Giuliano Andrea Pagani

Towards Decentralization: A Topological Investigation of the Medium and Low Voltage Grids

The traditional power grid has been designed in a hierarchical fashion, with energy pushed from the large scale production factories towards the end users. With the increasing availability of micro and medium scale generating facilities, the situation is changing. Many end users can now produce energy and share it over the power grid. Of course, end users need incentives to do so and want to act in an open decentralized energy market. In the present work, we offer a novel analysis of the medium and low voltage power grids of the North Netherlands using statistical tools from the complex network analysis field. We use a weighted model based on actual grid data and propose a set of statistical measures to evaluate the adequacy of the current infrastructure for a decentralized energy market. Further, we use the insight gained by the analysis to propose parameters that tie the statistical topological measures to economic factors that influence the attractiveness of participating in such decentralized energy market, thus identifying the important topological parameters to work on to facilitate such open decentralized markets.

Optimizing Energy Costs for Offices Connected to the Smart Grid

In addition to providing for a more reliable distribution infrastructure, the smart grid promises to give the end users better pricing and usage information. It is thus interesting for them to be ready to take advantage of features such as dynamic energy pricing and real-time choice of operators. In this work, we propose a system to monitor and control an office environment and to couple it with the smart grid. The idea is to schedule the operation of devices according to policies defined by the users, in order to minimize the cost of operation while leaving unaffected user comfort and productivity. The implementation of the system and its testing in a living lab environment show interesting economic savings of an average of about 35% and in some cases even overall energy savings in the order of 10% for a building equipped with renewable generation plants, and economic and energy savings of 20% and 10%, respectively, for a building without local renewable installations.

Smart meter aware domestic energy trading agents

The domestic energy market is changing with the increasing availability of energy micro-generating facilities. On the long run, households will have the possibility to trade energy for purchasing to and for selling from a number of different actors. We model such a futuristic scenario using software agents. In this paper we illustrate an implementation including the interfacing with a physical Smart Meter and provide initial simulation results. Given the high autonomy of the actors in the domestic market and the complex set of behaviors, the agent approach proves to be effective for both modeling and simulating purposes.

Service Orientation and the Smart Grid state and trends

The energy market is undergoing major changes, the most notable of which is the transition from a hierarchical closed system toward a more open one highly based on a “smart” information-rich infrastructure. This transition calls for new information and communication technologies infrastructures and standards to support it. In this paper, we review the current state of affairs and the actual technologies with respect to such transition. Additionally, we highlight the contact points between the needs of the future grid and the advantages brought by service-oriented architectures.

Generating Realistic Dynamic Prices and Services for the Smart Grid

The smart grid promises to change the way people manage their energy needs, to facilitate the inclusion of small-scale renewable sources, and to open the energy market to all. One of the enabling instruments is the real-time pricing of energy at the retail level: dynamic and flexible tariffs will vary through the day to reflect the actual availability of energy and the congestion conditions of the power grid, in turn, helping the power grid to stay in balance. Current pilots and research efforts consider how such dynamic tariffs can be formed and how these will affect energy distribution and usage, although currently there are no instances of them deployed. To experiment with dynamic pricing, we present a vision and related implementation of the services needed by the end user to enable the smart grid in a smart home. Our system realistically simulates the dynamic prices and services of the smart grid, using data coming from wholesale energy markets and renewable installations. In addition, we perform an economic analysis to assess the cost of energy produced by small renewable-based installations. The aim is to offer a testing tool to easily realize large simulations, testbeds, and pilot projects for future energy distribution scenarios. We have tested the proposed services and dynamic pricing solution in an existing living laboratory, showing the feasibility of the approach and effectiveness of the tool.

The smart grid's data generating potentials

The Smart Grid is the vision underlying the evolution the power grid is currently undergoing. Its pillars are increased efficiency, self-healing, operation automation, and renewable energy integration obtained through real-time control and digitalization of the infrastructure. Thus, an important ingredient-if not the main one-is information technology support for power transmission and distribution. Given the size of the power grid, its pervasiveness, and the need for its availability, it is easy to imagine that any serious ICT infrastructure dealing with it will have to manage a great deal of rapidly forming data. Now the question is whether the amount, diversity, and uses of such data put the smart grid in the category of Big Data applications, followed by the natural question of what is the value of such data. To provide an initial answer to this question, we analyze the current state of data generation of the Dutch grid, its evolution towards a smart grid, and a future realistic scenario. The scenario considered shows that the amount of data generated is comparable to some of todays social media and “classic” Big Data examples.

Towards a service-oriented energy market: current state and trend

The energy sector, which has traditionally been an oligarchic closed one, is undergoing major changes at all levels: more and more players are authorized to produce, deal and transport energy, and energy consumers are now in the position to also produce and trade energy. This new trend can be supported by Service-Oriented Architectures (SOAs) at all levels. In this short position paper, we overview the current situation of the energy sector and we indicate challenges for SOA to be addressed for a successful unbundling of the energy arena, thus providing a more efficient infrastructure with both environmental and economic benefits.

Adaptive game-based agent negotiation in deregulated energy markets

In the emerging deregulated energy paradigm enabled by the Smart Grid, energy provisioning will change drastically. Energy contracts will be negotiated between a potential multitude of parties at high frequency (e.g., several times per day) based on local needs and micro-generation production facilities. In this context, this paper presents an agent-based approach to manage negotiation among the different parties. The goal of the presented work is to propose adaptive negotiation strategies for trading energy in a deregulated market. In particular, we provide strategies derived from game theory, in order to optimize energy production and supply costs by means of negotiation and adaptation. The novelty lies in the adaptation of the class of minority and stochastic games to the energy trading problem in order to model the strategy of the various parties involved. The paper presents also simulation results of a scenario with a large number of energy buyers, a small set of prosumers (energy consumers and producers using renewable micro-generation facilities) and a few large-scale traditional electricity suppliers.

Knowing power grids and understanding complexity science

Complex networks theory has been well established as a useful framework for studying and analysing structure, dynamics and evolution of many complex systems. Infrastructural and man-made systems like power grids, gas and water networks and the internet, have been also included in this network framework, albeit sometimes ignoring the huge historical body of knowledge surrounding them. Although there seems to exist clear evidence that both complexity approach in general, and complex networks in particular, can be useful, it is necessary and profitable to put forward some of the limits that this scheme is facing when dealing with not so complex but rather complicated systems like the power grid. In this introductory paper, we offer a critical revision of the usefulness of the complexity and complex networks’ approach in this later case, highlighting both its strengths and weaknesses. At the same time we emphasise the disconnection between the so called complex and the more traditional engineering communities as one of the major drawbacks in the advent of a true body of understanding, more than simply knowing the subtleties of this kind of complex systems.

An Agent-Based Application to Enable Deregulated Energy Markets

Private houses are more and more enabled with devices that can produce renewable energy, and the not so remote chance of selling the surplus energy makes them new players in the energy market. This market is likely to become deregulated since each energy home-producer can negotiate the energy price with consumers, typically by means of an auction; on the other hand, consumers can always rely on energy companies, even if their energy is more expensive. This scenario could lead to advantages for users, but it is certainly complex and dynamic, and needs an appropriate management. To this purpose, in this paper we propose an agent-based application to deal with the negotiation among different parties producing and consuming energy. Software agents, thanks to their autonomy in taking decisions, well suit the requirements of the proposed scenario. For our application, we adopt a strategy derived from game theory, in order to optimize energy production and supply costs by means of negotiation and learning. The effectiveness of our approach is proved by simulation results of a situation involving energy buyers, energy producers using renewable micro-generation facilities and large-scale traditional electricity companies.