Acting as a key to future environmentally friendly transportation systems, electric vehicles (EVs) have attached importance to develop fast charging technologies to accomplish the requirement of vehicle users. However, fast charging behaviors would cause degradations in EVs' batteries, as well as negative effects like new demand peak and feeder overloads to the connected distribution network, especially when plugging in large scale EVs. Decentralized coordination is encouraged and our goal is to achieve an optimal strategy profile for EVs in a decentralized way considering both the need of fast charging and reducing degradations in batteries and the distribution network. In this article, we innovatively model the EV fast charging problem as an optimization coordination problem subject to the coupled feeder capacity constraints in the distribution network. The need of fast charging is expressed by the total charging time, and the relative tendency to fully charge within the desired time period. We introduce a ℓ₁-norm of the charging strategy which is non-convex to represent the total charging time, and apply the ℓ₁-norm minimization to approximate the sparse solution of ℓ₁-norm minimization. The shorter the charging horizon is the stronger willing of fast charging the user has. The objective of the optimization problem tradeoffs the EVs' battery degradation cost, the load regulation in the distribution network, the satisfaction of charging and the total charging time, which is non-separable among individual charging behaviors. Even though alternating direction method of multipliers (ADMM) has been widely applied in distributed optimization with separable objective and coupled constraints, its decentralized scheme cannot be applied directly to the underlying non-separable EV charging coordination problem. Hence, a hierarchical algorithm based on ADMM is proposed such that the convergence to the optimal strategies is guaranteed under certain step-size parameter. Furthermore, a receding horizon based algorithm is proposed considering the forecast errors on the base demand and the EV arrival distribution. The results are demonstrated via some simulation results.

The growing renewable integration significantly enhances the seasonal electricity imbalance of the electric energy system. However, traditional power system planning methods usually take into account the hourly power balance within typical days but seldom consider the seasonal imbalance risk in the long-term timescale. Therefore, this paper proposes a generation-transmission-storage co-planning model considering the seasonal imbalance risk brought by the long-term uncertainty of renewable generation in the power system. The Conditional Value at Risk (CVaR) method is introduced to quantify the seasonal imbalance risk. The power system energy balance constraints are decoupled into short-term (i.e., hourly) and long-term (i.e., monthly) energy balance so that the operation of the power system can be considered within different timescales and the seasonal imbalance risk could be calculated. The validity of the proposed model is first investigated via a modified Garver's 6-bus system case study. Furthermore, the research on the HRP-38 system (HRP represents high renewable penetration) illustrates that our proposed model could effectively control the energy imbalance risk from multiple timescales. Finally, the proposed method is applied to the whole China power system at the province level, proving that the proposed planning method could effectively enhance the reliability of China's power system, decreasing both the average annual short-term and long-term energy imbalance by 30%.

Networked Load frequency control (LFC) inevitably subjects to constrained bandwidth and time delays. However, the network traffic essentially varies from time to time and then the bandwidth status may be only busy during some specific peak periods but idle during others. This paper proposes a bandwidth-aware event-triggered load frequency control (ETLFC) scheme for multi-area power systems under time-varying delays by incorporating bandwidth-aware adaptive regulation scheme into decentralized ETLFC scheme. Different from existing time-delay ETLFC model, a model of aperiodic sampled-data system with time delay is constructed for the ETLFC scheme to obtain less conservative stability and co-design conditions based on looped Lyapunov functional. The regulation scheme is designed to adaptively adjust the threshold parameter of the ETLFC scheme based on variable bandwidth status and system output change in order to lessen communication burden while ensuring desired control performance. For fixed output change, a better bandwidth status generates a smaller threshold parameter to raise the triggering frequency in order to utilize available communication resources to improve control performance rather than unilaterally reduce data transmissions in the existing research. Case studies are undertaken by an IEEE 39-bus test system to show the effectiveness and advantages of the proposed scheme.

This work deals with decentralized optimization problems where a collection of network agents operate selfishly and individually under a set of coupling constraints among them. This collection of problems are motivated by coordinating electric vehicle (EV) charging in a feeder capacity-constrained distribution network where each EV makes decisions locally to achieve the system’s global benefit. First, a centralized scheme is established and admits an optimal coordination behaviour that minimizes the aggregate generation cost and all the EVs’ local costs. However, the coupling of the charging behaviours among the EV population brings challenges to the design of the decentralized algorithm, which is expected to converge to the global optimum. A partial augmented Lagrangian method is presented to disperse the coupling constraint by introducing a penalty term. Combined with the gradient projection method, an iterative procedure is designed such that each EV and feeder line update in parallel until satisfying the terminal condition. The convergence can be guaranteed under some certain mild conditions, and the convergence rate to the global optimum is analysed as well. We include some simulation results to demonstrate the performance of the developed results.

The transition of coal power generation is critical for the net-zero carbon emission evolution of power systems. It profoundly affects and is affected by the development of other parts of the power system so that a source-grid-load-storage co-planning model is required for the analysis. This paper proposes an efficient linear-form coal power transition planning model for long-term and large-scale planning problems. It considers six coal power transition alternatives, including new installation, at-life retirement, early retirement, rehabilitation, flexibility retrofit, carbon capture and storage retrofit, and covers a full life cycle of coal power from the temporal scale. First, coal power generation is clustered according to type and age to reduce the complexity in decisions and constraints in the planning model. Second, six transition alternatives are universally modelled considering the age dynamics of coal power during the planning horizon. Finally, a multistage coal power transition planning model coordinated with the expansion planning of source-grid-load-storage is formulated. Case studies on 8-stage 31-bus real-world China’s power system from 2020 to 2060 suggest the effectiveness of the proposed model. The transition path of coal power with the coordination of different transition alternatives and the changing role of coal power towards carbon neutrality are analysed.

In light of global climate change, China has set strategic goals for carbon peaking by 2030 and carbon neutrality by 2060, emphasizing the necessity of constructing a new power system with a high proportion of renewable energy sources. As coal-fired power plants are the main carbon emissions source in the power system, their low-carbon transition and morphology structure optimization is crucial. This paper explores the critical role of dynamic carbon emission factors within source–network–storage power system planning and proposes an innovative inverse dynamic carbon emission factor that effectively captures the nonlinear relationship between load rates and emissions. Comparative analyses using the HRP-38 test case demonstrate that the inverse model enhances computational efficiency, reduces solution times, and more accurately reflects the emissions characteristics of coal-fired units across varying operational conditions. Furthermore, the inverse model offers improved economic performance and broader flexibility in unit selection, highlighting its potential to balance carbon emissions control and economic optimization in future power system planning.