Perception is a vital part of driving. Every year, the loss in visibility due to snow, fog, and rain causes serious accidents worldwide. Therefore, it is important to be aware of the impact of weather conditions on perception performance while driving on highways and urban traffic in all weather conditions. The goal of this paper is to provide a survey of sensing technologies used to detect the surrounding environment and obstacles during driving maneuvers in different weather conditions. Firstly, some important historical milestones are presented. Secondly, the state-of-the-art automated driving applications (adaptive cruise control, pedestrian collision avoidance, etc.) are introduced with a focus on all-weather activity. Thirdly, the most involved sensor technologies (radar, lidar, ultrasonic, camera, and far-infrared) employed by automated driving applications are studied. Furthermore, the difference between the current and expected states of performance is determined by the use of spider charts. As a result, a fusion perspective is proposed that can fill gaps and increase the robustness of the perception system.

Previous studies have shown that electric water heaters (EWH) have strong potential in demand-side management applications, more precisely because they offer energy storage capability and then can be employed as shift loads. However, the challenge of EWH curtailment strategies is to minimize the impact on the hot water availability while shaving the peak of consumption during critical periods. The success of such strategies depends highly on the knowledge of the consumption behavior of each user. Thus, appropriated modeling and consumption analysis could yield better management strategies. This study proposes an electric water heater control strategy based on dynamic programming and power consumption profile classification. An adaptive clustering process allows recognizing the clients who contribute to the highest power consumption during the peak periods. The analysis and simulation indicate that an appropriate control on the group of users could be implemented to reduce peak demand and to meet the hot water demand. A K-means clustering algorithm has been used for cluster analysis. The silhouette method has been applied to estimate the appropriate number of clusters.

High penetration of selfish Home Energy Management Systems (HEMSs) causes adverse effects such as rebound peaks, instabilities, and contingencies in different regions of distribution grid. To avoid these effects and relieve power grid stress, the concept of HEMSs coordination has been suggested. Particularly, this concept can be employed to fulfill important grid objectives in neighborhood areas such as flattening aggregated load profile, decreasing electricity bills, facilitating energy trading, diminishing reverse power flow, managing distributed energy resources, and modifying consumers' consumption/generation patterns. This paper reviews the latest investigations into coordinated HEMSs. The required steps to implement these systems, accounting for coordination topologies and techniques, are thoroughly explored. This exploration is mainly reported through classifying coordination approaches according to their utilization of decomposition algorithms. Furthermore, major features, advantages, and disadvantages of the methods are examined. Specifically, coordination process characteristics, its mathematical issues and essential prerequisites, as well as players concerns are analyzed. Subsequently, specific applications of coordination designs are discussed and categorized. Through a comprehensive investigation, this work elaborates significant remarks on critical gaps in existing studies toward a useful coordination structure for practical HEMSs implementations. Unlike other reviews, the present survey focuses on effective frameworks to determine future opportunities that make the concept of coordinated HEMSs feasible. Indeed, providing effective studies on HEMSs coordination concept is beneficial to both consumers and service providers since as reported, these systems can lead to 5% to 30% reduction in electricity bills.

Demand Response (DR) programs show great promise for energy saving and load profile flattening. They bring about an opportunity for indirect control of end-users’ demand based on different price policies. However, the difficulty in characterizing the price-responsive behavior of customers is a significant challenge towards an optimal selection of these policies. This paper proposes a Demand Response Aggregator (DRA) for transactive policy generation by combining a Reinforcement Learning (RL) technique on the aggregator side with a convex optimization problem on the customer side. The proposed DRA can maintain users’ privacy by exploiting the DR as the only source of information. In addition, it can avoid mistakenly penalizing users by offering price discounts as an incentive to realize a satisfying multi-agent environment. With an ensured convergence, the resultant DRA is capable of learning adaptive Time-of-Use (ToU) tariffs and generating near-to-optimal price policies. Moreover, this study suggests an off-line training procedure that can deal with issues related to the convergence time of RL algorithms. The suggested process can notably expedite the DRA convergence and, in turn, enable online applications. The developed method is applied to a set of residential agents in order to benefit them by regulating their thermal loads according to generated price policies. The efficiency of the proposed approach is thoroughly evaluated from the standpoint of the aggregator and customers in terms of load shifting and comfort maintenance, respectively. Besides, the superior performance of the selected RL method is represented through a comparative study. An additional assessment is also conducted by use of a coordination algorithm to validate the competitiveness of the recommended DR program. The multifaceted evaluation demonstrates that the designed scheme can significantly improve the quality of the aggregated load profile with a low reduction in the aggregator’s income.

Paralleled operation of voltage-source inverters (VSIs) is currently achieved by using voltage/frequency droop control techniques which requires the knowledge of the system parameters. Otherwise, centralized control techniques with robust communication among VSIs controllers are also used. This paper presents a new control strategy which allows the load sharing between the power sources of an ac microgrid without centralized controller or any communication among the VSIs; only local measurements of voltage and output current are used. The dispatchable sources (e.g., fuel cells) of the microgrid are operated using voltage control with a direct droop scheme, and the nondispatchables or intermittent ones (e.g., wind turbine generators) are operated using power control with a complementary inverse droop scheme (D-Droop + I-Droop). The number of operating sources can be changed online without any modification needed on the VSI controllers. The proposed VSI controllers are based on the variable frequency adaptive linear neuron with frequency-locked loop for the VSIs system synchronization, voltage/power and signal estimation. Experimental results using field-programmable gate array devices for the implementation of each VSI control in the microgrid test bench demonstrated the validity of the proposition.

Greenhouse technologies provide controlled environmental conditions for crop growth, often incorporating automation to enhance productivity. Energy management, which involves monitoring, controlling, and conserving energy, is particularly crucial in northern climates, where greenhouses are among the most energy-intensive sectors of agriculture. This paper presents a comprehensive review of state-of-the-art greenhouse technologies from an energy management perspective, exploring their role in enhancing efficiency and sustainability. It examines the energy management framework, key technological advancements, benefits, challenges, and available solutions in the market. Furthermore, it discusses principles and methods of energy optimization, best practices for sustainable greenhouse operations, and emerging trends in smart grids, renewable integration, and automation. Unlike previous studies primarily focusing on agricultural and control perspectives, this review highlights new insights into integrating greenhouse energy management with smart grid participation, leveraging model predictive control (MPC) for energy optimization, multi-agent reinforcement learning (DRL) for adaptive control, and digital twin technology for real-time system modeling. By bridging greenhouse energy management with transactive energy platforms, this paper underscores the importance of intelligent, data-driven decision-making in enhancing efficiency, sustainability, and system resilience while minimizing environmental impact.