Charalambos Elias

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.

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 interconnection issues of the Intelligent Electrical Networks (IENs) with the existing ones, as well as the deployment of the appropriate load forecasting demand techniques. The concept of IENs has recently emerged as a potential solution to the global energy problem, due to the more flexible dispersed production units that are involved. In the same context, additional issues, such as the reduction of production-transfer-distribution costs as well as power losses are analyzed as well, via the penetration of Renewable Energy Sources (RESs). To this end, it is necessary to use appropriate advanced infrastructure, where in combination with modern telecommunication networks will make possible the full exploitation of IENs. Moreover, to make the electricity system more efficient, to avoid voltage and frequency imbalance issues and to implement optimal production and consumption planning, the Electric Load Forecast (ELF) is a vital process and plays a key role in the planning as well as in the management of electricity systems. Therefore, recent state of the art approaches in ELF methods are discussed as well.

New methods for state estimation are required due to the complexity of the topology of transmission and distribution systems, and the predictability in the management of prosumer dispatch. This paper describes a pilot project in Greece that, in accordance with OneNet’s architecture, addresses the challenges of congestion and balancing management that system operators face due to the high penetration of renewable energy sources. The respective data requirements and the IT/OT environment are described, as well as the interconnections among the various modules and functionalities. Available resources of the grid’s flexibility are identified, and the implementation of an integrated monitoring system based on efficient forecasting of volatile generation and demand is addressed. Congestion management and frequency and voltage control are in the center of interest of the demonstrator where, in close collaboration with system operators, respective network models are being developed.

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.

The shift towards distributed generation and microgrids has renewed the interest in forecasting algorithms and methods, which need to take into account the advances in information, metering and control technologies in order to address the challenges of forecasting problems. Technologies such as machine learning have been proven useful for short-term electricity load forecasting, especially for microgrids, as they can also take into account several types of historical data and can adapt to changes often encountered in small-scale systems and on a short time scale. In this paper, we present a flexible and easily customized modular toolbox, called Divinus, for electricity use profiling and forecasting in microgrids. Divinus may support a variety of machine learning algorithms for forecasting and profiling that can be used independently or combined. For demonstration purposes, we have implemented Self-Organizing Maps for profiling and k-Neighbors for forecasting. The testing of the platform was based on electricity consumption data of the Euripus campus of the National and Kapodistrian University of Athens in Evia, Greece, from January 2010 till March 2018. The tests that have been carried out so far show that the platform can be easily customized and the algorithms examined yield high accuracy and acceptable mean errors for the case of a university campus energy profile.