Uncertainties including demand and environmental factors would increase the operational risk of the energy supply networks. It significantly affects the technical and financial aspects of the integrated electricity and heating system (IEHS). This article presents a risk-averse multiobjective optimization strategy for short-term decision-making. The objective is to optimize the total cost and the technical dissatisfaction in a way that the economy of system operation and energy supply quality for the risk-averse scheme is guaranteed simultaneously. Based on the trapezoidal fuzzy membership function, the fuzzy constraint is constructed to quantify the technical dissatisfaction of the nodal voltage and supply temperature. Then, a multiobjective min–max–min problem is formulated to hedge the IEHS against risk imposed by the uncertain variables. Since the formulated two objectives compete with each other, a risk-averse multiobjective solution procedure based on the augmented epsilon-constraint algorithm is developed to recast the multiobjective problem into its equivalent single objective problem. And the accurate Pareto fronts under different risk levels are obtained with several iterations. Numerical case studies are conducted to evaluate the effectiveness and applicability of the proposed method. It provides benefits in the aspect of balancing the economy and energy supply quality under network constraints and uncertainties.