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Demand-side management (DSM) is proposed as a key solution to increase electric system flexibility, essential for large-scale deployment of renewable energy generation. System flexibility is required to avoid congestions in electric systems and reduce costs for network operators and consumers. IoT deployment, real-time pricing and cost reductions for electric energy storage opens new opportunities for DSM tools, which help matching demand with variable renewable generation. This paper describes the control structure developed, implemented and tested in real demonstrators to deploy demand management programs to reduce energy bills of industrial customers. This structure operates at two stages: remotely, to calculate economically optimal consumption and ESS operation set points and locally to adapt the forecast-based economic program to real-time conditions. The described DSM structure has been developed within a national Spanish research project called EV-OPTIMANAGER.

The metal casting industry is continuously pursuing improved energy efficiency of its processes. As a contribution to this effort, this paper describes a new concept of furnaces equipped with wireless power transfer (WPT) technology. The WPT system acts as a versatile power supply for small mobile aluminium transfer furnaces in order to maintain melt temperature during transfer and degassing. 100 kW system design and test results are presented, which were obtained in the frame of the NIWE Project, funded by European Union (FP7, grant agreement No. 296024). Two 50-kW grid-tied converter are implemented as a three-level NPC bridge with NTV modulation. HF converteres (which feeds WPT coils) are a three-leg interleaved half-bridge. Test results have shown high power transfer efficiency of over 92% and confirmed the possibility to apply the experience from wireless charging of electric vehicles to high-power industry applications.

The electric vehicle (EV) is considered as one of the most promising solutions to important problems of the current mobility model: high energy consumption (low efficiency), air pollution and dependence of fossil fuels. Despite its significant benefits, a massive deployment of electric vehicles could create important issues in the electric system, mainly in distribution grids: saturation due to increase of electric demand and power quality deterioration. Moreover, owners of EV charging facilities need to increase contracted power with an extra-cost of operation. In this paper, a show case is presented, how to install a complete EV charging station in an existing petrol station, solving power quality problems and without increasing contracted power.

Microgrids and distributed generation technologies are proposed, from diverse sources, as one of the most promising solutions for the current problems of electric systems in developed countries and as a way of improving the situation of developing ones. In this line, through E+ Project, a tool to manage microgrids in urban environments is being developed, as well as the business model needed for its implementation. This document describes the E+ business model, based on two main agents: the prosumer and the APM (Aggregated Prosumers' Manager). The model will suppose a revolution of the current scenario because of the modification of the agents involved and the consideration of the optimization of the distribution grid and the reduction of the final costumer bill as the highest goals. Since the implementation process is not easy due to regulatory barriers, an implementation roadmap is proposed presenting different steps or phases.

Lao People’s Democratic Republic (Laos) possesses large hydrologic resources, converting hydropower into the most important renewable energy resource in the country. Recently the Lao government, multilateral organizations and NGOs have developed large hydropower projects in tributaries of the Mekong River. These projects usually do not benefit poor people in remote areas where the prevailing source of electricity consists of private pico-hydropower units (

A novel method is proposed for designing an energy storage system (ESS) which is dedicated to the reduction of the uncertainty of short-term wind power forecasts up to 48 h. The investigation focuses on the statistical behavior of the forecast error and the state of charge (SOC) of the ESS. This approach gives an insight into the influence of the forecast conditions (horizon, quality) on the distribution of SOC. With this knowledge, an optimized sizing of the ESS can be done with a well-defined uncertainty limit. For this study, one-year time series of power output measurements and forecasts were available for two wind farms. As a reference, different forecast quality degrees are simulated based on a persistence approach. With the forecast data, empirical probability density functions (pdfs) are generated which are the basis of the proposed method. This approach can lead to a considerable reduction of the ESS and provides important information about the unserved energy. This unserved energy represents the remaining forecast uncertainty. As a consequence, the proposed probabilistic method permits the sizing of energy storage systems as a function of the desired remaining forecast uncertainty, reducing simultaneously power and energy capacity.

A novel method is proposed for designing an energy storage system (ESS) which is dedicated to reducing the uncertainty of the short term wind power forecast. The investigation focuses on the statistical behaviour of the forecast error and the state of charge (SOC) of the ESS. This approach gives an insight into the influence of the forecast conditions on the distribution of SOC. With this knowledge, an optimised sizing of the ESS can be done with a well defined uncertainty limit. One-year power output data measurements and two types for forecast were used for this study. In addition, different forecast quality degrees are simulated based on the persistence approach. With the forecast data, empirical probability density functions (pdf's) of the SOC are generated which is the base of the proposed method. This approach can lead to a considerable reduction of the ESS and provides important information about the unserved energy. This unserved energy is the remaining forecast error or uncertainty. As a consequence, the proposed probabilistic method permits the sizing of the energy storage system as a function of the desired remaining forecast uncertainty.

Wind power forecast error usually has been assumed to have a near Gaussian distribution. With a simple statistical analysis, it can be shown that this is not valid. To obtain a more appropriate probability density function (pdf) of the wind power forecast error, an indirect algorithm based on the Beta pdf is proposed. Measured one-year time series from two different wind farms are used to generate the forecast data. Three different forecast scenarios are simulated based on the persistence approach. This makes the results comparable to other forecast methods. It is found that the forecast error pdf has a variable kurtosis ranging from 3 (like the Gaussian) to over 10, and therefore it can be categorized as fat-tailed. A new approximation function for the parameters of the Beta pdf is proposed because results from former publications could not be confirmed. Besides, a linear approximation is developed to describe the relationship between the persistence forecast and the related mean measured power. An energy storage system (ESS), which reduces the forecast error and smooths the wind power output, is considered. Results for this case show the usefulness of the proposed forecast error pdf for finding the optimum rated ESS power.

In recent years there has been a significant turn towards the research and development of electric vehicles. There are several government incentives towards decarbonizing the transport sector which is a major atmospheric pollutant via greenhouse gases that conventional ICE gasoline and petrol-fueled vehicles produce during their operation. In parallel, major vehicle manufacturers have introduced hybrid and fully electric vehicles to the market trying to reach the critical mass of buyers that will result to large penetration of electromobility in the transportation sector. EVs are expected to have a significant impact on future energy supply and distribution systems since the energy required for the propulsion of millions of vehicles will be shifted from fossil fuel to electricity provided by the grid. The increased demand and the characteristics of the load, especially for new FEV charging modes such as stationary and dynamic dictate big investments towards the enlargement of grid capacity, the increased integration of ICT towards the creation of smart grids and installation of technical solutions that will reduce the impact FEVs will have on the grid stability. This paper presents the concept of FEV dynamic charging which is the focus of FABRIC IP, makes an initial assessment on the foreseen impact this charging mode will have on the grid and proposes technical solutions for increasing grid stability.

This paper presents initial findings from a study to identify road operator and power distribution system operator operational requirements for dynamic wireless power transfer systems for electric vehicles. A number of solutions are currently being developed around the world but in order for them to be effective and desirable in the transport domain, they must be able to meet the key operational requirements. The results presented in this paper aim to identify key requirements, assess to what degree they should be met and discuss possible implications of these requirements on the design of dynamic wireless power transfer systems for EVs. Requirements such as misalignment tolerances between primary and secondary coils due to driver behaviour, power transfer section segmentation and separation, possible impacts on road network performance, power and energy supply capability, and implications of peak power demand profiles under varying traffic conditions are considered and addressed.

Wireless electric vehicle charging will pose an additional strain on existing grid infrastructure. Additionally, dynamic or “on the move” charging schemes may result in increased demand variability due to fragmented charging duration caused by charging lane layouts and traffic. A simulation environment has been set up in order to; assess the impact of dynamic wireless charging on the grid, evaluate energy storage requirements for demand smoothing and finally to explore the possibility of integrating solar energy into the dynamic wireless charging infrastructure.