Path planning and obstacle avoidance are essential for autonomous driving cars. On the base of a self‐constructed smart obstacle avoidance car, which used a LeTMC‐520 depth camera and Jetson controller, this paper established a map of an unknown indoor environment based on depth information via SLAM technology. The Dijkstra algorithm is used as the global path planning algorithm and the dynamic window approach (DWA) as its local path planning algorithm, which are applied to the smart car, enabling it to successfully avoid obstacles from the planned initial position and reach the designated position. The tests on the smart car prove that the system can complete the functions of environment map establishment, path planning and navigation, and obstacle avoidance.

Day-ahead electricity price prediction is crucial for market participants to make optimal trading decisions. The implementation of the five-minute settlement (5MS) process in the Australian National Electricity Market (NEM) on October 1, 2021, reduced the settlement interval from 30 minutes to 5 minutes. This change has led to more frequent adjustments in pricing, allowing for a more accurate reflection of real-time supply and demand conditions. However, this increased frequency has significantly heightened the complexity of price fluctuations in the wholesale market. Consequently, conventional machine learning and deep learning methods struggle to provide accurate predictions at this higher resolution. Since electricity prices are fundamentally determined by the supply-demand balance and the bidding behaviors of market participants, this work introduces individual participant's bidding behaviors into the prediction model. We fine-tune a pre-trained Large Language Model (LLM) to create bidding behavior agents, which forecasts day-ahead bidding behaviors. Moreover, market sentiment plays a significant role in electricity price volatility, yet it remains challenging to quantify and assess its impact. To address this, we employ a pre-trained LLM to analyze online resources, incorporating market sentiment into the price prediction model. Additionally, to enhance the accuracy of spike predictions, we improve the conditional time series generative adversarial network (CTSGAN) model by utilizing a spike confusion matrix and further strengthen the model by integrating bidding behavior and market sentiment as inputs. Case studies demonstrate that the proposed model significantly improves both electricity price and spike prediction accuracy, offering a robust tool for market participants to navigate the complexities of the modern electricity market.

Electric vehicles (EVs) have emerged as a promising solution to reduce greenhouse gas emissions in urban areas. The construction of electric vehicle charging stations (EVCSs) is critical to the development of the EV industry. This paper proposes a novel integrated fuzzy inference system (FIS)-based planning framework for determining the optimal locations and capacities of EVCSs with PV systems and energy storage units. Several off-site factors that will affect the planning results of EVCSs are analyzed and incorporated into a multi-objective optimization problem, aiming at minimizing the cost of electricity (COE) and emission pollutants simultaneously. The proposed FIS-based planning approach introduces novel fuzzy criteria that account for the nonlinear and difficult-to-model joint effect of social and environmental factors. By incorporating these off-site factors, a more realistic framework for EVCS planning is presented. Numerical studies are conducted on a coupled 33-bus distribution system and 25-bus transportation system to illustrate the proposed planning method. According to the simulation results, by employing the proposed FIS-based planning framework, not only reduces the search space and simplifies the optimization problem, but the results obtained are more realistic according to practical system conditions.

The increasing sales of electric vehicles (EVs) in the market poses critical questions for the public on how to deal with millions of retired batteries (RBs). A number of studies have shown that second-life batteries (SLBs) from EVs usually have a considerable remaining capacity and hence still have additional utilization value. This article proposes a novel fuzzy inference system (FIS)-based planning framework for EV charging stations with wind, PV power, battery energy storage system and the utilization of SLBs (EVCS-WPE-SLBs system). In this framework, a narrowed and the most probable candidate node set for EVCS-WPE-SLBs system which makes the trade-off of several off-site factors from different fields that jointly determine the optimal planning results is obtained, including the installation and utilization requirements of SLBs, the transportation network features and power system properties. Numerical case studies are conducted on a coupled IEEE 33-bus distribution system and 25-node transportation system. According to the simulation results, the reasonable utilization of SLBs in the EVCS-WPE-SLBs system can reduce the total system cost while maintaining the normal operation of the system. Besides, a narrowed and most probable candidate node set not only reduces the search space and simplifies the optimization problem, but the results obtained are more practical according to the actual system conditions in real life.

Power systems are becoming increasingly vulnerable to cyberattacks due to extensive information exchange. This paper proposes a novel fuzzy inference system (FIS)-enhanced data integrity attack (DIA)-recovery framework and analyzes the combined effects of attacks simultaneously launched by groups with different motivations. These range from market participants seeking a competitive edge for personal economic benefits to outsider groups aiming to drive the system into an uneconomic operation state. A twostage optimization problem and a fuzzy-Bayesian approach are formulated to counteract the potential attacks on the Electric Vehicle Charging Management Center (EVCMC), where successful manipulation can result in substantial financial gains or disruptions to the power grid. According to the simulation results: 1) The proposed FIS approach provides a dynamic assessment of the attacker’s capability and the vulnerability of EVCMC to potential DIAs. 2) The combined effects of attack launched by market participants and outsider groups can cause more severe economic impacts compared to attack conducted separately. 3) The proposed attack-recovery scheme can identify the most vulnerable EVCMC to attackers and recover the optimal power dispatch under DIAs.