Forecasting Recharging Demand to Integrate Electric Vehicle Fleets in Smart Grids

Electric vehicle fleets and smart grids are two growing technologies. These technologies provided new possibilities to reduce pollution and increase energy efficiency. In this sense, electric vehicles are used as mobile loads in the power grid. A distributed charging prioritization methodology is proposed in this paper. The solution is based on the concept of virtual power plants and the usage of evolutionary computation algorithms. Additionally, the comparison of several evolutionary algorithms, genetic algorithm, genetic algorithm with evolution control, particle swarm optimization, and hybrid solution are shown in order to evaluate the proposed architecture. The proposed solution is presented to prevent the overload of the power grid

A Review of Energy Management of Renewable Multisources in Industrial Microgrids

This review aims to consolidate recent advancements in power control within microgrids and multi-microgrids. It specifically focuses on analyzing the comparative benefits of various architectures concerning energy sharing and demand cost management. The paper provides a comprehensive technical analysis of different architectures found in existing literature, which are designed for energy management and demand cost optimization. In summary, this review paper provides a thorough examination of power control in microgrids and multi-microgrids and compares different architectural approaches for energy management and demand cost optimization

Attributes of Big Data Analytics for Data-Driven Decision Making in Cyber-Physical Power Systems

Big data analytics is a virtually new term in power system terminology. This concept delves into the way a massive volume of data is acquired, processed, analyzed to extract insight from available data. In particular, big data analytics alludes to applications of artificial intelligence, machine learning techniques, data mining techniques, time-series forecasting methods. Decision-makers in power systems have been long plagued by incapability and weakness of classical methods in dealing with large-scale real practical cases due to the existence of thousands or millions of variables, being time-consuming, the requirement of a high computation burden, divergence of results, unjustifiable errors, and poor accuracy of the model. Big data analytics is an ongoing topic, which pinpoints how to extract insights from these large data sets. The extant article has enumerated the applications of big data analytics in future power systems through several layers from grid-scale to local-scale. Big data analytics has many applications in the areas of smart grid implementation, electricity markets, execution of collaborative operation schemes, enhancement of microgrid operation autonomy, management of electric vehicle operations in smart grids, active distribution network control, district hub system management, multi-agent energy systems, electricity theft detection, stability and security assessment by PMUs, and better exploitation of renewable energy sources. The employment of big data analytics entails some prerequisites, such as the proliferation of IoT-enabled devices, easily-accessible cloud space, blockchain, etc. This paper has comprehensively conducted an extensive review of the applications of big data analytics along with the prevailing challenges and solutions

ICT Integration for Electric Vehicles as Data Collector and Distributor of Data Services

At present, automotive companies are very interested in information communication technology (ICT), electric vehicle sensors, and their associated intelligent transport systems (ITS) applications. The production of in-vehicle sensors is developing continuously because of their proven benefits in preventing accidents, improving driving e?ciency, and collecting data for sensor-based services. These advantages are not only limited to the vehicle’s driver but also to the drivers of other vehicles and web database server as third parties. In this paper, we present Vehicle as a Data Collector and Distributor (VADCD), a concept that explains how a sensor-equipped vehicle can be considered as a pivotal, mobile source of sensory data and sensor-related applications and services

Concept For Databased Sales And Resource Planning For Re-Assembly In The Automotive Industry

In linear economy, the growing wealth in the world is linked to a growing resource consumption and greenhouse gas emission. This results in a shortage of primary resources, environmental destruction through resource extraction, and global warming. A high productivity of the manufacturing industry, overcapacities and a decrease in the value of existing products intensify this situation. Circular economy offers resource-efficient value addition by multiple utilization of resources. One challenge in this form of value creation is the duration of reconditioning processes and the lack of product innovation in reconditioned products. To bring products back to the market as quickly as possible, the method of Re-Assembly is introduced and focused in this paper. Re-Assembly can be defined as reconditioning old products into new or higher-valued products, by assembling new or remanufactured parts and components after disassembly. However, manufacturing companies face difficulties in industrialization of such methods. In practice, one of the biggest challenges is the mid- and long term planning of the reconditioning process. Due to uncertainties in the quality and quantity of the returning end-of-life products the resulting reconditioning process is challenging to predict in terms of process time and production costs. To encounter this, this paper presents a concept for sales and resource planning in the context of Re-Assembly. In the first step the uncertainties for the long term production planning and the resulting data requirements are identified. Based on this, a concept for sales and resource planning is presented. The approach is based on the Internet of Production reference framework and includes data from the whole product lifecycle. As the area of application, the automotive industry is chosen as it is the largest manufacturing industry in Germany and already leading in the recording of usage data of their products

Intelligent Microgrids

Center for Electromechanic