E. Veldman

Distribution Grid Impacts of Smart Electric Vehicle Charging From Different Perspectives

As a consequence of the developments in electric transportation and the evolution toward smart grids, large-scale deployment of smart charging strategies for electric vehicles (EVs) becomes feasible. This leads to opportunities for different market parties to use the flexibility of EVs for various objectives that may be conflicting and result in a nonoptimal shifting of peak demands for the distribution grids. In this paper, we assess the financial impact of various EV charging strategies on distribution grids. We compare a strategy that minimizes network peak loads (from a network operators perspective) with a strategy to minimize charging costs (from the perspective of a commercial party). In a scenario with a high wind penetration in the system, the electricity prices are, for a significant part, determined by the instantaneous wind production. Therefore, we additionally study the effect of wind energy on electricity prices and, consequently, on the resulting EV load and network impacts. We obtain the network costs by calculating the impacts expressed in the net present value (NPV) of the investments costs and energy losses. We found that, in the case where EVs are basing their charge schedules on electricity prices, the increase in NPV compared with a no EV scenario was found to be 25% higher than in the case where the extra peak load due to EVs was minimized. The large difference in network impacts between the price based and network based charging strategies was only observed in the case with a high wind penetration. The results strongly suggest that the situation where EVs are controlled with a strategy to minimize charging costs that does not take the distribution grids into account may not lead to an optimal situation when the entire electricity delivery system is regarded.

Sensing and control challenges for Smart Grids

Due to the adverse impacts of the consumption of fossil fuels on our environment, the quest for a more sustainable energy supply is increasingly intensifying worldwide. Many renewable energy sources, such as wind, solar and tidal power generate electricity. Therefore, the development towards a sustainable energy supply leads to increasing electrification. The decreasing degree of controllability of electricity production and the high, but rare production peaks caused by distributed generation require involving electricity consumption in balancing supply and demand. To this end, electricity networks must develop into “Smart Grids”. One of the consequences of this trend is a vastly increased need to acquire, process and communicate data, both for measurement and control purposes. In the contribution, first the background of the development towards Smart Grids will be studied in more detail. Then, the various functionalities and corresponding architectures and sensing and control requirements will be discussed for different Smart Grid concepts.

Deriving electric vehicle charge profiles from driving statistics

The impacts of EV charging on electricity grids is becoming an increasingly important subject of study, but detailed knowledge about the future charging profiles of EVs appears to be missing. In this study we construct EV charge profiles based upon a large dataset of driving patterns. We consider both controlled and uncontrolled charging scenarios, where the main rationale of the controlled charging scenario is to shift the EV electricity demand away from the standard household peak. We show that applying charge control results in only slightly higher peaks compared to the situation without EVs, whereas in the uncontrolled case, the peaks will be significantly higher. Moreover, it is shown that the aggregated charge profiles give a good approximation for the demand of approximately 50 EVs or more. The EV charge profiles can be used as a tool for future network planning and EV impact studies.

Capacity management within a multi-agent market-based active distribution network

Normal operation of an active distribution network (ADN) requires simultaneous optimization of different objectives of the various involved actors. This results in a multi-objective optimization problem which has not yet been treated completely. This paper considers a particular relationship between commercial and technical coordination, involving capacity management of the distribution network. First, the market-based ADN, its actors and their objectives are described. An agent-based approach is desirable to handle the complexity of this ADN. Then, several technical issues for integrating capacity management within a multi-agent market-based ADN are pointed out. After that, the developed agent architecture and coordination mechanism are further elaborated upon, along with a formulation of the multi-objective optimization problem. Finally, a decentralized approach for integrating capacity management is introduced and demonstrated.

Smart Grids Put into Practice: Technological and Regulatory Aspects

The transition towards a more sustainable energy supply system causes changes in the supply and demand of energy and requires more flexible and efficient operation of the electricity distribution grids. It calls for smart grids with embedded intelligent control to incorporate electricity storage and controllable loads. This will ensure cost-effective development of an efficient and reliable electricity system that allows the large-scale integration of distributed generation. A holistic approach is needed to realise these smart grids. The different issues which need to be addressed to make smart grids a successful reality are covered in this article. First, the most important issues from a technological viewpoint are identified. However, adapting the grids to future developments goes far beyond simply developing and implementing technologies. Changes in technological systems affect the institutional design applied in those systems. An integral view of technical, institutional, economic and social aspects is needed to realise steps towards putting smart grids into practice. This article highlights the needed changes towards smart grids from the technological perspective of a Dutch distribution system operator (DSO) and elaborates on the implications of these changes for the regulation for this sector.

Analysis of future electricity demand and supply in the low voltage distribution grid

Electricity demand and supply is expected to change significantly in the coming decades: electric vehicles, heat pumps, solar panels and μ-CHP-units all will have an impact on what the electricity usage of households will look like in the coming decades. As a consequence, it will be difficult for electricity distribution system operators to make accurate predictions of the required investments in the low voltage distribution grid. Moreover, new technologies are becoming available to distribution system operators to influence the balance of electricity demand and supply at the low voltage distribution grid. The implementation of demand side management, smart charging of electric vehicles and smart storage of electricity can be used to influence the load on the distribution grid. In this paper the effects of the different scenarios regarding future electricity demand and the different technologies available to distribution system operators are combined in a single model to analyse their impact on the distribution grid. Further, it is investigated how this affects the investments necessary to ensure a reliable electricity grid. The main conclusion is that especially the growing number of electric vehicles has a high impact: a large market penetration of electric vehicles results in a large increase of the grid load. A larger charging power increases the impact of electric vehicles on household electricity demand further. The implementation of smart charging can positively influence the grid load and consequently leads to fewer investments, but cannot completely negate the effect.

Automatic PV production profile generation using geographic and historical weather data

This paper introduces on a probabilistic model to automatically generate photovoltaic production profiles for a given geographical region and future scenarios for deployment of renewable energy resources. The model for these profiles uses local properties of buildings, demographic statistics, and historical weather data. Sub-models are calibrated with actual PV data from a smart grid pilot site in the Netherlands and literature based scenarios. It is shown that the model performs adequately and the results are compared with historical data of photovoltaic installations.