Flexibility-Enhancing Charging Station to Support the Integration of Electric Vehicles

This paper discusses the Inspiria charging station facility in Norway, which enables various charging point operators to offer different charging systems for different purposes and needs. The charging station can be considered a specific case of a shared economy, as users share the same infrastructure and much of the same space. By utilizing new technology and the differences in charging needs in an innovative way, the power requirements for charging can be controlled and the severity of high-load periods can be reduced—both within the charging station’s system and outside it. Using historical traffic data from the Inspiria charging station’s area and Monte Carlo simulations, this study investigated the impact of charging on the grid—both in the current period and in the future. Attention was paid to the impact associated with the usage of superfast chargers. The possibility of containing grid disturbances through utilization of local flexibility was investigated. Finally, we investigated the benefits that the charging station model brings to charging point operators and car owners. The research reported provides support for ambitions for accelerated roll-out and increased density of cost-effective charging points, the wider implication of which concerns the transition to fossil-free transport and the utilization of locally generated, renewable energ

The FlexNett Simulator

Published version, available at http://dx.doi.org/10.1088/1755-1315/352/1/012005, by the license Creative Commons Attribution 3.0 This paper documents research conducted in the Norwegian FLEXNETT project.It describes a new tool that was developed to study the future impact of prosumers with PV panels on the grid in Norway and the potential energy flexibility that lies with residential prosumers. Systematic use of energy flexibility can be an important instrument for managing peak loads and voltage problems in weak power grids. The influx of distributed energy resources can amplify this problem, but also help to resolve it. Self-balancing neighborhoods can be very attractive. This implies that loads related to energy demands can be curtailed and leveled out by different controllable devices or managed by using local energy production in the area to reduce the impact on the general distribution grid. The simulation tool is GIS based and can be applied to study the situation related to a single household, a neighborhood or in a specific transformer area. Unlike similar tools that address production yields over a period, the FLEXNETT Simulator addresses production and energy dynamics down to every 10 minutes. Due to the relatively low solar angle in Norway and rapidly changing weather these dynamics can be very prominent and induce local impact that is specific to a house or a neighborhood. The paper further describes how a recurrent neural network has been used as an engine to produce realistic values for the simulator

Bringing Business and Societal Impact Together in an Evolving Energy Sector

Source at http://www.jocet.org/. As the challenges associated with sustainability, urbanization, life quality and demography become more imminent, companies are adapting to the changing requirements by means of revised strategic approaches. Thus, enterprises are increasingly deviating from the traditionally absolute priority of maximizing total return for shareholders. While this priority is still important, businesses are also looking at the total societal impact (TSI), which represents a collection of measures and assessments that incorporate the economic, social and environmental impacts of their products and services [1]. This paper focuses on the compound influence that TSI may have within the energy domain. In particular, the business opportunities resulting from the Horizon 2020 funded project INVADE are being discussed but seen from the perspective of a socially responsible corporate strategy. Referring to discussions, analyses and undertaken initiatives this paper concludes that business models which incorporate environmentally friendly, local and social and fair energy are capable of accelerating business growth for the concerned companies

Toward Interactive Music Generation: A Position Paper

Music generation using deep learning has received considerable attention in recent years. Researchers have developed various generative models capable of imitating musical conventions, comprehending the musical corpora, and generating new samples based on the learning outcome. Although the samples generated by these models are persuasive, they often lack musical structure and creativity. For instance, a vanilla end-to-end approach, which deals with all levels of music representation at once, does not offer human-level control and interaction during the learning process, leading to constrained results. Indeed, music creation is a recurrent process that follows some principles by a musician, where various musical features are reused or adapted. On the other hand, a musical piece adheres to a musical style, breaking down into precise concepts of timbre style, performance style, composition style, and the coherency between these aspects. Here, we study and analyze the current advances in music generation using deep learning models through different criteria. We discuss the shortcomings and limitations of these models regarding interactivity and adaptability. Finally, we draw the potential future research direction addressing multi-agent systems and reinforcement learning algorithms to alleviate these shortcomings and limitations

E-Mobility and Batteries—A Business Case for Flexibility in the Arctic Region

This paper provides a method for determining the economic incentives and limitations for a battery used for peak clipping, with the goal of finding an optimal mix between the battery’s power density and energy density. A ratio called the R-factor has been introduced, which helps determine the energy demand to curb the peak. The paper’s results embrace different investment scenarios showing what battery capacity can be expected, dependent on interest rates, payback time and potential savings in power tariffs due to curtailment. In addition, the paper introduces the “wrench and cut” concept, which can help improve the investment case for batteries by combining battery operations with standard demand response operations. In particular, the effect of using a limited form of demand response-based load deactivation together with a battery has been analyzed. The investigation provided raises a point that battery degradation must be taken into account to prevent the reduction of battery life and possibly the needed payback period. The ultimate target of the presented research refers to vehicle-to-grid/vehicle-to-building developments in the Arctic region, where a vehicle is considered a mobile battery and where flexibility can be delivered in a cost-efficient way

Investigation of wireless electrification for a reconfigurable manufacturing cell

Reconfigurable manufacturing systems (RMS) with a rearrangeable structure can quickly adjust their productivity to meet the dynamic market changes and the demand for high-variety products. Industry 4.0 technologies have enhanced the RMS flexibility and made the automation of the reconfiguration of the manufacturing system possible. As an Industry 4.0 technology, wireless power transfer (WPT) can further increase the flexibility of RMS by providing safe, reliable, and maintenance-free autonomous charging. This paper examines the wireless electrification of RMS by investigating different WPT configurations that increase flexibility and autonomy, creating a highly flexible RMS. It also proposes a battery charging platform for further enhancement of the flexibility of RMS. As a low-cost WPT solution, the paper tests capacitive charging systems. The proposed charging system has about 135 W power transfer capability at a 5 cm distance and about 84% efficiency

Towards smart layout design for a reconfigurable manufacturing system

Global competition and increased variety in products have created challenges for manufacturing companies. One solution to handle the variety in production is to use reconfigurable manufacturing systems (RMS). These are modular systems where machines can be rearranged depending on what is being manufactured. However, implementing a rearrangeable system drastically increases complexity, among which one challenge with RMS is how to design a new layout for a customized product in a highly autonomous and responsive fashion, known as the layout design problem. In this paper, we combine several Industry 4.0 technologies, i.e., IIoT, digital twin, simulation, advanced robotics, and artificial intelligence (AI), together with optimization to create a smart layout design system for RMS. The system automates the layout design process of RMS and removes the need for humans to design a new layout of the system

Optimal midterm peak shaving cost in an electricity management system using behind customers' smart meter configuration

This paper analyses a local electricity system (LES) comprising photovoltaic production (PV), a connection to the distribution network, local loads and an energy storage system (ESS). Given the flexibility of the ESS, the LES can provide a peak shaving service (PSS) to the grid operator based on the actual monthly power tariff. This paper proposes a stochastic mixed-integer linear programming problem that maximises the expected operating profit of the LES midterm. Assuming a behind customers’ smart meter configuration, income is derived from selling the energy of prosumers to other external electrical areas. If the costs are higher than the income, the net profit will be negative, i.e. a net loss. The cost component of the objective function can be reduced through the management of local resources and by providing PSS to the distribution network operator to minimise the power cost of the monthly power tariff. The model is tested for 720 h (considering a month of 30 days) in three cases: (i) without PV and ESS; (ii) with PV and ESS, where losses are 0%; (iii) with PV and ESS, where losses are 18%. Due to the monthly power tariff, the net loss of the LES is reduced through the optimal management of local resources when the ESS losses are lower than 18%. To assess seasonal implications about the LES, the 12 months of the year are also tested. The month of October indicated the highest peak shaving, while the lowest peak shaving depended on the ESS losses

Using Deep Learning Methods to Monitor Non-Observable States in a Building

This paper presents results from ongoing research with a goal to use a combination of time series from non-intrusive ambient sensors and deep recurrent neural networks to predict room usage at a university campus. Training data was created by collecting measurements from ambient sensors measuring room CO2, humidity, temperature, light, motion and sound, while the ground-truth counts was created manually by human observers. Results include analyses of relationships between different sensor data sequences and recommendations for a prototype predictive model using deep recurrent neural networks

Utilizing Local Flexibility Resources to Mitigate Grid Challenges at Electric Vehicle Charging Stations

Charging of electric vehicles (EVs) on a large scale can cause problems for the grid. Utilizing local flexibility resources, such as smart charging, stationary battery, vehicle-to-grid applications, and local generation can be an efficient way to contain the grid challenges and mitigate the need for grid reinforcement. Focusing on the INSPIRIA charging station located in Norway, this paper investigates the possibility of coping with imminent grid challenges by means of local flexibility. First, the potential grid challenges are estimated with the help of Monte Carlo simulations. Second, cost and performance for the various local flexibility sources are presented. Third, an analysis of the choice of battery, charging process, and battery economy are provided. Finally, the paper discusses the optimal mix of flexibility resources to efficiently mitigate grid challenges at the INSPIRIA charging station