The adoption of electric vehicles for global decarbonization integrates traffic and power networks. However, emissions from upstream power generation and ongoing gasoline vehicles continue to impact overall carbon levels, which cannot be ignored. This work addresses this issue by proposing a uniform carbon pricing scheme, where the emission responsibilities are incorporated to achieve low-carbon scheduling of coupled power-traffic networks. Specifically, gasoline vehicles have a direct carbon price imposition through an emission model; the carbon emission flow theory enables a carbon price based on the actual charging demand, thus, an innovative energy-carbon integrated price is introduced to regulate electric vehicles. The scheduling results are obtained by an iterative method, with their existence and uniqueness proven through the fixed-point theory, and a Genetic Algorithm-based method is introduced to accelerate the computation process. Finally, two case studies demonstrate the effectiveness of the proposed pricing scheme in identifying emission responsibilities and imposing charging fees on emissions. The outcomes demonstrate a superior balance between reducing emissions and enhancing economic performance compared to current methods.

The fifth-generation (5G) communication technology development makes it possible for distributed resources to participate in power system frequency regulation. This paper models the whole-chain communication time delay of distributed frequency regulation resources participating in automatic generation control (AGC) via a 5G network. The whole-chain communication time delay consists of time delay in the radio access network, backhaul network, and transmission and core networks. We integrate the time delay model into the power system frequency regulation model to reveal its impact on frequency regulation performance. In addition, we analyze the feasibility of the distributed variable renewable energy (VRE) participating in AGC and calculate the capabilities needed for different VRE penetrations. Finally, we provide a case study on the HRP-38 system. The results show that the communication time delay will substantially impact the frequency regulation performance when the VRE penetration is above 40%. In addition, distributed VRE may be a promising AGC resource. Distributed VRE that accounts for approximately 5% of the total installed VRE capacity is needed to participate in AGC via a 5G network, which could maintain the frequency performance of the power system with above 65% VRE penetrated at a normal level.

Constructing virtual power plants (VPPs) based on cellular base stations (CBSs) can effectively enable the CBSs to participate in power system operations. Then, like VPPs constructed by other distributed resources, it is essential for CBSbased VPPs to conduct self-scheduling at the day-ahead stage. However, the operational flexibility of CBS-based VPPs has yet to be systematically investigated, so the dispatch potential cannot be fully explored. This paper proposes a self-scheduling framework based on the device-level modeling of CBS operational flexibility. Both the DC part and the AC part of CBSs are systematically studied. In the scheduling framework, the backup storage units at the DC part are arranged to utilize the spare capacity to conduct temporal arbitrage while guaranteeing the power supply reliability requirement; the cooling infrastructures at the AC part are dispatched with optimized schemes with temperature prerequisites. The synergy effect improves the scheduling of CBSbased VPPs and reduces operation costs. Case studies validate the proposed framework.

The concept of capacity credit (CC) has been proposed to evaluate the contribution of generators for system adequacy since the 1960s. CC determines the extent to which the added unit affects the generation adequacy of the system. The equivalent reliability builds the bridge between the evaluated unit and the virtual firm capacity. This article firstly summarizes the reliability equivalent criteria for CC of generators and lists various CC definitions. Second, the methodologies for evaluating CC are categorized into three parts in terms of the modeling of the reliability function and the solving strategies. Then, a theoretic expression from several analytical methods is proposed to better interpret the characteristics of the CC function. Some discussions of CC are conducted afterwards. The applications of CC on power system planning and capacity market are presented. Finally, we propose four aspects of future work well worth further investigations.

With increasing distributed energy resource integration, future power and energy systems will be more decentralized using advanced Internet of Things (IoT) technologies. Integrated energy systems (IESs) boost the whole energy efficiency by coordinating multiregional energy resources and networks. However, distributed coordination of the IES requires different subregions or energy hubs (EHs) to share their sensitive information (e.g., energy demands and operation status) explicitly, which poses serious privacy leakage. To this end, secure multiparty computation (SMPC) is innovatively introduced to the distributed optimization of the IES in this article. First, the standardized modeling of multiple interconnected EHs with the linearized network models is formulated to analyze the IES’s inherent energy and information interaction comprehensively. Then, a privacy-preserving distributed optimal energy flow algorithm is proposed by combining the Paillier Cryptosystem mechanism with the alternating direction multiplier method (ADMM). Theoretical analysis proves the proposed method is convergent without sharing sensitive information in plaintext. Numerical experiments on a three-subregions IES validate that the proposed method has better convergence performance than the differential privacy-based method. Results show that the maximum relative error of the distributed optimal solutions with various step sizes is no more than 0.072% compared with the centralized method.

Power systems around the world are experiencing an energy revolution that substitutes fossil fuels with renewable energy. Such a transition poses two significant challenges: highly variable generators that add short-term and long-term difficulties for supply–demand balance, and a high proportion of convertor-based devices that may jeopardize power system security and stability. At the same time, machine learning techniques provide more opportunities to study the complex power system security and stability problems. This article summarizes the machine learning framework to embed security rules into power system operation optimization under high renewable energy penetration. First, we explore how high penetration renewable energy impacts power system security and stability. Then, we review how the complex security and stability boundary of power systems is modeled using various machine learning techniques. Finally, we show how the machine learning model is transformed into optimization constraints that can be embedded into the power system operation model. The framework is substantiated through case studies of practical power systems.

Multienergy systems (MESs) are able to unlock the energy system flexibility using the coupling across multiple energy sectors. Such coupling contributes to improving the overall energy efficiency and promoting the accommodation of renewable energy. Among a wide range of literature, this article provides a perspective of network analytics on how to model, optimize, and conduct low-carbon analysis on MESs. The energy sector coupling involves different levels, for example, from a single building to nationwide. In this article, we categorize multienergy networks into two levels, that is, the district level that covers a relatively small area such as a campus or a community, where the energy conversion and utilization is the major focus, and the multiregion level that covers a relatively large area such as a big city, a province, or the whole country, where the energy transmission is the major concern. We first review the state-of-the-art multienergy networks standardized modeling approaches including: 1) energy hub (EH) model for district level energy networks; 2) network models, including power, heat, and gas steady-state and dynamic network models, for multiregion level energy networks; and 3) load models, including electricity, heat, and gas load forecasting models. Second, we explore the planning and operation methods for both district level and multiregion level energy networks. Third, we introduce a special technique named the carbon emission flow (CEF) model that is able to calculate the equivalent CO2 emission associated with the energy flows in multienergy networks. We also demonstrate how the technique can help multienergy networks planning and operation toward a low carbon society. Finally, we envision several further key research topics in the field of multienergy networks.

The integration of multienergy systems (MES) provides an important opportunity to increase the technical, economic, and environmental performance of the overall system compared with separately operated energy systems. The operation of district MES encounters numerous uncertainties such as fluctuations in energy prices as well as loads in different energy forms. In this paper, we formulate a probabilistic optimal energy flow (P-OEF) model of a district MES into a linear program (LP) problem based on the linear energy hub model. We propose an improved multiparametric LP (MPLP) based online dictionary-learning approach to efficiently calculate the P-OEF problem. We then derive a generalized critical region theory of the MPLP problem to jointly consider the change of parameters in terms of both the objective function and the constraints of P-OEF in MES. Finally, we explore the physical significance of the MPLP's critical region in the P-OEF problem. We perform a case study to verify the validity and effectiveness of the proposed approach compared to the traditional Monte Carlo approach for the P-OEF of the MES.