Sebnem Rusitschka

Smart Grid Data Cloud: A Model for Utilizing Cloud Computing in the Smart Grid Domain

This paper presents a model for smart grid data management based on specific characteristics of cloud computing, such as distributed data management for real-time data gathering, parallel processing for real-time information retrieval, and ubiquitous access. The appliance of the cloud computing model meets the requirements of data and computing intensive smart grid applications. We gathered these requirements by analyzing the set of well-known smart grid use cases, most of which demand flexible collaboration across organizational boundaries of network operators and energy service providers as well as the active participation of the end user. Hence, preserving confidentiality and privacy, whilst processing the massive amounts of smart grid data, is of paramount importance in the design of the proposed Smart Grid Data Cloud.

Platforms for Smart Cities – connecting humans, infrastructure and industrial IT

Todays platforms have come a long way from being focused on technical aspects - improving the lives of engineers and developers - to powerful, socio-economic platforms affecting the daily lives of millions of users. These socio-economic platforms enable a new class of applications and services to emerge at an unprecedented speed, quality and cost. Cities and Urban Areas can increase their pace of innovation, while effort for planning and implementation can be reduced from timespans of years or even a decade to months. In this paper, we will show examples of such platforms and how they impact the communities they are implemented in.

Adaptive middleware for real-time prescriptive analytics in large scale power systems

The increased digitalization of power systems poses both opportunities and challenges for system operators. GPS time-synchronized high-resolution data streams emanating from measurement devices distributed over a wide area enable the detection of disturbances and the real-time monitoring of consequences as they are evolving, such as undamped oscillations. Processing these data streams is not possible with state-of-the-art SCADA systems that poll data asynchronously at much lower time intervals. Moreover, real-time analysis on fresh streaming data at the enterprise level is an unresolved challenge. In this paper we propose an adaptive middleware concept that can make better use of available data processing resources by enabling distributed computation both on the enterprise and on the field level. We apply the concept of linked data to provide a map for moving the computation to the data it requires for analysis. If based on the IEC 61850 standard semantic data model, the linked data concept additionally yields location and domain awareness that can be leveraged for real-time prescriptive analytics in the field. Another advantage of the proposed adaptive middleware is the abstraction of computational resources: Analytical programs can be written once and then be used to process historical data residing on servers on the enterprise level as well on the distributed devices that originated the data to enable fast analysis of events as they are unfolding.

Big Data-Driven Innovation in Industrial Sectors

This chapter provides the conceptual background and overview of big data-driven innovation in society. Specifically, it examines the nature of data-driven innovation, exemplars of big data-driven innovations in sectors spanning healthcare, public sector, finance, media, energy, and transport. It discusses core enablers for these innovations highlighting factors and challenges associated with the adequate diffusion, uptake, and sustainability of big data-driven initiatives. Finally, it presents policy recommendations to guide the development of a big data innovation ecosystem.

Big Data in the Energy and Transport Sectors

Massive amounts of sensor and textual data await the energy and transport sector stakeholders once the digital transformation of the sector reaches its tipping point. This chapter gives a definition of big data application scenarios through examples in different segments of the energy and transport sectors. A mere utilization of existing big data technologies as employed by online businesses will not be sufficient. Domain-specific big data technologies are needed for cyber-physical energy and transport systems, while the focus needs to move beyond big data to smart data technologies. Unless the need for privacy and confidentiality is satisfied, there will always be regulatory uncertainty and barriers to user acceptance of new data-driven offerings. The chapter concludes with recommendations that will help sustain the quality and competitiveness of European infrastructures as it undergoes a digital transformation.

A Catallaxy-based market mechanism for power balancing

In this paper a Catallaxy-based market mechanism is proposed for power balancing algorithms. Here, all market participants are self-organizing and coordinate themselves without any central control. This approach is highly flexible allowing for different types of negotiations on different time scales. For application in power systems we present a two-tier approach, where energy brokers assemble complex load profiles for customers. A distributed control algorithm is presented to match power demand and supply. This example shows the functionality of a free market design.

The Role of Context and Resilient Middleware in Next Generation Smart Grids

The emerging trends of volatile distributed energy resources and micro-grids are putting pressure on electrical power system infrastructure. This pressure is motivating the integration of digital technology and advanced power-industry practices to improve the management of distributed electricity generation, transmission, and distribution, thereby creating a web of systems. Unlike legacy power system infrastructure, however, this emerging next-generation smart grid should be context-aware and adaptive to enable the creation of applications needed to enhance grid robustness and efficiency. This paper describes key factors that are driving the architecture of smart grids and describes orchestration middleware needed to make the infrastructure resilient. We use an example of adaptive protection logic in smart grid substations as a use case to motivate the need for contextawareness and adaptivity.