Photovoltaic generation is playing a significant role in realizing the target of carbon neutralization. This article proposes a two-stage robust optimization model to assess the maximum hosting capacity of photovoltaic generation in power distribution network (PDN) considering the integration of soft open point (SOP) and electric vehicles (EVs). Convex relaxation method is applied to convert the two-stage robust optimization model to the form of mixed-integer second-order cone programming. A solving strategy based on the column-and-constraint generation algorithm is developed to solve the model, in which linearized difference-of-convex strategy is proposed to deal with the relaxation gap. Case studies are carried out on the modified IEEE 33-bus PDN integrated with SOP and EVs, and the results show the advantages of the proposed model and solving strategy.

This article proposes a novel optimal sizing method for the energy station in the multienergy system (MES) integrated with data center. First, an overall framework of the energy station, data center, and multienergy loads is constructed to describe the interconnection of energy system and information system. Then, the dynamic voltage frequency scaling (DVFS) technique, the thermal inertia, and the deep coupling relationships of the electricity load, waste heat, cooling load, and workload in the data center are respectively modeled. On this basis, the optimal sizing model of the energy station is established, in which the data center can participate in the integrated demand response by the DVFS technique and thermal inertia. The capacity of energy station and the scheduling schemes of energy devices and servers are optimized simultaneously to coordinate the energy flow and information flow. Based on the quasi-steady-state assumption of thermal balance, the model is simplified to a mixed-integer quadratic program problem, and it is solved by Gurobi. Case studies based on a real-world test system in China illustrate that the integration of data center can be utilized to reduce the total cost of energy station planning in MES.

Probabilistic multienergy flow (PMEF) analysis is the fundamental of planning and operation of integrated energy systems (IES) considering uncertainties. The injection of hydrogen will make the PMEF calculation more complex and can deeply affect the distribution of energy flows. This article studies an efficient PMEF calculation method which can obtain the probabilistic information of electricity–gas IES with hydrogen injection. First, the steady-state model of electricity–gas IES with hydrogen injection is established, in which the gas network with hydrogen injection is formulated in compact matrix form. Second, the Jacobian matrix for the steady-state model of electricity–gas IES with hydrogen injection is derived. Third, the cumulant method and Nataf transformation are applied to calculate the PMEF considering the correlations. Finally, case studies are carried out on a coupled IES, which consists of modified IEEE 33-bus electricity system and 11-node gas system. The results demonstrate that the proposed PMEF method can reduce computational time greatly while keeping high computational accuracy when compared with the Monte–Carlo simulation method.

With the digitalization of energy systems, data centers are gradually becoming an integral part of energy hubs (EHs). Considering the spatiotemporal load regulation of data centers, this article proposes a novel multi-stage coordinated planning model for multiple EHs, which can co-optimize the structure and capacity of each EH, as well as interconnected lines and pipes among multiple EHs. First, a digraph-based computerized modeling method is developed to describe the structures of different EHs. Then, the workload allocation, multi-energy consumption, and service quality of data centers are modeled in sequence to illustrate the tight coupling relationships of energy flow and workload flow. On this basis, the coordinated planning model of multiple EHs is established with the objective function of minimizing the total cost, which is mainly subjected to the constraints of energy device selection, inside topology design, multi-energy load balance, workload balance, and risks. Case studies are carried out on a test system in north China to verify the effectiveness of the proposed planning method.

To enhance the resilience of power grids against typhoon disasters, this study proposes a novel preventive scheduling method by coordinating source, network, and load resources. An evaluation index for unexpected load shedding that considers the importance of load is provided on the basis of the utility function and employed as the objective function. Moreover, the piecewise function based objective function is transformed into a mixed-integer linear formulation by introducing auxiliary variables with clear physical meanings. The startup and shutdown arrangements of units and the optimal scheduling of their outputs, as well as the opening and closing of line switches, for protecting the power supply of important loads and various demand-side management measures are comprehensively modeled. In response to the difficulty in solving the problem caused by numerous factors considered in the model, an identification method for variable and constraint reduction by narrowing the range of switchable lines is proposed to verify the effectiveness of the switchable lines in optimal transmission switching (OTS) in reducing the congestion caused by typhoon disasters. The proposed method is applied to the modified IEEE two-area system and the IEEE-118 system. Results indicate that the utility function based collaborative preventive scheduling method can effectively reduce the loss of important loads during disasters, without excessively cutting off other loads, compared with the widely used load shedding based method. Furthermore, the model dimensionality reduction method based on the effectiveness identification of switchable lines can greatly improve the solving efficiency with on decrease in the accuracy.

Emerging prosumers are driving changes in the way traditional distribution systems operate. With the increase in the penetration of renewable energy, the energy management of prosumers has created new challenges. This paper proposes a novel peer-to-peer (P2P) transaction strategy for coupling distributed energy resources (DERs) and electric vehicles (EVs) to achieve optimal energy transaction in high-penetration distribution systems. Firstly, the model of a prosumer aggregate is established. Secondly, the economic models of prosumers and P2P transaction are constructed. Prosumers can evaluate the response benefits of internal distributed generation (DG), energy storage systems (ESSs), flexible load, EVs, and external P2P benefits and propose a joint response mechanism with mutual benefit priority to solve the problem of joint optimization. In addition, to ensure compliance with network constraints, a P2P transaction negotiation algorithm considering network charges is proposed. This algorithm guides prosumers to reach a transaction by modifying the transaction price, enabling fast negotiation without intermediaries. Finally, the proposed method is validated on an IEEE 33-bus test system, demonstrating its ability to reduce internal energy imbalances and improve economic benefits for prosumers.