Renewable energy is often suggested as a possible solution for reducing greenhouse gas emissions and decreasing dependency on fossil energy sources. The most readily available renewable energy sources in Europe, wind, solar and biomass are dispersed by nature, making them ideally suited for use within Decentralized Energy Systems. Decentralized energy grids can help integrate renewable production, short lived by-products e.g. heat, minimize transport of energy carriers and fuel sources and reduce the dependency on fossils, hence, possibly improving the overall efficiency and sustainability of the energy distribution system. Within these grids balance between local renewable production and local energy demand is an important subject. Currently, fluctuations between demand and production of energy are mainly balanced by input from conventional power stations, which operate on storable fossil energy sources e.g. coal, oil, natural gas and nuclear. Within the long term scope of transition towards a low carbon intensive energy system, sustainable systems must be found which can replace fossil energy sources as load balancer in our energy supply systems.
While the Municipality of Amsterdam wants to expand the electric vehicle public charging infrastructure to reach carbon-neutral objectives, the Distribution System Operator cannot allow new charging stations where low-voltage transformers are reaching their maximum capacity. To solve this situation, a smart charging project called Flexpower is being tested in some districts. Charging power is limited during peak times to avoid grid congestion and, therefore, enable the expansion of charging infrastructure while deferring grid investments. This work simulates the implementation of the Flexpower strategy with high penetration of electric vehicles, considering dynamic and local power limits, to assess the impact on both the satisfaction of electric vehicle users and the business model of the Charging Point Operator. A stochastic approach, based on Gaussian Mixture Models, has been used to model different profiles of electric vehicle users using data from the Amsterdam public electric vehicle charging infrastructure. Several key performance indicators have been defined to assess the impact of such charging limitations on the different stakeholders. The results show that, while Amsterdam’s existing public charging infrastructure can host just twice the current electric vehicle demand, the application of Flexpower will enable the growth in charging stations without requiring grid upgrades. Even with 7 times more charging sessions, Flexpower could provide a power peak reduction of 57% while supplying 98% of the total energy required by electric vehicle users.
As the impact of our actions on the climate become more and more clear and environmental awareness is rising, the quest for increasing efficiency and lower environmental impact becomes very important. Efficiency is particularly important in the field of electricity consumption, which keeps on rising as electrification of our transportation, houses, offices and more continues worldwide. These loads and sustainable sources have one thing in common: Direct Current. To successfully respond to this growing usage of direct current (DC) systems it is important to provoke an evolution in the provision of DC infrastructure. The goal of this paper is to create a methodology to calculate and evaluate the power losses in both traditional AC grids and DC microgrids. This is done through simulation models made by Caspoc, a software for modeling and simulating physical systems in analog/power electronics, electric power generation/conversion/distribution and mechatronics. The results are compared on the quantifiable indicator: energy savings. The impact of cable losses and different converters is calculated through the simulation. This methodology and simulation strategy can be the basis for the optimal grid design in other infrastructures and cases. The model will be validated with intensive tests of household equipment in a later stage of the project, this paper focuses on the model and methodology itself. DOI: 10.1109/DUE.2014.6827760
The integration of renewable energy resources, controllable devices and energy storage into electricity distribution grids requires Decentralized Energy Management to ensure a stable distribution process. This demands the full integration of information and communication technology into the control of distribution grids. Supervisory Control and Data Acquisition (SCADA) is used to communicate measurements and commands between individual components and the control server. In the future this control is especially needed at medium voltage and probably also at the low voltage. This leads to an increased connectivity and thereby makes the system more vulnerable to cyber-attacks. According to the research agenda NCSRA III, the energy domain is becoming a prime target for cyber-attacks, e.g., abusing control protocol vulnerabilities. Detection of such attacks in SCADA networks is challenging when only relying on existing network Intrusion Detection Systems (IDSs). Although these systems were designed specifically for SCADA, they do not necessarily detect malicious control commands sent in legitimate format. However, analyzing each command in the context of the physical system has the potential to reveal certain inconsistencies. We propose to use dedicated intrusion detection mechanisms, which are fundamentally different from existing techniques used in the Internet. Up to now distribution grids are monitored and controlled centrally, whereby measurements are taken at field stations and send to the control room, which then issues commands back to actuators. In future smart grids, communication with and remote control of field stations is required. Attackers, who gain access to the corresponding communication links to substations can intercept and even exchange commands, which would not be detected by central security mechanisms. We argue that centralized SCADA systems should be enhanced by a distributed intrusion-detection approach to meet the new security challenges. Recently, as a first step a process-aware monitoring approach has been proposed as an additional layer that can be applied directly at Remote Terminal Units (RTUs). However, this allows purely local consistency checks. Instead, we propose a distributed and integrated approach for process-aware monitoring, which includes knowledge about the grid topology and measurements from neighboring RTUs to detect malicious incoming commands. The proposed approach requires a near real-time model of the relevant physical process, direct and secure communication between adjacent RTUs, and synchronized sensor measurements in trustable real-time, labeled with accurate global time-stamps. We investigate, to which extend the grid topology can be integrated into the IDS, while maintaining near real-time performance. Based on topology information and efficient solving of power flow equation we aim to detect e.g. non-consistent voltage drops or the occurrence of over/under-voltage and -current. By this, centrally requested switching commands and transformer tap change commands can be checked on consistency and safety based on the current state of the physical system. The developed concepts are not only relevant to increase the security of the distribution grids but are also crucial to deal with future developments like e.g. the safe integration of microgrids in the distribution networks or the operation of decentralized heat or biogas networks.
Residential electricity distribution grid capacityis based on the typical peak load of a house and the loadsimultaneity factor. Historically, these values have remainedpredictable, but this is expected to change due to increasingelectric heating using heat pumps and rooftop solar panelelectricity generation. It is currently unclear how this increasein electrification will impact household peak load and loadsimultaneity, and hence the required grid capacity of residentialelectricity distribution grids. To gain better insight, transformerand household load measurements were taken in an all-electricneighborhood over a period of three years. These measurementswere analyzed to determine how heat pumps and solar panelswill alter peak load and load simultaneity and hence gridcapacity design parameters. Moreover, the potential for smartgrids to reduce peak loads and load simultaneity, and hencereduce required grid capacities, was examined.
