The goal of the study presented in this article is to provide a general overview of the various aspects related to electric vehicles (EVs), along with all associated emerging challenges and perspectives. In this context, the basic types of EVs and the corresponding charging technologies are analyzed. Since EVs are expected to be a key component of future smart electrical grids (SEG), connection to the grid issues, along with advanced charging techniques (i.e., wireless power transfer), are analyzed as well. To this end, the main features, the requirements of vehicle to grid (V2G) communications, as well as future developments and scenarios of electrification, are also presented and analyzed. Moreover, integration issues with currently deployed fifth generation (5G) mobile wireless networks are also outlined, in order to ensure optimum transmission and reception quality in V2G communications and improved user experience. This integration is also expanded in autonomous vehicles (AVs) technology (self-driving objects), since optimized information processing from various diverse sources is required in order to ensure advanced traffic management aspects.

In a modern and dynamic electricity market, ensuring reliable, sustainable and efficient electricity distribution is a pillar of primary importance for grid operation. The high penetration of renewable energy sources and the formation of competitive prices for utilities play a critical role in the wider economic development. Electricity load and price forecasting have been a key focus of researchers in the last decade due to the substantial economic implications for both producers, aggregators and end consumers. Many forecasting techniques and methods have emerged during this period. This paper conducts a extensive and analytical review of the prevailing load and electricity price forecasting methods in the context of the modern wholesale electricity market. The study is separated into seven main sections. The first section provides the key challenges and the main contributions of this study. The second section delves into the workings of the electricity market, providing a detailed analysis of the three markets that have evolved, their functions and the key factors influencing overall market dynamics. In the third section, the main methodologies of electricity load and price forecasting approaches are analyzed in detail. The fourth section offers a comprehensive review of the existing literature focusing on load forecasting, highlighting various methodologies, models and their applications in this field. This section emphasizes the advances that have been made in all categories of forecasting models and their practical application in different market scenarios. The fifth section focuses on electricity price forecasting studies, summarizing important research papers investigating various modeling approaches. The sixth section constitutes a fundamental discussion and comparison between the load- and price-focused studies that are analyzed. Finally, by examining both traditional and cutting-edge forecasting methods, this review identifies key trends, challenges and future directions in the field. Overall, this paper aims to provide an in-depth analysis leading to the understanding of the state-of-the-art models in load and price forecasting and to be an important resource for researchers and professionals in the energy industry. Based on the research conducted, there is an increasing trend in the use of artificial intelligence models in recent years, due to the flexibility and adaptability they offer for big datasets, compared to traditional models. The combination of models, such as ensemble methods, gives us very promising results.

In modern and dynamic electricity market, ensuring reliable, sustainable and efficient electricity distribution are pillars of primary importance for grid operation. The high penetration of renewable energy sources and the formation of competitive prices for utility, play a critical role in the wider economic development. Electricity load and price forecasting has been a key focus of researchers in the last decade due to the substantial economic implications for both producers, aggregators and end consumers. Many forecasting techniques and methods have emerged during this period. This paper conducts a extensive and analytical review of the prevailing load and electricity price forecasting methods in the context of the modern wholesale electricity market. The study is separated into three main sections. First, the first section delves into the workings of the electricity market, providing a detailed analysis of the three markets that have evolved, their functions and the key factors influencing overall market dynamics. The second section offers a comprehensive review of the existing literature focusing on load forecasting, highlighting various methodologies, models and their applications in load demand forecasting. This section emphasizes the advances that have been made in all categories of forecasting models and their practical application in different market scenarios. The third section focuses on electricity price forecasting methods, summarizing important research papers investigating various modeling approaches to price forecasting. By examining both traditional and cutting-edge forecasting methods, this review identifies key trends, challenges, and future directions in the field. Overall, this paper provides an in-depth analysis leading to the understanding of the state-of-the-art models in load and price forecasting, being an important resource for researchers and professionals in the energy industry.

The increasing adoption of electric vehicles in residential buildings creates challenges for charging infrastructure management, particularly in pricing services to balance revenue, user satisfaction, and grid stability. Traditional pricing methods, such as fixed rates and time-of-use tariffs, cannot adapt to the dynamic nature of charging demand. We propose a reinforcement learning framework for dynamic pricing of residential EV charging stations. The framework formulates the pricing problem as a Markov decision process and employs proximal policy optimization to learn a pricing policy based on real-time conditions. The state representation includes ten features covering temporal indicators, charging loads, grid status, traffic, and weather. A multi-objective reward function balances revenue, station utilization, grid stability, and user satisfaction. The system is trained on 6878 charging sessions from a residential complex in Trondheim, Norway. Compared with fixed pricing and time-of-use pricing, the proposed method achieves an overall score of 0.569, representing improvements of 32.9% and 48.9%, respectively. Sensitivity analysis confirms that the model remains robust across different demand response assumptions. The main contributions include a custom reinforcement learning environment for residential EV charging and empirical evidence that learned policies outperform traditional pricing approaches.

The growing use of electric vehicles in the residential building sector presents new challenges in the management of the charging infrastructure, especially in deciding how to best price the use of it to balance operator revenue and user satisfaction with grid stability. Traditional pricing methods like fixed pricing rates and time-of-use tariffs cannot accommodate the dynamic nature of charging demand, which fluctuates depending on temporal patterns and weather conditions as well as user behavior. This limitation means that the resources are not used optimally and the revenue opportunities are lost during periods of high demand. To overcome this issue, we propose a reinforcement learning framework for dynamic pricing for residential electric vehicle charging stations. The framework models the pricing problem as a Markov Decision Process and uses Proximal Policy Optimization to learn a policy for setting optimal prices of private and shared charging stations according to real-time conditions. The state representation is done using ten features such as temporal indicators, current loading on the grid, grid status, traffic volume, and weather data. A multi-objective reward function is an approach to balance four objectives - revenue maximization, station utilization, grid stability, and user satisfaction. The system is trained on actual charging data from a residential complex in Trondheim, Norway. 6878 charging sessions during a 13-month period are used for training. We compare the learned policy with three baseline technologies: fixed pricing, time-of-use pricing and rule-based pricing. Experimental results show that the proposed approach reaches an overall score of 0.569, which is 32.9% and 48.9% improvements in comparison to fixed pricing and time-of-use pricing, respectively. The learned policy is able to successfully adjust the prices based on different conditions and sustain a balanced performance for all the goals. The main contributions include a custom reinforcement learning environment for residential EV charging pricing, a multi-objective reward formulation, and empirical evidence that learned policies outperform traditional pricing approaches.

The goal of the study presented in this article is to investigate all current issues related to the proper deployment of load forecasting (LF) techniques in smart grids (SGs). The latter concept has recently emerged as a potential solution to the global energy problem as well as to the ever-increasing and diverse consumer demands. To this end, more flexible dispersed production units are involved, mainly based on renewable energy sources (RESs). Another key novelty of SGs is their ability to gather information directly from consumers and production units in real time, thus facilitating optimum network planning and recovery as well as minimization of outage probability. Hence, it is important to use appropriate advanced infrastructure, which, in combination with modern telecommunication networks, will enable the full exploitation of SGs. In this context, to make the electricity system more efficient, avoid voltage and frequency imbalance issues and implement optimal production and consumption planning, LF is a vital process and plays a key role in the management of future electricity systems. Therefore, recent state-of-the art approaches in LF methods are also presented and discussed. In the same context, current limitations and proposals for future work are identified as well.