Microgrids have become an attractive option for distributed generation (DG) with increase in renewable energy sources (RES) and storage systems. The existence of both AC and DC microgrids has led to a new concept of hybrid AC/DC microgrid which consists of both AC and DC grids tied by an interlinking converter (ILC). Such hybrid AC/DC microgrid has the advantages of both AC and DC with increased efficiency and less cost since the conversion between AC and DC is reduced. The management of power in such a grid becomes important for voltage and frequency control. Different voltage and frequency control strategies have been successfully implemented within AC and DC grids, but the control of hybrid microgrid requires further attention with focus on ILC. This study presents an overview of various control schemes used for voltage and frequency regulation in standalone and transition mode operation of hybrid microgrid. A detailed study on hybrid grid structure is presented with different modes of operation. Various ILC strategies based on droop and communication‐based control are presented. Based on the analysis, some recommendations are presented for future research.

Large-scale electric vehicle (EV) integration will introduce new challenges to the complex, constrained, and coupled electric power-traffic networks (PTN). In this paper, a coordinated solution for Stochastic Security Constrained Unit Commitment-Traffic Assignment Problem (SCUC-TAP) is proposed in which stochastic SCUC is used to model the power grid operation with large-scale renewable energy integration, and TAP is used to model the constrained traffic network. The impact of the departure time of commuting EVs on mitigating the congestion in PTN is analyzed based on proposed model. A heuristic algorithm using Benders decomposition is proposed in which the traffic plan is introduced for decoupling the proposed SCUC-TAP and taking the full use of existing software packages of TAP and stochastic SCUC. Two case studies including a 6-bus power system with 9 nodes and 24 directed links traffic network and an IEEE 118-bus system with three independent urban traffic network are used to simulate various scenarios in the coupled networks of power and transportation. The simulation results show that by introducing a flexible EV charge/travel schedule, the power system generation and delivery costs can be further reduced in which most EV users gain from the optimization of transportation, charge/discharge, and power system delivery schedules.

Battery-Based Energy Storage Transportation (BEST) is a potential solution for optimizing the power system operations with a high penetration of wind energy. In this paper, we propose a scenario-based stochastic model for the BEST-integrated power system scheduling. In this model, load and wind energy forecasting inaccuracies and random disturbances are modeled in scenario trees using the Monte Carlo simulation method. Random disturbances represent forced outages of both power system and railway system components, including generation units, transmission lines, railway stations, and railway lines. Benders decomposition is adopted to solve the stochastic model. Two BEST-integrated power systems are used to illustrate the proposed model and the performance of the proposed solution algorithm. The first one is a 6-bus power system integrated with a 3-station and 3-line railway network. The second one is the modified IEEE 118-bus power system integrated with a railway network composed of 8 railway stations and 10 rail lines. Simulation results show that the BEST system implementation is a viable option for managing the large-scale integration of wind power which can reduce the curtailment of wind power and accordingly lower the operation cost of power systems.

This paper evaluates the effect of integrating battery-based energy storage transportation (BEST) by railway transportation network on power grid operation and control. A time-space network model is adopted to represent transportation constraints. The proposed model integrates the hourly security-constrained unit commitment with vehicle routing problem. The BEST solution provides the locational and hourly charging/discharging schedule of the battery storage system. The mobility of BEST will be of particular interest for enhancing the power system resilience in disaster areas where the transmission grid is congested or on outrage. Two cases are used to simulate the BEST including a six-bus power system linking with a three-station railway system, as well as the IEEE 118-bus systems linking with an eight-station railway system. The results show that under certain conditions, the mobility of battery storage system can economically relieve the transmission congestion and lower the operation costs.

Quantifying short-term uncertainty in wind power plays a crucial role in power system decision-making. In recent years, the scenario generation community has conducted numerous studies employing generative models. Among these generative models, diffusion models have shown remarkable capabilities with excellent posterior representation. However, diffusion models are seldom used to quantify renewable energy uncertainty. To fill this research gap, this manuscript proposes a novel conditional latent diffusion model (CLDM) adapted for short-term scenario generation. CLDM decomposes the wind power scenario generation task into deterministic forecasting and forecast error scenario generation. The embedding network is used to regress deterministic forecasts, which reduces the denoising complexity of diffusion models. The denoising network generates forecast error scenarios in a latent space. Subsequently, the wind power scenarios are reconstructed by combining deterministic forecasts and forecast error scenarios. The case study compares with existing state-of-the-art methods, CLDM demonstrates superior evaluation metrics and enhances the denoising efficiency.